ALBERT

Description: 〈p〉Publication date: Available online 8 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Julie A. Fortin, Jeffrey A. Cardille, Elijah Perez〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The ongoing march toward freely available, highly pre-processed satellite imagery has given both researchers and the public unprecedented access to a vast and varied data stream teeming with potential. Among many sources, the multi-decade Landsat archive is certainly the best known, but legacy and current data from other sensors is available as well through the USGS data portals: these include CBERS, ASTER, and more. Though the particular band combinations or non-global missions have made their integration into analyses more challenging, these data, in conjunction with the entire Landsat record, are available to contribute to multi-decade surveys of land-cover change.〈/p〉 〈p〉With the goal of tracing forest change through time near the Roosevelt River in the state of Mato Grosso, Brazil, we used BULC and Google Earth Engine to fuse information from 13 space-borne imagers capturing 140 images spanning 45 years. With high accuracy, the resulting time series of classifications shows the timing and location of land-use/land-cover change—both deforestation and regrowth—at sub-annual time scales. Accuracy estimates showed that the synthesized BULC classification time series was better than nearly all of the single-day image classifications, covering the entire study area at sub-annual frequency while reducing the impact of clouds and most unwanted noise as it fused information derived from a wide array of imaging platforms. The time series improved and gradually sharpened as the density of observations increased in recent decades, when there were three or more clear, higher-resolution views of a pixel annually from any sensor combination. In addition to detailing the methodology and results of multi-source data fusion with the BULC approach, this study raises timely points about integrating information from early satellite data sources and from sensors with footprints smaller than Landsat's. There are decades of research deriving sensor-specific techniques for classifying land use and land cover from a single image in a variety of settings. The BULC approach leverages the many successes of single-sensor research and can be used as a straightforward, complementary tool for blending many good-quality mapped classifications from disparate sources into a coherent, high-quality time series.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Yue Ma, Nan Xu, Jinyan Sun, Xiao Hua Wang, Fanlin Yang, Song Li〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Currently, 〈em〉in-situ〈/em〉 data and the time duration of altimeter data are limitations in calculating the water level and water volume of lakes and reservoirs from remotely sensed data. A novel method is proposed to estimate the temporal change in water levels and water volumes for lakes with only remotely sensed data. First, the surface profiles, including the ground and the underwater bottom, were extracted from the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar raw data via a new surface detecting algorithm. Second, the lake boundaries between land and water in different years were identified using a thresholding method based on the annual median Landsat composite. Third, water levels were calculated by matching the lidar surface profiles with the lake boundaries based on the nearby georeferenced coordinates. Finally, the water volumes in different years were estimated via the contours (i.e., lake boundaries) with different elevations. Lake Mead was selected as the study area, which is the largest reservoir in the United States in terms of water capacity. With only one day measuring lidar points in February 2012 and over 20 years of Landsat images (from 1987 to 2007), the water levels and water volumes in different years were estimated and compared with the 〈em〉in-situ〈/em〉 data. Our results performed well in accordance with the 〈em〉in-situ〈/em〉 measurements; the R-square of the water levels and water volumes were both over 0.99; the RMSE of the interannual variations of water levels and water volumes were 0.96 m and 0.31 km〈sup〉3〈/sup〉, respectively. The MABEL was used as a technology demonstrator for the satellite photon-counting laser altimeter and had similar data to the ICESat-2 dataset. Future ICESat-2 datasets will broaden this method to estimate water volumes for remote lakes from the 1980s, where no 〈em〉in-situ〈/em〉 data are available (such as the Tibetan Plateau and polar regions with thousands of remote and wild lakes), which could not be achieved in previous studies.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): P. Potapov, A. Tyukavina, S. Turubanova, Y. Talero, A. Hernandez-Serna, M.C. Hansen, D. Saah, K. Tenneson, A. Poortinga, A. Aekakkararungroj, F. Chishtie, P. Towashiraporn, B. Bhandari, K.S. Aung, Q.H. Nguyen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Spatially and temporally consistent vegetation structure time-series have great potential to improve the capacity for national land cover monitoring, to reduce latency and cost of international reporting, and to harmonize regional land cover characterizations. Here we present a semi-automatic, operational algorithm for mapping and monitoring of woody vegetation canopy cover and height at a regional scale using freely available Landsat time-series data. The presented algorithm employs automatic data processing and mapping using a set of lidar-based vegetation structure prediction models. Changes in vegetation cover are detected separately and integrated into the structure time-series. Sample-based validation and inter-comparison with existing datasets demonstrates the spatial and temporal consistency of our regional data time-series. The dataset reliably reflects changes in tree cover (tree cover loss user's accuracy of 0.84 and producer's accuracy of 0.75) and can serve as a tool to map annual forest extent (user's accuracy of 0.98 and producer's accuracy of 0.81 for 10% canopy cover threshold to define the forest class). The tree height estimates are consistent with a GLAS-based global map (mean average error of 3.7 m, the correlation coefficient of 0.92 and the R〈sup〉2〈/sup〉 of 0.85). The algorithm was prototyped within the Lower Mekong region where it revealed an intensive woody vegetation dynamic. Of the year 2000 forest area (defined using canopy cover threshold of 10% and tree height threshold of 5 m), 9.4% was deforested by the year 2017, and 16.6% was affected by stand-replacement disturbance followed by reforestation. The average annual area of stand-level forest disturbance within the region was 2.34 Mha, and increased by 34% from 2001 (1.85 Mha) to 2017 (2.48 Mha). Total forest area decreased by 6.2% within the region, and 11.1% of year 2000 primary forest area was lost by 2017. At the national level, Cambodia demonstrated the highest rate of deforestation, with a net forest area loss of 22.5%. We estimated that 21.3% of 2017 forest cover had an age of 17 years or less, illustrating the intensive forest land uses within the region. The time-series product is suitable for mapping annual land cover and inter-annual land cover change using customized class definitions. The regionally-consistent data are publicly available for download (〈a href="https://glad.umd.edu/" target="_blank"〉https://glad.umd.edu/〈/a〉), and online analysis (〈a href="https://rlcms-servir.adpc.net/en/forest-monitor/" target="_blank"〉https://rlcms-servir.adpc.net/en/forest-monitor/〈/a〉), and serve as an input to the SERVIR-Mekong Regional Land Cover Monitoring System.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Peifeng Ma, Weixi Wang, Bowen Zhang, Jili Wang, Guoqiang Shi, Guangqing Huang, Fulong Chen, Liming Jiang, Hui Lin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉As the world's largest city cluster, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is vulnerable to significant subsidence. The widely distributed sediments and rapid urbanization in the GBA result in the concurrence of large- and small-scale subsidence. Mono-sensor synthetic aperture radar (SAR) images usually suffer from the limitation of either low resolution or small coverage, and thus, are not applicable to completely monitoring the GBA. In this study, we used Sentinel-1 (S1), COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images jointly to reveal multi-scale subsidence of the GBA. The overall subsidence ranged from 0 to 112.3 mm/yr derived from the 2015–2017 S1 images. Three regional subsidence bowls (Zhuhai–Zhongshan, Jiangmen, and Guangzhou–Zhongshan) formed in the western alluvial plain. The high correlation between regional subsidence and Quaternary sediments confirms that sediment consolidation is the main cause of subsidence. The land use and numerical modeling results suggest that groundwater extraction and artificial loading are the triggering factors. Two representative local subsidence cases were analyzed using high-resolution images: reclamation settlement at the Hong Kong International Airport (HKIA) and sinkhole subsidence due to the excavation of the Shenzhen Wenbo skyscraper foundation pit. The validation at the HKIA showed that the measurements from the CSK and S1 data both agreed well with the leveling data. However, CSK outperformed S1 in the sense that it increased the point density by a factor of 5, improved the height precision by a factor of 4, and showed fewer false alarms. CSK is therefore more applicable to monitoring the local subsidence of key infrastructures. The cross-heading tracks of TSX and CSK images detected precursory subsidence before the sinkhole collapse from two sides, indicating that the cross-heading tracks benefit the comprehensive monitoring of local subsidence in high-rise and high-density built environments. In summary, the synergistic use of multi-sensor SAR images demonstrates the practicability of the operational surveillance of multi-scale subsidence from regional surveying to the fine monitoring of local areas in the GBA.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 2 July 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Weiwei Liu, Jon Atherton, Matti Mõttus, Jean-Philippe Gastellu-Etchegorry, Zbyněk Malenovský, Pasi Raumonen, Markku Åkerblom, Raisa Mäkipää, Albert Porcar-Castell〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Solar-induced chlorophyll fluorescence (SIF) has been shown to be a suitable remote sensing proxy of photosynthesis at multiple scales. However, the relationship between fluorescence and photosynthesis observed at the leaf level cannot be directly applied to the interpretation of retrieved SIF due to the impact of canopy structure. We carried out a SIF modelling study for a heterogeneous forest canopy considering the effect of canopy structure in the Discrete Anisotropic Radiative Transfer (DART) model. A 3D forest simulation scene consisting of realistic trees and understory, including multi-scale clumping at branch and canopy level, was constructed from terrestrial laser scanning data using the combined model TreeQSM and FaNNI for woody structure and leaf insertion, respectively. Next, using empirical data and a realistic range of leaf-level biochemical and physiological parameters, we conducted a local sensitivity analysis to demonstrate the potential of the approach for assessing the impact of structural, biochemical and physiological factors on top of canopy (TOC) SIF. The analysis gave insight into the factors that drive the intensity and spectral properties of TOC SIF in heterogeneous boreal forest canopies. DART simulated red TOC fluorescence was found to be less affected by biochemical factors such as chlorophyll and dry matter contents or the senescent factor than far-red fluorescence. In contrast, canopy structural factors such as overstory leaf area index (LAI), leaf angle distribution and fractional cover had a substantial and comparable impact across all SIF wavelengths, with the exception of understory LAI that affected predominantly far-red fluorescence. Finally, variations in the fluorescence quantum efficiency (Fqe) of photosystem II affected all TOC SIF wavelengths. Our results also revealed that not only canopy structural factors but also understory fluorescence should be considered in the interpretation of tower, airborne and satellite SIF datasets, especially when acquired in the (near-) nadir viewing direction and for forests with open canopies. We suggest that the modelling strategy introduced in this study, coupled with the increasing availability of TLS and other 3D data sources, can be applied to resolve the interplay between physiological, biochemical and structural factors affecting SIF across ecosystems and independently of canopy complexity, paving the way for future SIF-based 3D photosynthesis models.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 231〈/p〉 〈p〉Author(s): Rafael Almar, Erwin W.J. Bergsma, Philippe Maisongrande, Luis Pedro Melo de Almeida〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This article shows the capacity do derive depth using the sub metric Pleiades satellite mission (Airbus/CNES) in persistent mode, which allows acquiring a sequence of images (12 images) at a regional scale (~100 km〈sup〉2〈/sup〉). To derive depths, a spatiotemporal cross-correlation method for estimating wave velocity and inverse bathymetry is presented and applied to the 12-image sequence. A good agreement is found with in-situ bathymetry measurements obtained during the COMBI 2017 Capbreton experiment (correlation of 0.8, RMSE = 1.4 m). Depth estimate saturation is found for depths 〉35 m, mainly in a deep canyon just off the coast located in front of the entrance to Capbreton harbour. The image sequence is used to study the sensitivity of the number of images. The results show that the accuracy increases with the number of images in the sequence and with a fine resolution. Despite their noisy nature, newly available time-updated satellite bathymetries can be used to understand coastal evolution at several scales and improve risk mitigation strategies through modelling.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 231〈/p〉 〈p〉Author(s): Benjamin P. Page, Leif G. Olmanson, Deepak R. Mishra〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (R〈sub〉〈em〉rs〈/em〉〈/sub〉) products over optically variable freshwater lake surfaces. MSI-R〈sub〉〈em〉rs〈/em〉〈/sub〉 products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SD〈sub〉〈em〉MSI〈/em〉〈/sub〉) was then developed using a calibration dataset consisting of log-transformed SD〈sub〉〈em〉in situ〈/em〉〈/sub〉 measurements (lnSD〈sub〉〈em〉in situ〈/em〉〈/sub〉) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SD〈sub〉〈em〉sOLI〈/em〉〈/sub〉) where cross-sensor performance can be evaluated during coincident overpass events. SD〈sub〉〈em〉sOLI〈/em〉〈/sub〉 applied to both MSI and OLI (SD〈sub〉〈em〉OLI〈/em〉〈/sub〉) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (〈em〉n〈/em〉 = 18) and 13-Aug 2018 (〈em〉n〈/em〉 = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 231〈/p〉 〈p〉Author(s): Martyna Wietecha, Łukasz Jełowicki, Krzysztof Mitelsztedt, Stanisław Miścicki, Krzysztof Stereńczak〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The primary goal of this study was to bridge the gap between hyperspectral data classifications and their practical implementation for forest management. Information on the distributions and abundances of tree species can help forest managers understand the spatial coverage of tree species and manage the species composition by establishing a specific strategy. For this purpose, classification of tree species was applied to airborne hyperspectral imagery, and a determination of selected forest stand characteristics (i.e., main tree species, species cover proportions, and species mixture patterns) was made.〈/p〉 〈p〉We selected the Milicz Forest District of southwestern Poland as our study area. Sixteen airborne hyperspectral images were acquired with the HySpex VNIR-1800 and SWIR-384 sensors on 19 August 2015. We classified four deciduous tree species (Black alder, Pedunculate oak, Silver birch, and European beech) and coniferous species using a support vector machine (SVM) classifier. To properly evaluate the classification accuracy, the level of crown visibility from above was determined during ground measurements. The obtained information was used to create a reference set integrated with the Remote Sensing data.〈/p〉 〈p〉The classification accuracy was 91%. The main tree species found in the sample plots were correctly assessed based on the coverage area for 92% of the sample plots. The species cover proportions were estimated correctly for 75–94% of the sample plots with a tolerance threshold ≤10 percentage points depending on the species considered. Similarities of the species structures shown in the sample plots between the classification map and reference field data were found by Morisita's index (0.92). Spatial mixture pattern detection was performed for 316 forest stands. The results of the developed method were found to agree to field data with 69% accuracy. This study shows that airborne hyperspectral data serve as a reliable source for the precise description of forest characteristics, such as the main tree species, tree species cover proportions and mixture patterns.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Yong Ge, Shan Hu, Zhoupeng Ren, Yuanxin Jia, Jianghao Wang, Mengxiao Liu, Die Zhang, Weiheng Zhao, Yaowen Luo, Yangyang Fu, Hexiang Bai, Yuehong Chen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉China aims to end absolute poverty by 2020. In pursuit of this goal, a series of poverty reduction policies and measures have been proposed. As a vital element of poverty reduction, land use in China's poverty-stricken areas also undergone great changes accordingly. However, the land use change patterns in these areas are not well understood. It's necessary to analyze the spatial-temporal land use change patterns to provide data that support poverty alleviation programs. In this study, we proposed a framework for mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018. The Landsat 8 surface reflectance datasets from 2013 to 2018 (available on Google Earth Engine) were utilized to detect the changes in arable land, built-up land, water, vegetation, and un-used land. The land use transition matrix was computed to describe characteristics of the transition, and a Bayesian hierarchical model was employed to investigate the spatial-temporal land use change patterns. Our results demonstrated that the arable land continuously decreased over the study period, while built-up land and vegetation gradually expanded. The primary land use transition occurred between the arable land and vegetation. The local trends of each county indicated obvious regional differences of land use change. Moreover, significant differences existed between deep poverty-stricken counties and normal poverty-stricken counties on arable land and built-up land change, indicating that more intense human construction activities in normal poverty-stricken areas. The annual land use mapping results generated for poverty-stricken areas, along with further analysis of overall temporal change and local change trends, could provide a better understanding of land use changes and regional differences in China's poverty-stricken areas and promote the poverty reduction and sustainable development in those areas.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Qiaoyan Zhong, Jun Ma, Bin Zhao, Xinxin Wang, Jiamin Zong, Xiangming Xiao〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vegetation plays an essential role in improving urban environments and enhancing the physical and mental health of residents. However, rapid urbanization has exerted complicated influence on vegetation conditions, which remain poorly understood. To assess the impacts of urbanization on the vegetation structure and function in the urban area, we quantified the changes in impervious surface area (ISA) and assessed the impacts of urbanization on vegetation greenness (enhanced vegetation index (EVI)), and gross primary production (GPP) in megacity Shanghai during 2000–2016. The results show that 38.0% and 28.0% decreasing trends of EVI and GPP occurred in peri-urban and rural areas due to land use and land cover conversion, whereas 2.8% and 4.6% increasing trends of EVI and GPP occurred in the central city during 2000–2016 in Shanghai. In addition, the enhancement of EVI and GPP owing to the indirect impact of urbanization increased as the impervious surface coverage (ISC) gradient rose and peaked when the ISC reached ~0.8, which compensated for vegetation loss by 24.6% and 17.0%, respectively. The compensation was more stable and significant in peri-urban areas than urban and rural areas. This study provides detailed data and insights on the impacts of urbanization on vegetation, which may help stakeholders to make better management plans for urban vegetation.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Shanshan Wei, Hongliang Fang, Crystal B. Schaaf, Liming He, Jing M. Chen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The vegetation clumping index (CI) quantifies the degree to which foliage deviates from a random distribution, and therefore, is an important canopy structural parameter that governs the photosynthesis and evapotranspiration processes in terrestrial ecosystems. While the temporal variation of CI has been investigated in many field studies, there has not been a global CI product that captured its seasonal and inter-annual variations. In this study, a look-up table (LUT) method was developed based on an improved Normalized Difference between Hotspot and Darkspot (NDHD) method to estimate the daily CI from the MODIS V006 MCD43 BRDF (bidirectional reflectance distribution function) product. This new CI product is consistent with field measurements at 33 sites distributed globally (R〈sup〉2〈/sup〉 = 0.38, RMSE = 0.04, and bias = −0.01). CI shows stronger seasonal variations for deciduous broadleaf forest (DBF) and mixed forests than other vegetation types. The large seasonal differences between evergreen and deciduous vegetation, especially for DBF and deciduous shrubs, allow the CI to be used to detect seasonal structural changes and classify vegetation. The global CI shows a negative correlation to the leaf area index (LAI) for all land cover types, especially forests. The global CI demonstrates an inter-annual variation that correlated with global precipitation (R〈sup〉2〈/sup〉 = 0.34). In addition, the global CI shows a global decreasing trend from 2001 to 2017 (−0.007/decade) that was consistent with the increasing LAI (0.049/decade). The time series of CI provides a new perspective for understanding the structural characteristics of vegetation in global ecosystem studies.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Harry West, Nevil Quinn, Michael Horswell〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Drought is a common hydrometeorological phenomenon and a pervasive global hazard. As our climate changes, it is likely that drought events will become more intense and frequent. Effective drought monitoring is therefore critical, both to the research community in developing an understanding of drought, and to those responsible for drought management and mitigation. Over the past 50 years remote sensing has shifted the field away from reliance on traditional site-based measurements and enabled observations and estimates of key drought-related variables over larger spatial and temporal scales than was previously possible. This has proven especially important in data poor regions with limited in-situ monitoring stations. Available remotely sensed data products now represent almost all aspects of drought propagation and have contributed to our understanding of the phenomena. In this review we chart the rise of remote sensing for drought monitoring, examining key milestones and technologies for assessing meteorological, agricultural and hydrological drought events. We reflect on challenges the research community has faced to date, such as limitations associated with data record length and spatial, temporal and spectral resolution. This review then looks ahead to the future in terms of new technologies, such as the ESA Sentinel satellites, analytical platforms and approaches, such as Google EarthEngine, and the utility of existing data in new drought monitoring applications. We look forward to the continuation of 50 years of progress to provide effective, innovative and efficient drought monitoring solutions utilising remote sensing technology.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): A.L. Cohen-Zada, S. Maman, D.G. Blumberg〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wind streaks are common aeolian features observed on terrestrial planets. They were first identified on Mars; thus, the associated terminology is based on Mars-related observations usually describing surface patterns with distinct albedos. However, terms related to reflected tones are only relevant to past knowledge for Mars, and not necessarily suitable for description of radar-visible streaks located on Venus, Titan, or Earth. Furthermore, the sensor type used to observe wind streaks can influence the subsequent data analysis. The purpose of this study was to examine the effects of sensor type on wind streak identification and interpretation. Six case studies featuring wind streaks on Earth were performed, using imaging by both radar and optical sensors. The results indicate that wind streak identification is constrained to a specific combination of sensor settings appropriate for the local surface properties. Only half the optically visible wind streaks considered in this study were also radar-visible, but all the wind streaks observable in the radar images also appeared in the optical images. Furthermore, “bright” and “dark” (reflectance and backscatter) are relative terms and should be used with caution. These results suggest that the Venusian wind streak database is most likely far from complete and that many more streaks exist.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Oliver Cartus, Maurizio Santoro〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Approaches exploiting the complementary information on forest above-ground biomass (AGB) contained in multi-temporal and multi-frequency radar backscatter have hardly been explored in the tropics. Having available a multi-seasonal stack of air- and spaceborne X-, C-, L-, and P-band imagery for forest sites in Lope and Mondah, Gabon, we analyzed the sensitivity of backscatter at different frequencies to AGB under varying environmental imaging conditions, and the performance of an AGB retrieval when combining multi-temporal and multi-frequency backscatter observations. For the tropical forest sites in Gabon, which differ significantly with respect to forest composition and climatic conditions, we find that i) P-band allows for estimating AGB with the highest accuracy, ii) multi-temporal L-band backscatter may achieve accuracies close to what can be achieved with few P-band observations, iii) the use of multi-temporal observations is beneficial/crucial at all frequencies, in particular when the imaging conditions are persistently moist and the sensitivity of individual images to AGB reduced, and iv) combining P- and L-band backscatter allows for improving the AGB retrieval primarily when few P-band observations are complemented by a larger number of L-band observations. While the results of this study reemphasize that P-band is the most suitable frequency for mapping AGB in the tropics, they also advise consideration of the growing archives of spaceborne backscatter observations acquired at higher frequencies when mapping AGB in the tropics.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Huan Wu, John S. Kimball, Naijun Zhou, Lorenzo Alfieri, Lifeng Luo, Jinyang Du, Zhijun Huang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Improving flood modeling accuracy is crucial for real-time flood monitoring and early warning systems. Knowing the sources, patterns and driving factors of model uncertainty aids the development of more accurate flood predictions. This study investigates the consistency of two global flood inundation products, i.e., the Soil Moisture Active Passive (SMAP) satellite based fractional water (〈em〉Fw〈/em〉) cover and the Global Flood Monitoring System (GFMS) modeled flood inundation. Using Pearson's correlation coefficient (〈em〉r〈/em〉) as the indicator of the SMAP-GFMS model consistency, this research documents the spatial and temporal patterns of the correlations between the two flood products, and investigates factors affecting these relationships, including climate, land cover, hydrology and terrain distributions. Results reveal that globally, 64% locations have moderate to strong SMAP-GFMS correlation (〈em〉r〈/em〉 ≥ 0.4). Locations that are dry and have low biomass and high seasonal flood variability tend to have high correlation; for example, 47% locations with 〈em〉r〈/em〉 ≥ 0.4 occur in tropical and arid climate zones, and 43% locations with 〈em〉r〈/em〉 ≥ 0.4 are observed in 〈em〉Barren〈/em〉, 〈em〉Evergreen Broadleaf Forest〈/em〉, 〈em〉Grasslands〈/em〉, 〈em〉Open Shrubland〈/em〉 and 〈em〉Savannahs〈/em〉. Also, larger rivers have higher correlation, and in each Strahler stream order there are 60% to 65% locations having 〈em〉r〈/em〉 ≥ 0.4. Larger watersheds show higher SMAP-GFMS consistency in particular watersheds between 1000 and 40,000 km〈sup〉2〈/sup〉. Regions with greater urban infrastructure tend to have lower correlation, while locations with lower elevations and relatively flat topography have higher SMAP-GFMS consistency. This study indicates that GFMS and SMAP provide complementary information on surface water storage variations influencing precipitation driven runoff and flooding, which may enable enhanced global flood predictions.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): JongCheol Pyo, Hongtao Duan, Sangsoo Baek, Moon Sung Kim, Taegyun Jeon, Yong Sung Kwon, Hyuk Lee, Kyung Hwa Cho〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remote sensing is useful for detecting and quantifying cyanobacteria blooms for managing water systems. In particular, airborne hyperspectral remote sensing has an advantage in precise cyanobacteria detection with high spatial and spectral resolution. Many bio-optical algorithms have been developed and utilized to estimate algal concentration. However, achieving the optimal conventional optical model accuracy is still challenging in freshwater owing to the biophysical complexity of the inland water and the seasonal reflection of site-specific optical properties. Thus, this study applied convolutional neural network (CNN) with various input windows to estimate the concentrations of phycocyanin (PC) and chlorophyll-a (Chl-a), and generated a phytoplankton pigment map. We proposed that the Point-centered regression CNN (PRCNN) showed accurate PC and Chl-a simulations, with R〈sup〉2〈/sup〉 〉 0.86 and 0.73, respectively, and root mean square errors of 〈10 mg·m〈sup〉−3〈/sup〉, which were smaller than the conventional optical algorithm in our study area. In addition, the generated PC and Chl-a map from PRCNN closely followed the spatial distribution of the pigment and showed reasonable concentration levels. Through testing we found that a small input size and deep spectral bands contributed to the CNN model to achieve strong capacity to reflect the dynamic spatial feature of phytoplankton pigments. Therefore, this study demonstrated that CNN regression has the potential to detect and quantify cyanobacteria with high accuracy and can be an alternative to bio-optical algorithms.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Nico Lang, Konrad Schindler, Jan Dirk Wegner〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sentinel-2 multi-spectral images collected over periods of several months were used to estimate vegetation height for Gabon and Switzerland. A deep convolutional neural network (CNN) was trained to extract suitable spectral and textural features from reflectance images and to regress per-pixel vegetation height. In Gabon, reference heights for training and validation were derived from airborne LiDAR measurements. In Switzerland, reference heights were taken from an existing canopy height model derived via photogrammetric surface reconstruction. The resulting maps have a mean absolute error (MAE) of 1.7 m in Switzerland and 4.3 m in Gabon (a root mean square error (RMSE) of 3.4 m and 5.6 m, respectively), and correctly estimate vegetation heights up to 〉50 m. They also show good qualitative agreement with existing vegetation height maps. Our work demonstrates that, given a moderate amount of reference data (i.e., 2000 km〈sup〉2〈/sup〉 in Gabon and ≈5800 km〈sup〉2〈/sup〉 in Switzerland), high-resolution vegetation height maps with 10 m ground sampling distance (GSD) can be derived at country scale from Sentinel-2 imagery.〈/p〉〈/div〉 〈/div〉 〈div xml:lang="en"〉 〈h5〉Graphical Abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0034425719303669-ga1.jpg" width="301" alt="Unlabelled Image" title="Unlabelled Image"〉〈/figure〉〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): W.A. Obermeier, L.W. Lehnert, M.J. Pohl, S. Makowski Gianonni, B. Silva, R. Seibert, H. Laser, G. Moser, C. Müller, J. Luterbacher, J. Bendix〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Provisioning services from grassland ecosystems are strongly linked to physical and chemical grassland traits, which are affected by atmospheric CO〈sub〉2〈/sub〉 concentrations ([CO〈sub〉2〈/sub〉]s). The influences of increased [CO〈sub〉2〈/sub〉]s ([eCO〈sub〉2〈/sub〉]s) are typically investigated in Free Air Carbon dioxide Enrichment (FACE) studies via destructive sampling methods. This traditional approach is restricted to sampling plots and harvest dates, while hyperspectral approaches provide new opportunities as they are rapid, non-destructive and cost-effective. They further allow a high temporal resolution including spatially explicit information. In this study we investigated the hyperspectral predictability of 14 grassland traits linked to forage quality and quantity within a FACE experiment in central Germany with three plots under ambient atmospheric [CO〈sub〉2〈/sub〉]s, and three plots at [eCO〈sub〉2〈/sub〉]s (∼20% above ambient [CO〈sub〉2〈/sub〉]s). We analysed the suitability of various normalisation and feature selection techniques to link comprehensive laboratory analyses with two years of hyperspectral measurements (spectral range 600–1600 nm). We applied partial least squares regression and found good to excellent predictive performances (0.49 ≤ leave one out cross-validation 〈em〉R〈/em〉〈sup〉2〈/sup〉≤ 0.94), which depended on the normalisation method applied to the hyperspectral data prior to model training. Noteworthy, the models' predictive performances were not affected by the different [CO〈sub〉2〈/sub〉]s, which was anticipated due to the altered plant physiology under [eCO〈sub〉2〈/sub〉]s. Thus, an accurate monitoring of grassland traits under different [CO〈sub〉2〈/sub〉]s (present-day versus future, or within a FACE facility) is promising, if appropriate predictors are selected. Moreover, we show how hyperspectral predictions can be used e.g., within a future phenotyping approach, to monitor the grassland on a spatially explicit level and on a higher temporal resolution compared to conventional destructive sampling techniques. Based on the information during the vegetation period we show how hyperspectral monitoring might be used e.g., to adapt harvest practices or gain deeper insights into physiological plant alterations under [eCO〈sub〉2〈/sub〉]s.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Xi Li, Ruiqi Ma, Qingling Zhang, Deren Li, Shanshan Liu, Tao He, Lixian Zhao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The released VIIRS DNB nightly images, also known as VIIRS DNB daily nighttime images, provide rich information for time series analysis of global socioeconomic dynamics. Anisotropic characteristic is a possible factor that influences the VIIRS DNB radiance at night and its time series analysis. This study aims to investigate the relationship between viewing angles and VIIRS DNB radiance of Suomi NPP satellite in urban areas. First, twenty-nine points were selected globally to explore the angle variation of Suomi NPP satellite views at night. We found that the variation of the satellite viewing zenith angle (VZA) is consistent (e.g. between 0° and 70°) since the range of VZA is fixed depending on the sensor design, and the range of viewing azimuth angle (VAA) increases with the increase of latitude. Second, thirty points in cities of Beijing, Houston, Los Angeles, Moscow, Quito and Sydney, were used to investigate the angle-radiance relationship. We proposed a zenith-radiance quadratic (ZRQ) model and a zenith-azimuth-radiance binary quadratic (ZARBQ) model to quantify the relationship between satellite viewing angles and artificial light radiance, which has been corrected by removing the moonlight and atmospheric impact from VIIRS DNB radiance products. For all the thirty points, the ZRQ and ZARBQ analysis have averaged 〈em〉R〈/em〉〈sup〉2〈/sup〉 of 0.50 and 0.53, respectively, which indicates that the viewing angles are important factors influencing the variation of the artificial light radiance, but extending zenith to zenith-azimuth does not much better explain the variation of the observed artificial light. Importantly, based on the data analysis, we can make the hypothesis that building height may affect the relationship between VZA and artificial light, and cold and hot spot effects are clearly found in tall building areas. These findings are potentially useful to reconstruct more stable time series VIIRS DNB images for socioeconomic applications by removing the angular effects.〈/p〉〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Di Long, Liangliang Bai, La Yan, Caijin Zhang, Wenting Yang, Huimin Lei, Jinling Quan, Xianyong Meng, Chunxiang Shi〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., ~40 km × 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km × 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km × 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25° × 0.25°) and CLDAS land surface model (LSM) SSM output (0.0625° × 0.0625°) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of −0.001 cm〈sup〉3〈/sup〉/cm〈sup〉3〈/sup〉 (0.041 cm〈sup〉3〈/sup〉/cm〈sup〉3〈/sup〉) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km × 1 km resolution.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Kazuto Sazawa, Kensuke Kawamura, Taisuke Yasuda, Hideki Kuramitz, Naoya Wada〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The sulfur-bearing volcanic gases SO〈sub〉2〈/sub〉 and H〈sub〉2〈/sub〉S have a direct impact on ecosystems and can endanger human health. Timely information regarding dispersion patterns of fumarolic gases is crucial for predicting potential impacts. However, real-time fixed-point observations are difficult in alpine areas where snow accumulation is seasonably variable, in addition to being potentially dangerous to on-the-ground observers. Thus, in this study we tested a novel technique using a colorimeter, digital camera, and an unmanned aerial vehicle (UAV) imagery to evaluate the dispersion of sulfur particles from fumarolic gas over a snow-covered location in the Tateyama Mountain Range, central Japan, from 2013 to 2015. Snow samples were collected at the depths ranging from 0 to 0.5 cm and the sulfur particle content was determined by X-ray fluorescence analysis. The dissolved ion concentrations in snow melt solutions were analyzed to clarify the contribution of Asian yellow dust (KOSA) to the snow surface color. RGB color images captured by the UAV were converted to CIE-Lab color space with three axes: X axis changed from red to green (〈em〉a〈/em〉*), Y axis from yellow to blue (〈em〉b〈/em〉*), and Z axis from white to black (〈em〉L〈/em〉*). The color levels of green and yellow increased in the snow surface of fumarolic areas and showed a significant correlation with the densities of sulfur particles on the snow surface (〈em〉r〈/em〉〈sup〉2〈/sup〉 = 0.853, 〈em〉P〈/em〉 〈 0.001). These results indicated that aerial images of snow surface color can be used for estimation of sulfur particle dispersion from fumarolic gases.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Fangfang Yao, Jida Wang, Chao Wang, Jean-François Crétaux〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Improved monitoring of inundation area variations in lakes and reservoirs is crucial for assessing surface water resources in a growing population and a changing climate. Although long-record optical satellites, such as Landsat missions, provide sub-monthly observations at fairly fine spatial resolution, cloud contamination often poses a major challenge for producing temporally continuous time series. We here proposed a novel method to improve the temporal frequency of usable Landsat observations for mapping lakes and reservoirs, by effectively recovering inundation areas from contaminated images. This method automated three primary steps on the cloud-based platform Google Earth Engine. It first leveraged multiple spectral indices to optimize water mapping from archival Landsat images acquired since 1992. Errors induced by minor contaminations were next corrected by the topology of isobaths extracted from nearly cloud-free images. The isobaths were then used to recover water areas under major contaminations through an efficient vector-based interpolation. We validated this method on 428 lakes/reservoirs worldwide that range from ~2 km〈sup〉2〈/sup〉 to ~82,000 km〈sup〉2〈/sup〉 with time-variable levels measured by satellite altimeters. The recovered water areas show a relative root-mean-squared error of 2.2%, and the errors for over 95% of the lakes/reservoirs below 6.0%. The produced area time series, combining those from cloud-free images and recovered from contaminated images, exhibit strong correlations with altimetry levels (Spearman's rho mostly ~0.8 or larger) and extended the hypsometric (area-level) ranges revealed by cloud-free images alone. The combined time series also improved the monthly coverage by an average of 43%, resulting in a bi-monthly water area record during the satellite altimetry era thus far (1992–2018). The robustness of this method was further verified under five challenging mapping scenarios, including fluvial lakes in humid basins, reservoirs with complex shape geometries, saline lakes with high mineral concentrations, lakes/reservoirs in mountainous regions, and pan-Arctic lakes with frequent snow/ice covers. Given such performance and a generic nature of this method, we foresee its potential applications to assisting water area recovery for other optical and SAR sensors (e.g., Sentinel-2 and SWOT), and to estimating lake/reservoir storage variations in conjunction with altimetry sensors.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Kun Shi, Yunlin Zhang, Kaishan Song, Mingliang Liu, Yongqiang Zhou, Yibo Zhang, Yuan Li, Guangwei Zhu, Boqiang Qin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Thus, information on the spatial and temporal distribution of TSI is critical for supporting scientifically sound water resource management decisions. We proposed a semi-analytical approach to remotely estimate TSI based on Landsat 8 OLI data for inland waters. The approach has two major steps: deriving the total absorption coefficient of optically active constituents (OACs) and building the relationship between the total absorption coefficient and TSI. First, version 6.0 of the Quasi-Analytical Algorithm (QAA_V6, developed by Zhongping Lee) was implemented with Landsat 8 OLI data to derive the total absorption coefficients of the OACs. Second, we modeled TSI using the total absorption coefficients of OACs at 440 nm based on a large in situ measurement dataset. The total absorption coefficient of OACs at 440 nm gave satisfactory validation results for modeling TSI with a mean absolute percent error of 6% and a root-mean-square error of 5.77. Then, we performed this approach in three inland waters with various eutrophic statuses to validate its results, and the approach demonstrated a robust and satisfactory performance. Finally, an application of the approach was demonstrated in Lake Qiandaohu. Our semi-analytical approach has a sound optical mechanism and extensive application for different trophic inland waters.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Lev D. Labzovskii, Su-Jong Jeong, Nicholas C. Parazoo〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Cities are responsible for 70% of fossil fuel CO〈sub〉2〈/sub〉 emissions (FFCO〈sub〉2〈/sub〉), and these emissions are poorly monitored. As a result, the use of spaceborne observation as a tool for addressing urban FFCO〈sub〉2〈/sub〉 emissions has intensified. This work aims to understand the suitability of modern spaceborne remote sensing for capturing CO〈sub〉2〈/sub〉 urban enhancement and for monitoring FFCO〈sub〉2〈/sub〉 emissions from cities. We have used the first four years of observations from NASA Orbiting Carbon Observatory 2 (OCO-2) to collect CO〈sub〉2〈/sub〉 urban anomalies (XCO〈sub〉2ano〈/sub〉) from large cities (〉500 km〈sup〉2〈/sup〉 area) by combining urban-to-rural gradient and statistical filtering approaches. Approximately half of all XCO〈sub〉2ano〈/sub〉 (44%) were significant and noise-free to meet the accuracy requirements for city-scale applications. We captured positive CO〈sub〉2〈/sub〉 enhancement over urban areas compared to background at a global scale. Median XCO〈sub〉2ano〈/sub〉 estimates were positive at 1.07 ± 0.80 ppm (worldwide), 1.05 ± 0.80 ppm (Northern Hemisphere), and 0.96 ± 0.80 ppm (Southern Hemisphere). Most monthly XCO〈sub〉2ano〈/sub〉 (~83%) emerged in four regions of the Northern Hemisphere (East Asia, Europe, North America, and South Asia), where most cities with strong emissions are located. We report that the XCO〈sub〉2ano〈/sub〉 from numerous cities have a moderate linear relationship with city size (〈em〉r〈/em〉 = 0.54–0.65) and FFCO〈sub〉2〈/sub〉 strength (〈em〉r〈/em〉 = 0.64). As expected, five out of the six strongest XCO〈sub〉2ano〈/sub〉 were found over the megacities (〉10 million population) of Los Angeles (2.04 ± 0.91 ppm), Tehran (1.94 ± 1.54 ppm), Rhine-Main Metropolitan Area (1.51 ± 0.59 ppm), Pearl River Delta (1.48 ± 1.11 ppm), and Seoul (1.47 ± 1.72 ppm), with Houston (1.50 ± 0.72 ppm) as the only non-megacity. Overall, we have shown that spaceborne remote sensing of XCO〈sub〉2〈/sub〉 is suitable for capturing CO〈sub〉2〈/sub〉 enhancement over a wide range of cities (especially in the Northern Hemisphere). Though we registered the global-scale urban footprint in CO〈sub〉2〈/sub〉 signal and demonstrated the sensitivity of XCO〈sub〉2ano〈/sub〉 to city size and FFCO〈sub〉2〈/sub〉, spaceborne remote sensing is still limited in its provision of columnar CO〈sub〉2〈/sub〉 enhancement which is fundamentally linked with underlying urban emissions. This linkage may be more effectively addressed when spaceborne remote sensing becomes increasingly optimized for city-scale applications.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Changjiang Xiao, Nengcheng Chen, Chuli Hu, Ke Wang, Jianya Gong, Zeqiang Chen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sea surface temperature (SST) is one of the most important parameters in the global ocean-atmospheric system, changes of which can have profound effects on the global climate and may lead to extreme weather events such as droughts and floods. Therefore, predicting the dynamics of future SSTs is of vital importance which can help identify these extreme events and alleviate the losses they cause. In this paper, a machine learning method combining the long short-term memory (LSTM) deep recurrent neural network model and the AdaBoost ensemble learning model (LSTM-AdaBoost) is proposed to predict the short and mid-term daily SST considering that LSTM is good at modelling long-term dependencies but suffers from overfitting, while AdaBoost has strong prediction capability and is not easily overfitted. By combining these two strong and heterogeneous models, the prediction errors related to variance may cancel out each other and the final results can be improved. In this method, the historical time-series satellite data of SST anomaly (SSTA) is used instead of SST itself considering that the fluctuations of SSTs are very small compared to their absolute magnitudes. The seasonality of the SSTA time series is first modelled using polynomial regression and then removed. Then, the deseasonalized time series are used to train the developed LSTM model and AdaBoost model independently. Daily SSTA predictions are made using these two models, and eventually, their predictions are combined as final predictions using the averaging strategy. A case study in the East China Sea that predicts the daily SSTA 10 days ahead shows that the proposed LSTM-AdaBoost combination model outperforms the LSTM and AdaBoost separately, as well as the optimized support vector regression (SVR) model, the optimized feedforward backpropagation neural network model (BPNN), and the stacking LSTM-AdaBoost model (S_LSTM-AdaBoost), when judged using multiple error statistics and from different perspectives. The results suggest that the LSTM-AdaBoost combination model using the averaging strategy is highly promising for short and mid-term daily SST predictions.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Miguel A. Belenguer-Plomer, Mihai A. Tanase, Angel Fernandez-Carrillo, Emilio Chuvieco〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59 ± 0.06 (± confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43 ± 0.08 and 0.37 ± 0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.〈/p〉〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, Min Chen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The direct and diffuse components of downward shortwave radiation (SW), and photosynthetically active radiation (PAR) at the Earth surface play an essential role in biochemical (e.g. photosynthesis) and physical (e.g. energy balance) processes that control weather and climate conditions, and many ecological processes. Space-based observations have the unique advantage of providing reliable estimates of SW and PAR globally with sufficient accuracy for constructing Earth's radiation budget and estimating land-surface fluxes that control these processes. However, most existing space-based SW and PAR estimations from sensors onboard polar-orbiting and geostationary satellites have inherently low temporal resolution and/or limited spatial coverage of the entire Earth surface. The unique location/orbit of Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides an unprecedented opportunity to obtain global estimates of SW and PAR accurately at a high temporal resolution of about 1–2 h. In this study, we developed and used a model (random forest, RF) to estimate global hourly SW and PAR at 0.1° × 0.1° (about 10 km at equator) spatial resolution based on EPIC measurements. We used a combination of EPIC Level-2 products, including solar zenith angle, aerosol optical depth, cloud optical thickness, cloud fraction, total column ozone and surface pressure with their associated quality flags to drive the RF model for estimating SW and PAR. We evaluated the model results against in situ observations from the Baseline Surface Radiation Network (BSRN) and Surface Radiation Budget Network (SURFRAD). We found the EPIC SW and PAR estimates at both hourly and daily time scales to be highly correlated and consistent with these independently obtained in situ measurements. The RMSEs for estimated daily diffuse SW, direct SW, total SW, and total PAR were 19.10, 38.47, 33.52, and 14.09 W/m〈sup〉2〈/sup〉, respectively, and the biases for these estimates were 1.71, −0.77, 1.04 and 4.11 W/m〈sup〉2〈/sup〉, respectively. We further compared the estimated SW and PAR with the Clouds and the Earth's Radiant Energy System Synoptic 1° × 1° (CERES SYN1deg) products and found a good correlation and consistency in their accuracy, spatial patterns and latitudinal gradient. The EPIC SW and PAR estimates provide a unique dataset (i.e. observations from single instrument from pole-to-pole for the entire sunlit portion of Earth) for characterizing their diurnal cycles and their potential impact on photosynthesis and evapotranspiration processes.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): Gabriel Antunes Daldegan, Dar A. Roberts, Fernanda de Figueiredo Ribeiro〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Fire is used worldwide to clear natural vegetation areas for economic activities and to manage the regeneration of already opened sites. In Brazil, fire has been traditionally used to convert natural vegetation areas to agricultural lands (slash and burn) and to manage pastures for livestock. We developed the Burned Area Spectral Mixture Analysis (BASMA) algorithm in Google Earth Engine, which is designed to process Landsat data to produce a multi-temporal fire scar database representing annual burned area for an extent of 362,000 km〈sup〉2〈/sup〉 in the transition zone between the Amazon forest and the Cerrado biome. This region is considered a conservation hotspot, given its high deforestation rates over the last four decades. We digitally processed a 32-year time-series (1985 to 2017) of Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper+, and Landsat 8 Operational Land Imager data to map fire scars based on sub-pixel char fraction, aiming to generate a consistent burned area product at a finer scale and covering a longer period than those currently available for the region. Manually interpreted reference burned area polygons for each annual mosaic was used to guide the definition of the best fire scar endmember and its fraction threshold. To assess our BASMA-delineated fire scar, they were compared to independent datasets of manually delineated burned area produced by visual analysis of finer spatial resolution imagery, returning an average Dice Coefficient value of 0.86. Accuracy was also measured against the 30-meter Burned Area product available at the ‘Queimadas’ data portal. A total of 11,106,258 ha was mapped as having been affected by fire during the annual dry season over the 32 years, which represents 30.7% of the study region. Results showed a decreasing trend in the annual amount of burned area over the time-series. It reflects a similar pattern shown in the deforestation rate for the Legal Amazon, measured by the Brazilian National Institute for Space Research - INPE. Moreover, the Cerrado biome subset of the study region consistently showed higher burned area when compared to the Amazon forest subset. Our findings provide robust evidence that our approach is a consistent method to identify and delineate fire scars for large areas over a long time-series in a very efficient fashion, given the digital processing power of Google Earth Engine, which reduces the time necessary to analyze such big amount of data.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: October 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 232〈/p〉 〈p〉Author(s): H. Taubenböck, M. Weigand, T. Esch, J. Staab, M. Wurm, J. Mast, S. Dech〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With 37 million inhabitants, Tokyo is the world's largest city in UN statistics. With this work we call this ranking into question. Usually, global city rankings are based on nationally collected population figures, which rely on administrative units. Sprawling urban growth, however, leads to morphological city extents that may surpass conventional administrative units. In order to detect spatial discrepancies between the physical and the administrative city, we present a methodology for delimiting Morphological Urban Areas (MUAs). We understand MUAs as a territorially contiguous settlement area that can be distinguished from low-density peripheral and rural hinterlands. We design a settlement index composed of three indicators (settlement area, settlement area proportion and density within the settlements) describing a gradient of built-up density from the urban center to the periphery applying a sectoral monocentric city model. We assume that the urban-rural transition can be defined along this gradient. With it, we re-territorialize the conventional administrative units. Our data basis are recent mapping products derived from multi-sensoral Earth observation (EO) data – namely the Global Urban Footprint (GUF) and the GUF Density (GUF-DenS) – providing globally consistent knowledge about settlement locations and densities. For the re-territorialized MUAs we calculate population numbers using WorldPop data. Overall, we cover the 1692 cities with 〉300,000 inhabitants on our planet. In our results we compare the consistently re-territorialized MUAs and the administrative units as well as their related population figures. We find the MUA in the Pearl River Delta the largest morphologically contiguous urban agglomeration in the world with a calculated population of 42.6 million. Tokyo, in this new list ranked number 2, loses its top position. In rank-size distributions we present the resulting deviations from previous city rankings. Although many MUAs outperform administrative units by area, we find that, contrary to what we assumed, in most cases MUAs are considerably smaller than administrative units. Only in Europe we find MUAs largely outweighing administrative units in extent.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 220〈/p〉 〈p〉Author(s): Xiyu Chen, Lin Liu, Annett Bartsch〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Microwave remote sensing, both active or passive, can provide useful information about the freeze/thaw (F/T) state of soil near the surface. Here we apply an edge detection algorithm on time series of indicators derived from measurements of SMAP L-band radiometer and ASCAT C-band scatterometer to detect the freeze/thaw onsets of surface soil. Comparing these results against the onsets derived from in situ measurements in Alaska, we demonstrate that this algorithm is an effective approach to detect onsets of the soil F/T transition. More specifically, our results show that the thawing onsets estimated from the SMAP data occurred 5 to 13 days earlier than the onsets estimated from the in situ measurements, which is likely due to the influence of snowmelt on the radiometer signal. The thawing onsets estimated from the ASCAT data were about 6 days later than the in situ onsets. Our estimated freezing onsets from each microwave remote sensing dataset were close to the in situ onsets (1–5 days). We also compare our estimated onsets with those from the SMAP Level 3 F/T product and the mean biases for thawing and freezing onsets are 1 ± 2 and 1 ± 3 days, respectively. Furthermore, we illustrate the complementary nature of the SMAP and ASCAT measurements and the potential for combining these two to differentiate snowmelt from soil thawing events.〈/p〉〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 220〈/p〉 〈p〉Author(s): Mathilde Cancet, David Griffin, Madeleine Cahill, Bertrand Chapron, Johnny Johannessen, Craig Donlon〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Australia's marginal seas include a wide range of ocean current regimes ranging from tide-dominated in the North-West where the continental shelf is wide, to boundary current- and eddy-dominated in the South-East, where the shelf is narrow. Here, we take the opportunity to test the GlobCurrent surface ocean current products against in-situ observations in these two contrasting regimes. Observations by Acoustic Doppler Current Profiler (ADCP) instruments of Australia's Integrated Marine Observing System (IMOS) and drifting buoys of the Global Drifting Programme (GDP) are used.〈/p〉 〈p〉The monthly-timescale variability of the GlobCurrent alongshore current component is in moderately good agreement with the observations on the continental shelf in the South-East but neither the shorter period variability nor the long-term mean are representative of the ADCP observations. While the observed tidal currents are negligibly small, the wind-driven signals are not. But these are evidently too transient to be adequately sampled by altimetry. The inclusion of an Ekman component does not represent these signals because the interaction of the Ekman transport with the coastal boundary condition is not included. Similarly, the error of the time-mean velocity, which is the dominant error, is because that product is not designed to represent the highly anisotropic nature of the sea level gradients over the continental shelf nor the constraints on the flow field that are imposed by the topography. We thus conclude that the GlobCurrent product needs improvements before it can be described as very suitable for applications on this, and probably other, narrow continental shelf. Off the continental shelf, in contrast, the GlobCurrent products compare quite well with the trajectories of drifting buoys, confirming that the products are quite suitable for blue-water applications.〈/p〉 〈p〉In contrast to the South-East, the tides are very strong in the North-West region of Australia. The sub-tidal variability is weak, in both relative and absolute senses. Consequently, the removal of the tidal signal from the sea level observations needs to be very complete for the residual error to be smaller than the true sub-tidal signal. Transient wind forced signals are also occasionally large so this step of the de-aliasing also needs to be very accurate. Unfortunately, it appears that more work is required before accurate estimates of sub-tidal variability are available from GlobCurrent: the magnitude of the GlobCurrent estimates of sub-tidal current variability far exceed the magnitude of, and are uncorrelated with, the detided ADCP data.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 220〈/p〉 〈p〉Author(s): F.J.A. van Ruitenbeek, H.M.A. van der Werff, W.H. Bakker, F.D. van der Meer, K.A.A. Hein〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks.〈/p〉 〈p〉Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension.〈/p〉 〈p〉The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples.〈/p〉 〈p〉The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core.〈/p〉 〈/div〉 〈/div〉 〈div xml:lang="en"〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S003442571830484X-ga1.jpg" width="314" alt="Unlabelled Image" title="Unlabelled Image"〉〈/figure〉〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 219〈/p〉 〈p〉Author(s): 〈/p〉

Description: 〈p〉Publication date: January 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 220〈/p〉 〈p〉Author(s): Jianzhi Dong, Wade T. Crow〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Due to their shallow penetration of the soil column, remote-sensing (RS) soil moisture retrievals are often considered ill-suited for measuring the strength of soil moisture-air temperature coupling. Consequently, soil moisture proxies derived from antecedent rainfall considerations are commonly applied in their place. However, the suitably of satellite RS products has not yet been examined for newer soil moisture products derived from L-band microwave radiometry. This study globally compares correlations between monthly soil moisture and the monthly number of summertime hot days (NHD) for the case of three separate RS-based soil moisture products and a fourth soil moisture proxy derived from the standard precipitation index (SPI). Compared with SPI-based estimates, C- and X-band RS soil moisture products demonstrate a significantly (at 〈em〉p〈/em〉 = 0.05 [-] confidence) weaker correlation with NHD. However, 2010–2018 L-band Soil Moisture and Ocean Salinity (SMOS) based soil moisture-NHD correlation is generally comparable to the SPI case. Furthermore, utilizing higher-precision 2015–2018 soil moisture products from the L-band Soil Moisture Active and Passive (SMAP) mission further strengthens soil moisture-NHD correlation and leads to stronger sampled correlations than SPI over global hot-spot regions (significant at 〈em〉p〈/em〉 = 0.05 [-] confidence). Combined with the general equivalence of monthly surface and root zone soil moisture anomalies, these results suggest that the signal-to-noise ratio (SNR, i.e. the relative size of soil moisture signal and random observation error variances) of RS-based surface soil moisture product, instead of their vertical measurement depth, is the key limiting factor determining their ability to quantify land-atmosphere coupling strengths. Based on this, we argue that L-band soil moisture products have reached a sufficient level of SNR to be of value for the study of land-atmosphere coupling.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 219〈/p〉 〈p〉Author(s): J. Antonio Guzmán Q., Benoit Rivard, G. Arturo Sánchez-Azofeifa〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Increases in liana abundance in tropical forests are pervasive threats to the current and future forest carbon stocks. Never before has the need been more evident for new approaches to detect the presence of liana in ecosystems, given their significance as fingerprints of global environmental change. In this study, we explore the use of longwave infrared reflectance (LWIR, 8–11 μm) as a wavelength region for the classification of liana and tree leaves and compare classification results with those obtained using visible-near infrared reflectance data (VIS-NIR, 0.45–0.95 μm). Twenty sun leaves were collected from each of 14 liana species and 21 tree species located at the canopy or forest edge (〈em〉n〈/em〉 = 700) in Santa Rosa National Park, Costa Rica. LWIR and VIS-NIR reflectance measurements were performed on these leaves using a portable calibrated Fourier Transform Infrared Spectroscopy (FTIR) Agilent ExoScan 4100 and a UniSpec spectral analysis system, respectively. The VIS-NIR and LWIR data were first resampled. Then these two spectral libraries were pre-processed for noise reduction and spectral feature enhancement resulting in three datasets for each spectral region as follows: filtered only, filtered followed by extraction of the first derivative, and continuous wavelet transformation (CWT). Data reduction was then applied to these data sets using principal components analysis (PCA). The outputs obtained from the PCA were used to conduct the supervised classification of liana and tree leaves. In total, 21 classifiers were applied to datasets of training and testing to extract the classification accuracy and agreement for liana and tree leaves. The results suggest that the classification of leaves based on LWIR data can reach accuracy values between 66 and 96% and agreement values between 32 and 92%, regardless of the type of classifier. In contrast, the classification based on VIS-NIR data shows accuracy values between 50 and 70% and agreement values between 0.01 and 40%. The highest classification rates of liana and tree leaves were obtained from datasets pre-processed using the CWT or from the extraction of the first derivative and classified using either random forest, 〈em〉k〈/em〉-nearest neighbor, or support vector machine with radial kernel. The results using the LWIR reflectance highlight the potential of this spectral region for the accurate detection of liana extent in tropical ecosystems. Future studies should consider this potential and test the regional monitoring of lianas.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 219〈/p〉 〈p〉Author(s): Adam J. Purdy, Joshua B. Fisher, Michael L. Goulden, Andreas Colliander, Gregory Halverson, Kevin Tu, James S. Famiglietti〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurate estimation of global evapotranspiration (ET) is essential to understand water cycle and land-atmosphere feedbacks in the Earth system. Satellite-driven ET models provide global estimates, but many of the ET algorithms have been designed independently of soil moisture observations. As water for ET is sourced from the soil, incorporating soil moisture into global remote sensing algorithms of ET should, in theory, improve performance, especially in water-limited regions. This paper presents an update to the widely-used Priestley Taylor-Jet Propulsion Laboratory (PT-JPL) ET algorithm to incorporate spatially explicit daily surface soil moisture control on soil evaporation and canopy transpiration. The updated algorithm is evaluated using 14 AmeriFlux eddy covariance towers co-located with COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture observations. The new PT-JPL〈sub〉SM〈/sub〉 model shows reduced errors and increased explanation of variance, with the greatest improvements in water-limited regions. Soil moisture incorporation into soil evaporation improves ET estimates by reducing bias and RMSE by 29.9% and 22.7% respectively, while soil moisture incorporation into transpiration improves ET estimates by reducing bias by 30.2%, RMSE by 16.9%. We apply the algorithm globally using soil moisture observations from the Soil Moisture Active Passive Mission (SMAP). These new global estimates of ET show reduced error at finer spatial resolutions and provide a rich dataset to evaluate land surface and climate models, vegetation response to changes in water availability and environmental conditions, and anthropogenic perturbations to the water cycle.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 19 September 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Sylvain Jay, Frédéric Baret, Dan Dutartre, Ghislain Malatesta, Stéphanie Héno, Alexis Comar, Marie Weiss, Fabienne Maupas〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (〈em〉Beta vulgaris〈/em〉 L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (C〈sub〉ab〈/sub〉), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents.〈/p〉 〈p〉Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GF〈sub〉GREENPIX〈/sub〉 and VI〈sub〉CAB〈/sub〉) extracted from the images are shown to play a major role in these performances. GF〈sub〉GREENPIX〈/sub〉 is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (〈em〉VARI〈/em〉) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VI〈sub〉CAB〈/sub〉 is the 〈em〉mND〈/em〉〈sub〉〈em〉blue〈/em〉〈/sub〉 index value averaged over the darkest green pixels, and provides critical information on C〈sub〉ab〈/sub〉. When exploited within uni- or multivariate empirical models, these two variables improve the GF, GAI, C〈sub〉ab〈/sub〉, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.90) are obtained by multiplying C〈sub〉ab〈/sub〉 and GAI estimates respectively derived from VI〈sub〉CAB〈/sub〉 and a log-transformed version of GF〈sub〉GREENPIX〈/sub〉, log(1-GF〈sub〉GREENPIX〈/sub〉).〈/p〉 〈p〉The GF〈sub〉GREENPIX〈/sub〉 and VI〈sub〉CAB〈/sub〉 variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 219〈/p〉 〈p〉Author(s): Huan Li, Hongjie Xie, Stefan Kern, Wei Wan, Burcu Ozsoy, Stephan Ackley, Yang Hong〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We use total (sea ice plus snow) freeboard as estimated from Ice, Cloud and land Elevation Satellite (ICESat) Geophysical Laser Altimeter System (GLAS) observations to compute Antarctic sea-ice thickness and volume. In order to overcome assumptions made about the relationship between snow depth and total freeboard or biases in snow depth products from satellite microwave radiometry, we implement a new algorithm. We treat the sea ice-snow system as one layer with reduced density, which we approximate by means of a priori information about the snow depth to sea-ice thickness ratio. We derive this a priori information directly from ICESat total freeboard data using empirical equations relating in-situ measurements of total freeboard to snow depth or sea-ice thickness. We apply our new algorithm (one-layer method or OLM), which uses the buoyancy equation approach without the need for auxiliary snow depth data, to compute sea-ice thickness for every ICESat GLAS footprint from a valid total freeboard. An improved method for sea-ice volume retrieval is also used to derive ice volume at 6.25 km scale. Spatio-temporal variations of sea-ice thickness and volume are then analyzed in the circumpolar Antarctic as well as its six sea sectors: Pacific Ocean, Indian Ocean, Weddell East, Weddell West, Bell-Amund Sea, and Ross Sea, under both interannual and seasonal scales. Because the OLM algorithm relies on only one parameter, the total freeboard, and is independent of auxiliary snow depth information, it is believed to become a viable alternative sea-ice thickness retrieval method for satellite altimetry.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 19 June 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Xinjie Liu, Luis Guanter, Liangyun Liu, Alexander Damm, Zbyněk Malenovský, Uwe Rascher, Dailiang Peng, Shanshan Du, Jean-Philippe Gastellu-Etchegorry〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 219〈/p〉 〈p〉Author(s): Nan Chen, Wei Li, Charles Gatebe, Tomonori Tanikawa, Masahiro Hori, Rigen Shimada, Teruo Aoki, Knut Stamnes〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Cloud detection and screening constitute critically important first steps required to derive many satellite data products. Traditional threshold-based cloud mask algorithms require a complicated design process and fine tuning for each sensor, and they have difficulties over areas partially covered with snow/ice. Exploiting advances in machine learning techniques and radiative transfer modeling of coupled environmental systems, we have developed a new, threshold-free cloud mask algorithm based on a neural network classifier driven by extensive radiative transfer simulations. Statistical validation results obtained by using collocated CALIOP and MODIS data show that its performance is consistent over different ecosystems and significantly better than the MODIS Cloud Mask (MOD35 C6) during the winter seasons over snow-covered areas in the mid-latitudes. Simulations using a reduced number of satellite channels also show satisfactory results, indicating its flexibility to be configured for different sensors. Compared to threshold-based methods and previous machine-learning approaches, this new cloud mask (i) does not rely on thresholds, (ii) needs fewer satellite channels, (iii) has superior performance during winter seasons in mid-latitude areas, and (iv) can easily be applied to different sensors.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 218〈/p〉 〈p〉Author(s): Xiaoping Wang, Fei Zhang, Hsiang-te Kung, Verner Carl Johnson〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content (SOMC) in the bare topsoil of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. The results indicated that the correlation of coefficients (R) between SOMCs and hyperspectral data processed by fractional derivative were significant at the 0.01 level; the number of wave bands increased initially and then decreased when the order increased. The correlation of coefficient peak appeared at the 1.2 order with a value of 0.52. The correlation of coefficients (R) between SOMCs and the optimal remote sensing indexes (the ratio index, RI; difference index, DI; and the normalized difference index, NDI) of peaked at the 1.2 order, with correlation of coefficients (R) values of 0.81, 0.86 and 0.82, respectively. Six SOMC estimation models were created by means of a single band and optimal remote sensing indexes using Gray Relational Analysis-BP Neural Network (GRA-BPNN). This study found that the optimal model was a 1.2 order derivative model, where the lowest root mean square error (RMSE) was 3.26 g/kg, the highest was 0.92, and the residual prediction deviation (RPD) was 2.26. To complete the high accuracy retrieval of SOMCs, based on Landsat OLI operational land images data, more ‘hidden’ information from the Landsat OLI images were obtained by employing the subsection of spectral band method and the fractional derivative algorithm. Accuracy of the SOMC map was attained by the optimal model of the ground hyperspectral data and the Landsat OLI data, which had low RMSE values of 4.21 g/kg and 4.16 g/kg, respectively. Therefore, we conclude that the SOMC can be estimated and retrieved using a fractional derivative algorithm, the subsection of spectral band method, and the optimal remote sensing index.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Megan M. Miller, Manoochehr Shirzaei〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hurricane Harvey caused unprecedented flooding and socioeconomic devastation in Eastern Texas with high winds, elevated storm tide, and record rainfall. The flooded area is mapped using the radar backscattering difference between Sentinel-1A/B satellite acquisitions spanning the event, which provides a snapshot of standing water at the time of image acquisition. We find vast areas outside of designated flood hazard zones are overwhelmed. Furthermore, we map pre-cyclone land subsidence using multitemporal interferometric processing of large SAR datasets acquired by Advanced Land Observation Satellite (ALOS) and Sentinel-1A/B satellites. We find that subsidence of up to 49 mm/yr and 34 mm/yr during the ALOS (Jul-2007–Jan-2011) and Sentinel-1A/B (Dec-2015 to Aug-2017) acquisition periods affect various parts of Houston-Galveston area. We conclude that 85% of the flooded area subsided at a rate 〉 5 mm/yr. We suggest that subsidence affected flood severity by modifying base flood elevations and topographic gradients, supported by the Chi-square test of independence. This work highlights the importance of incorporating InSAR measurements of land subsidence in flood resilience strategies.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Fabio Michele Rana, Maria Adamo, Richard Lucas, Palma Blonda〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The present paper applies Synthetic Aperture Radar (SAR) based on Local Gradient-Modified (LG-Mod) algorithm to retrieve wind directions from Sentinel-1 data in the Camargue and the Wadden Sea protected coastal areas. Wind speeds are estimated through the inversion of the C-band MODel 5.N (CMOD5.N) backscattering model. Both Interferometric Wide Swath (IW) and Extra Wide Swath (EW) Level 1 products were evaluated for wind fields retrieval at high (5 km) and medium (12.5 km) output spatial resolutions. SSW fields from Sentinel-1 were compared with Numerical Weather Prediction (NWP) models and in situ data. Exploitation of the LG-Mod provided wind direction with a related marginal error parameter (i.e., 〈em〉ME〈/em〉〈sub〉〈em〉α〈/em〉〈/sub〉〈sup〉〈em〉ROI〈/em〉〈/sup〉) which proved useful for selecting the optimal input pixel size of SAR data processing. When compared to in situ data, the selection of the optimal pixel size reduced the Root Mean Squared Error (RMSE) values of LG-Mod wind directions up to 7° and about 45° for Wadden Sea and the Camargue site, respectively. In turn, such reduction provided a decrease of the wind speed RMSE values up to 0.7 m/s and 2.1 m/s, for Wadden Sea and the Camargue site, respectively. In addition, the LG-Mod gave better performance than the global NWP model European Centre for Medium-Range Weather Forecasts (ECMWF) in estimation of wind direction, at 12.5 km output spatial resolution, for both sites. The 〈em〉ME〈/em〉〈sub〉〈em〉α〈/em〉〈/sub〉〈sup〉〈em〉ROI〈/em〉〈/sup〉 exploitation in the directional analysis of IW and EW products evidenced that at high resolution (5 km) the percentage of reliable wind directions from IW images (84.5%) resulted much larger than that obtained from EW images (30.1%). At medium resolution (12.5 km) instead, the percentage values resulted quite close to each other (99.2% and 86.3%, respectively). IW images proved optimal for high resolution SSW retrieval, whereas EW images suitable for medium resolution. With respect to NWP models, the spectral analysis confirmed the suitability of Sentinel-1 to represent the local wind fields spatial variability in coastal areas, at both high and medium output resolution. Our findings suggest that the combination of the LG-Mod algorithm with NWP models could better resolve spatially wind patterns in complex coastal areas.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Blaž Sovdat, Miha Kadunc, Matej Batič, Grega Milčinski〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The true color composite is a widely used Earth observation product for displaying satellite imagery. As it is often used in communication with non-expert audiences in various media, it is desirable that such a product approximates the color perceived by the human eye. Additionally, as the Sentinel-2 mission with its high resolution multispectral imagery and short revisit times is delivering unprecedented amounts of data, any algorithm for computing the composite should be efficient. In this paper we define the natural color product, propose two efficient approaches for computing it, analyze the results, and implement the products on a satellite imagery service for interactive use. Our algorithms work on a per-pixel basis and hence parallelize naturally. The presented approaches are general and not limited to Sentinel-2 data.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Liguang Jiang, Ole Baltazar Andersen, Karina Nielsen, Guoqing Zhang, Peter Bauer-Gottwein〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Water surface elevation (WSE) is an essential quantity for water resource monitoring and hydrodynamic modeling. Satellite altimetry has provided data for inland water bodies. The height that is derived from altimetry measurement is ellipsoidal height. In order to convert the ellipsoidal height to orthometric height, which has physical meaning, accurate estimates of the geoid are needed. This paper evaluates the suitability of geodetic altimetric measurements for improvement of global geoid models over a large lake in the Tibetan Plateau. CryoSat-2 and SARAL/AltiKa are used to derive the high-frequency geoid correction. A validation of the local geoid correction is performed with data from in-situ observations, a laser altimetry satellite (ICESat), a Ka-band radar altimetry satellite (SARAL) and a SAR radar altimetry satellite (Sentinel-3). Results indicate that the geodetic altimetric dataset can capture the high-resolution geoid information. By applying local geoid correction, the precision of ICESat, SARAL and Sentinel-3 retrievals are significantly improved. We conclude that using geodetic altimetry to correct for local geoid residual over large lakes significantly decreases the uncertainty of WSE estimates. These results also indicate the potential of geodetic altimetry missions to determine local geoid residual with centimeter-level accuracy, which can be used to improve global and regional geopotential models.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Radoslaw Guzinski, Héctor Nieto〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Sentinel satellite missions are designed to provide remote-sensing observational capability to many diverse operational applications, including in the field of agriculture and food security. They do this by acquiring frequent observations from a combination of optical, thermal and microwave sensors at various spatial resolutions. However, one currently missing capability, that would enable monitoring of evapotranspiration, crop water stress and water use at field scale, is the lack of high-resolution (tens of meters) thermal sensor. In this study we evaluate a methodology for bridging this data gap by employing a machine learning algorithm to sharpen low-resolution thermal observations from the Sentinel-3 satellites using images acquired by high-resolution optical sensors on the Sentinel-2 satellites. The resulting dataset is then used as input to land-surface energy balance model to estimate evapotranspiration. The methodology is tested using Terra and Landsat satellite observations, due to lack of sufficiently long time-series of Sentinel observations, and benchmarked against fluxes derived with high-resolution thermal observations acquired by the Landsat satellites. We then apply the methodology to Sentinel-2 and Sentinel-3 images to confirm its applicability to this type of data. The results show that the fluxes derived with sharpened thermal data are of acceptable accuracy (relative error lower than 20%) and provide more information at flux-tower footprint scale than the corresponding low-resolution fluxes. They also replicate the spatial and temporal patterns of fluxes derived with high-resolution thermal observations. However, the increase in error of the modelled fluxes compared to using high-resolution thermal observations and the inherent limitations of the sharpening approach point to the need to add high-resolution thermal mission to the Sentinels' constellation.〈/p〉〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Wenlu Qi, Seung-Kuk Lee, Steven Hancock, Scott Luthcke, Hao Tang, John Armston, Ralph Dubayah〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Interferometric Synthetic Aperture Radar (InSAR) and lidar are increasingly used active remote sensing techniques for forest structure observation. The TanDEM-X (TDX) InSAR mission of German Aerospace Center (DLR) and the upcoming Global Ecosystem Dynamics Investigation (GEDI) of National Aeronautics and Space Administration (NASA) together may provide more accurate estimates of global forest structure and biomass via their synergic use. In this paper, we explored the efficacy of simulated GEDI data in improving height estimates from TDX InSAR data. Our study sites span three major forest types: a temperate forest, a mountainous conifer forest, and a tropical rainforest. The GEDI lidar coverage was simulated for the full nominal two-year mission duration, under both cloud-free and 50%-cloud conditions. We then used these GEDI data to parameterize the Random Volume over Ground (RVoG) model driven by TDX imagery. In particular, we explored the following three strategies for forest structure estimation: 1) TDX data alone; 2) TDX + GEDI-derived digital terrain model (DTM); and 3) TDX + GEDI DTM + GEDI canopy height. We then validated the retrieved forest heights against wall-to-wall airborne lidar measurements. We found relatively large biases at 90 [m] spatial resolution, from 4.2–11.9 [m], and root mean square errors (RMSEs), from 7.9–12.7 [m] when using TDX data alone under constrained RVoG assumptions of a fixed extinction coefficient (〈em〉σ〈/em〉) and a zero ground-to-volume amplitude ratio (〈em〉μ〈/em〉 〈em〉=〈/em〉 〈em〉0〈/em〉). Results improved significantly with the aid of a DTM derived from GEDI data which enabled estimation of spatially-varying 〈em〉σ〈/em〉 values (vs. fixed extinction) under a 〈em〉μ〈/em〉 = 0 assumption, with biases reduced to 1.7–4.2 [m] and RMSEs to 4.9–8.6 [m] across cloudy and cloud-free cases. The best agreement was achieved in the third strategy by also incorporating information of GEDI-derived canopy height to further enhance the RVoG parameters. The improved model, when still assuming 〈em〉μ〈/em〉 = 0, reduced biases to less than or close to 1 m and further reduced RMSEs to 4.0–6.7 [m]. Finally, we used GEDI data to estimate spatially-varying 〈em〉μ〈/em〉 in the RVoG model. We found biases of between −0.7–0.9 [m] and RMSEs in the range from 2.6–7.1 [m] over the three sites. Our results suggest that use of GEDI data improves height inversion from TDX, providing heights at more accuracy than can be achieved by TDX alone, and enabling wall-to-wall height estimation at much finer spatial resolution than can be achieved by GEDI alone.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Liang Chang, Ruya Xiao, Abhnil Amtesh Prasad, Guoping Gao, Guiping Feng, Yu Zhang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Water vapor is the primary greenhouse gas of the Earth-atmosphere system and plays a vital role in understanding climate change, if correctly measured from satellites. The Moderate Resolution Imaging Spectroradiometer (MODIS) can monitor water vapor retrievals at near-infrared (nIR) bands in the daytime as well as at infrared (IR) bands in both daytime and night time. However, the accuracy of IR retrievals under confident clear conditions (〉99% probability) is much poorer than that of nIR retrievals. Additionally, IR retrievals under unconfident clear conditions (〉95%, 〉66% and ≤66% probabilities) are usually discarded because the possible presence of clouds would further reduce their accuracy. In this study, we develop a cloud mask-related differential linear adjustment model (CDLAM) to adjust IR retrievals under all confident clear conditions. The CDLAM-adjusted IR retrievals are evaluated with the linear least square (LS) adjusted nIR retrievals under confident clear condition and Global Positioning System (GPS) observations under different probabilities of clear conditions. Both case studies in the USA and global (65° S~65° N) evaluation reveal that the CDLAM can significantly reduce uncertainties in IR retrievals at all clear-sky confidence levels. Moreover, the accuracy of the CDLAM-adjusted IR retrievals under unconfident clear conditions is much better than IR retrievals without adjustment under confident clear conditions, highlighting the effectiveness of the CDLAM in enhancing the accuracy of IR retrievals at all clear-sky confidence levels as well as the data availability improvement of IR retrievals after adjustment with the CDLAM (14% during the analyzed time periods). The most likely reason for the efficiency of the CDLAM may be that the deviation of the differential water vapor information derived by the differential process is significantly shrunken after the linear regression analysis in the presented model. Therefore, the CDLAM is a promising tool for effectively adjusting IR retrievals under all probabilities of clear conditions and can improve our knowledge of the water vapor distribution and variation.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Eduardo Eiji Maeda, Filipe Lisboa, Laura Kaikkonen, Kari Kallio, Sampsa Koponen, Vanda Brotas, Sakari Kuikka〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Monitoring temporal changes in phytoplankton dynamics in high latitude lakes is particularly timely for understanding the impacts of warming on aquatic ecosystems. In this study, we analyzed 33-years of high resolution (30 m) Landsat (LT) data for reconstructing seasonal patterns of chlorophyll 〈em〉a〈/em〉 (chl 〈em〉a〈/em〉) concentration in four lakes across Finland, between 60°N and 64°N. Chl 〈em〉a〈/em〉 models based on LT spectral bands were calibrated using 17-years (2000–2016) of field measurements collected across the four lakes. These models were then applied for estimating chl 〈em〉a〈/em〉 using the entire LT-5 and 7 archives. Approximately 630 images, from 1984 to 2017, were analyzed for each lake. The chl 〈em〉a〈/em〉 seasonal patterns were characterized using phenology metrics, and the time-series of LT-based chl 〈em〉a〈/em〉 estimates were used for identifying temporal shifts in the seasonal patterns of chl 〈em〉a〈/em〉 concentration. Our results showed an increase in the length of phytoplankton growth season in three of the lakes. The highest increase was observed in Lake Köyliönjärvi, where the length of growth season has increased by 28 days from the baseline period of 1984–1994 to 2007–2017. The increase in the length of season was mainly attributed to an earlier start of phytoplankton blooms. We further analyzed surface temperature (T〈sub〉s〈/sub〉) and precipitation data to verify if climatic factors could explain the shifts in the seasonal patterns of chl 〈em〉a〈/em〉. We found no direct relationship between T〈sub〉s〈/sub〉 and chl 〈em〉a〈/em〉 seasonal patterns. Similarly, the phenological metrics of Ts, in particular length of season, did not show significant temporal trends. On the other hand, we identify potential links between changes in precipitation patterns and the increase in the phytoplankton season length. We verified a significant increase in the rainfall contribution to the total precipitation during the autumn and winter, accompanied by a decline in snowfall volumes. This could indicate an increasing runoff volume during the beginning of spring, contributing to an earlier onset of the phytoplankton blooms, although further assessments are needed to analyze historical streamflow values and nearby land cover data. Likewise, additional studies are needed to better understand why chl 〈em〉a〈/em〉 patterns in some lakes seem to be more resilient than in others.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Wei Zhao, Si-Bo Duan, Ainong Li, Gaofei Yin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Land surface temperature (LST) plays a key role in connecting land surface energy and water exchanges with near-surface atmosphere. However, the spatial distribution of LST over mountainous areas is not only strongly affected by the differences in surface thermal properties but also by the differences in thermal or radiative environment induced by the topographic variations, presenting significant terrain effect. This effect greatly hinders researches on surface energy fluxes and soil moisture estimation in these regions. To normalize the terrain effect, a random forest (RF)-based normalization approach was developed in this study and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data over a typical mountain region in the eastern part of the Tibetan Plateau, China. An LST linking model was first constructed to express the complicated interrelationship between LST and other surface variables, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), surface albedo (ALB), cumulative incident solar radiation (CSR), normalized difference water index (NDWI), surface elevation (ELV), and surface slope (SLP). The estimation results well indicated the good accuracy of the model with the coefficient of determination (R〈sup〉2〈/sup〉) above 0.92 for 90 selected days in 2015. Based on the good LST linking model, the LST normalization was achieved by replacing the topography affected factors (CSR, ELV, and SLP) with reference values for each pixel when keeping the rest status factors as their original values. The normalization result was indirectly validated by the normalization results with the traditional method based on the linear correction upon the regression between surface elevation and LST. The cross-validation clearly indicated the proposed method had an obvious advantage in reducing the topography-induced LST difference based on the average decrease in the temperature range (9.51 K) and the standard deviation (1.09 K) for the images on the 90 selected days over the study area. In contrast, the traditional method was hard to capture the terrain effect only based on the relationship between LST and surface elevation due to the complex interaction between LST and other factors. Overall, the proposed method shows a good application potential for normalizing the terrain effect on LST, and the results will be helpful for the LST-based estimation of surface energy fluxes or soil moisture over mountainous areas.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): J. Gómez-Enri, C.J. González, M. Passaro, S. Vignudelli, O. Álvarez, P. Cipollini, R. Mañanes, M. Bruno, M.P. López-Carmona, A. Izquierdo〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Coastal altimetry products are available and are being extensively validated. Their accuracy has been assessed in many coastal zones around the world and they are ready for exploitation near the shore. This opens a variety of applications of the sea level data obtained from the specific reprocessing of radar altimeter signals in the coastal strip. In this work, we retracked altimeter waveforms of the European Space Agency satellites: ERS-2 RA and Envisat RA-2 from descending track (#0360) over the eastern side of the Strait of Gibraltar using the Adaptive Leading Edge Sub-waveform (ALES) retracker. We estimated along-track Sea Level Anomaly (AT_SLA) profiles (RA-2) at high posting rate (18 Hz) using improved range and geophysical corrections. Tides were removed with a global model (DTU10) that displays a good performance in the study area: the mean root square sum (〈em〉RSS〈/em〉) of the main constituents obtained with 〈em〉DTU10〈/em〉 and 11 tide gauge stations was 4.3 cm in agreement with the 〈em〉RSS〈/em〉 using a high-resolution local hydrodynamic model (〈em〉UCA2.5D〈/em〉) (4.2 cm). We also estimated a local mean sea surface by reprocessing ERS-2/Envisat waveforms (track #0360) with ALES. The use of this local model gave more realistic AT_SLA than the values obtained with the global model DTU15MSS. Finally, the along-track Absolute Dynamic Topography (AT_ADT) was estimated using a local Mean Dynamic Topography obtained with the local hydrodynamic model UCA2.5D. We analysed the cross-strait variability of the sea level difference between the African/Spanish coasts along the selected track segment. This was compared to the sea level cross-strait difference from the records of two tide gauges located in the African (Ceuta) and Spanish (Tarifa) coasts. The sea level differences from altimetry and tide gauges were linked to the zonal component of the wind. We found a positive and significant (〉95% c.l.) correlation between easterlies/westerlies and positive/negative cross-strait sea level differences between the southern and northern coasts of the Strait in both datasets (altimetry: r = 0.54 and in-situ: r = 0.82).〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Zhanmang Liao, Binbin He, Xingwen Quan, Albert I.J.M. van Dijk, Shi Qiu, Changming Yin〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With the upcoming BIOMASS mission, P-band PolInSAR is expected to provide new perspectives on global forest aboveground biomass (AGB). However, its performance has not yet been fully evaluated for dense tropical forests with complex structure and very high biomass. Based on the TropiSAR campaign in French Guiana, we explored the challenges of the three most commonly used PolInSAR measures to capture AGB in tropical forests; coherence magnitude, interferometric phase, and backscatter. An improved AGB estimation approach was developed by integrating multiple information derived from single-baseline PolInSAR data. The approach involves ground-volume backscatter decomposition and combines volume backscatter with the retrieved forest height. Volume backscatter from the forest canopy was the best predictor of AGB for tropical forests, whereas the ground backscatter contribution was affected by the complex underlying surface and terrain slope. Both LiDAR- and PolInSAR-derived forest heights showed limited correlation with high AGB due to the varying forest basal area. The linear combination of PolInSAR-derived forest height and volume backscatter complemented each other and produced improved AGB estimates. Comparing three different PolInSAR data pairs, the proposed method produced an AGB map with an average R〈sup〉2〈/sup〉 of 0.7 and RMSE of 34 tons/ha (relative RMSE of 9.4%) at a spatial resolution of 125 × 125 m〈sup〉2〈/sup〉 for biomass between 250–500 tons/ha.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Mark J. Lara, Melissa L. Chipman, Feng Sheng Hu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Anthropogenic climate change has been linked to the degradation of permafrost across northern ecosystems, with notable implications for regional to global carbon dynamics. However, our understanding of the spatial distribution, temporal trends, and seasonal timing of episodic landscape deformation events triggered by permafrost degradation is hampered by the limited spatial and temporal coverage of high-resolution optical, RADAR, LIDAR, and hyperspectral remote sensing products. Here we present an automated approach for detecting permafrost degradation (thermoerosion), using meso-scale high-frequency remote sensing products (i.e., Landsat image archive). This approach was developed, tested, and applied in the ice-rich lowlands of the Noatak National Preserve (NOAT; 12,369 km〈sup〉2〈/sup〉) in northwestern Alaska. We identified thermoerosion (TE) by capturing the spectral signal associated with episodic sediment plumes in adjacent water bodies following TE. We characterized and extracted this episodic turbidity signal within lakes during the snow-free period (June 15–October 1) for 1986–2016 (continuous data limited to 1999–2016), using the cloud-based geospatial parallel processing platform, Google Earth Engine™. Thermoerosional detection accuracy was calculated using seven consecutive years of sub-meter high-resolution imagery (2009–2015) covering 798 (~33%) of the 2456 lakes in the NOAT lowlands. Our automated TE detection algorithm had an overall accuracy and kappa coefficient of 86% and 0.47 ± 0.043, indicating that episodic sediment pulses had a “moderate agreement” with landscape deformation associated with permafrost degradation. We estimate that lake shoreline erosion, thaw slumps, catastrophic lake drainage, and gully formation accounted for 62, 23, 13, and 2%, respectively, of active TE across the NOAT lowlands. TE was identified in ~5% of all lakes annually in the lowlands between 1999 and 2016, with a wide range of inter-annual variation (ranging from 0.2% in 2001 to 22% in 2004). Inter-annual variability in TE occurrence and spatial patterns of TE probability were correlated with annual snow cover duration and snow persistence, respectively, suggesting that earlier snowmelt accelerates permafrost degradation (e.g. TE) in this region. This work improves our ability to detect and attribute change in permafrost degradation across space and time.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Barbara Barzycka, Małgorzata Błaszczyk, Mariusz Grabiec, Jacek Jania〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The warming climate of the Arctic affects the mass budget of glaciers, and changes in the distribution of glacier facies are indicative of their response to climate change. The glacial mass budget over large land ice masses can be estimated by remote sensing techniques, but selecting an efficient remote sensing method for recognizing and mapping glacier facies in the Arctic remains a challenge. In this study, we compared several methods of distinguishing the facies of the Vestfonna ice cap, Svalbard, based upon Synthetic Aperture Radar (SAR) images and terrestrial high frequency Ground Penetrating Radar (GPR) measurements. Glacier zones as determined using the backscattering coefficient (sigma0) of SAR images were compared against GPR data, and an alternative application of Internal Reflection Energy (IRE) calculated from terrestrial GPR data was also used for differentiating the extent of glacier facies. The IRE coefficient was found to offer a suitable method for distinguishing glacier zones and for validating SAR analysis. Furthermore, results of analysis of fully polarimetric Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and European Remote Sensing Synthetic Aperture Radar (ERS-2 SAR) images were compared with the IRE coefficient classification. Especially promising method is H-α segmentation, where the glacier zone boundaries corresponded very well with both GPR visual interpretation and IRE classification results. The IRE coefficient's simplicity of calculation makes it a good alternative to the subjective GPR visual interpretation method, where results strongly depend on the operator's level of experience. We therefore recommend for GPR profiles to be used for additional validation of SAR image analysis in studies of glacier facies on the High Arctic ice masses.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Donghwan Kim, Hanwen Yu, Hyongki Lee, Edward Beighley, Michael Durand, Douglas E. Alsdorf, Euiho Hwang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hydraulic variables obtained from remotely sensed data have been successfully used to estimate river discharge (〈em〉Q〈/em〉). However, most studies have used a rating curve based on a single hydraulic variable or the Manning equation (multiplicative method). In this study, we developed a mathematically different approach to estimating 〈em〉Q〈/em〉 by applying the ensemble learning regression method (here termed ELQ), which is one of the machine learning techniques that linearly combine several functions to reduce errors, over the Congo mainstem as a test-bed. Using the training dataset (November 2002 – November 2006) of water levels (〈em〉H〈/em〉) derived from different Envisat altimetry observations, the ELQ-estimated 〈em〉Q〈/em〉 at the Brazzaville in-situ station showed reduced root-mean-square error (RMSE) of 823 m〈sup〉3〈/sup〉 s〈sup〉−1〈/sup〉 (relative RMSE (RMSE normalized by the average in-situ 〈em〉Q〈/em〉, RRMSE) of 2.08%) compared to the 〈em〉Q〈/em〉 obtained using a single rating curve. ELQ also showed improved performance for the validation dataset (December 2006 – September 2010). Based on the error analysis, we found the correlation coefficients between input variables affect the performance of ELQ. Thus, we introduced an index, termed the Degree of compensation (〈em〉I〈/em〉〈sub〉〈em〉DoC〈/em〉〈/sub〉), which describes how ELQ performs compared to the classic hydraulic relation (e.g., 〈em〉H〈/em〉-〈em〉Q〈/em〉 rating curve). The performance of ELQ improves when 〈em〉I〈/em〉〈sub〉〈em〉DoC〈/em〉〈/sub〉 increases because the additional information could be added in the ELQ process. Since ELQ can combine several variables obtained over different locations, it would be advantageous, particularly if there exist few virtual stations along a river reach. It is expected that ELQ can be also applied to the products of the Surface Water Ocean Topography (SWOT) mission, which will provide direct measurements of surface water slope (〈em〉S〈/em〉), effective river width (〈em〉W〈/em〉〈sub〉〈em〉e〈/em〉〈/sub〉), and 〈em〉H〈/em〉, to be launched in 2021.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 222〈/p〉 〈p〉Author(s): Emma K. Fiedler, Alison McLaren, Viva Banzon, Bruce Brasnett, Shiro Ishizaki, John Kennedy, Nick Rayner, Jonah Roberts-Jones, Gary Corlett, Christopher J. Merchant, Craig Donlon〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Six global, gridded, gap-free, daily sea surface temperature (SST) analyses covering a period of at least 20 years have been intercompared: ESA SST CCI analysis long-term product v1.0, MyOcean OSTIA reanalysis v1.0, CMC 0.2 degree, AVHRR_ONLY Daily 1/4 degree OISST v2.0, HadISST2.1.0.0 and MGDSST. A seventh SST product of the ensemble median of all six has also been produced using the GMPE (Group for High Resolution SST Multi-Product Ensemble) system. Validation against independent near-surface Argo data, a long timeseries of moored buoy data from the tropics and anomalies to the GMPE median have been used to examine the temporal and spatial homogeneity of the analyses. A comparison of the feature resolution of the analyses has also been undertaken. A summary of relative strengths and weaknesses of the SST datasets is presented, intended to help users to make an informed choice of which analysis is most suitable for their proposed application.〈/p〉〈/div〉

Description: 〈p〉Publication date: Available online 21 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Yun Yang, Martha Anderson, Feng Gao, Christopher Hain, Asko Noormets, Ge Sun, Randolph Wynne, Valerie Thomas, Liang Sun〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Forest ecosystem services such as clean water, wildlife habitat, and timber supplies are increasingly threatened by drought and disturbances (e.g., harvesting, fires and conversion to other uses), which can have great impacts on stand development and water balance. Improved understanding of the hydrologic response of forested systems to drought and disturbance at spatiotemporal resolutions commensurate with these impacts is important for effective forest management. Evapotranspiration (ET) is a key hydrologic variable in assessing forest functioning and health, but it remains a challenge to accurately quantify ET at landscape scales with the spatial and temporal detail required for effective decision-making. In this study, we apply a multi-sensor satellite data fusion approach to study the response of forest ET to drought and disturbance over a 7-year period. This approach combines Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) ET product time series retrieved using a surface energy balance model to generate a multi-year ET datacube at 30-m resolution and daily timesteps. The study area (~900 km〈sup〉2〈/sup〉) contains natural and managed forest as well as croplands in the humid lower coastal plains in North Carolina, USA, and the simulation period from 2006 to 2012 includes both normal and severe drought conditions. The model results were evaluated at two AmeriFlux sites (US-NC2 and US-NC1) dominated by a mature and a recently clearcut pine plantation, respectively, and showed good agreement with observed fluxes, with 8–13% relative errors at monthly timesteps. Changes in water use patterns in response to drought and disturbance as well as forest stand aging were assessed using the remotely sensed time series describing total evapotranspiration, the transpiration (T) component of ET, and a moisture stress metric given by the actual-to-reference ET ratio (〈em〉f〈/em〉〈sub〉〈em〉RET〈/em〉〈/sub〉). Analyses demonstrate differential response to drought by land cover type and stand age, with larger impacts on total ET observed in young pine stands than in mature stands which have substantially deeper rooting systems. Transpiration flux shows a clear ascending trend with the growth of young pine plantations, while stand thinning within the plantation leads to decreases in both remotely sensed leaf area index and T, as expected. Time series maps of 〈em〉f〈/em〉〈sub〉〈em〉RET〈/em〉〈/sub〉 anomalies at 30-m resolution capture signals of drought, disturbance and the subsequent recovery after clearcut at the stand scale and may be an effective indicator for water use change detection and monitoring in forested landscapes.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Abolfazl Irani Rahaghi, Ulrich Lemmin, Daniel Sage, David Andrew Barry〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Platform (BLIMP) that records LSWT images and an autonomously operating catamaran (called ZiviCat) that measures in situ surface/near surface temperatures within the image area, thus permitting simultaneous ground-truthing of the BLIMP data. The BLIMP was equipped with an uncooled InfraRed (IR) camera. The ZiviCat was designed to measure along predefined trajectories on a lake. Since LSWT spatial variability in each image is expected to be low, a poor estimation of the common spatial and temporal noise of the IR camera (nonuniformity and shutter-based drift, respectively) leads to errors in the thermal maps obtained. Nonuniformity was corrected by applying a pixelwise two-point linear correction method based on laboratory experiments. A Probability Density Function (PDF) matching in regions of overlap between sequential images was used for the drift correction. A feature matching-based algorithm, combining blob and region detectors, was implemented to create composite thermal images, and a mean value of the overlapped images at each location was considered as a representative value of that pixel in the final map. The results indicate that a high overlapping field of view (~95%) is essential for image fusion and noise reduction over such low-contrast scenes. The in situ temperatures measured by the ZiviCat were then used for the radiometric calibration. This resulted in the generation of LSWT maps at sub-pixel satellite scale resolution that revealed spatial LSWT variability, organized in narrow streaks hundreds of meters long and coherent patches of different size, with unprecedented detail.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Paheding Sidike, Vasit Sagan, Maitiniyazi Maimaitijiang, Matthew Maimaitiyiming, Nadia Shakoor, Joel Burken, Todd Mockler, Felix B. Fritschi〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurately mapping heterogeneous agricultural landscape is an important prerequisite for agricultural field management (e.g., weed control), plant phenotyping and yield prediction, as well as ecological characterization. Compared to traditional mapping practices that require intensive field surveys, remote sensing technologies offer efficient and cost-effective means for crop type mapping from regional to global scales. However, mapping heterogeneous agricultural landscape is a challenge because of diverse and complex spectral profiles of crops. We propose a novel deep learning method, namely deep progressively expanded network (dPEN), for mapping nineteen different objects including crop types, weeds and crop residues, in a heterogeneous agricultural field using WorldView-3 (WV-3) imagery. To assess the mapping accuracy of dPEN, we created a calibrated WV-3 dataset with the corresponding ground truth. In addition, the suitability of visible/near-infrared (VNIR, 400–1040 nm) and short-wave infrared (SWIR, 1195 nm–2365 nm) bands of WV-3 to classification accuracy were examined and discussed in detail. To the best of our knowledge, this is the first effort to explore the significance of all SWIR bands in WV-3 for classification accuracy in a heterogeneous agricultural landscape. The results demonstrated that: (1) The proposed dPEN allows for building a deeper neural network from multispectral data which was the limitation of many convolutional neural networks; (2) dPEN was able to extract more discriminative features from VNIR and SWIR bands by producing the highest overall accuracy (OA: 86.06%) over competing methods such as support vector machine and random forest; (3) The inclusion of WV-3 SWIR bands greatly improved the classification accuracy; (4) SWIR bands were particularly beneficial to improve the classification accuracy of some individual classes such as weeds, crop residues, and corn and soybean during late developmental stages; (5) The red-edge band (705–745 nm) was identified as the most important band affecting the classification accuracy nearly 10%, whereas the coastal band (400–450 nm) provided the lowest contribution; and (6) SWIR-5 band (2145–2185 nm) contributed most to OA by enhancing it approximately 4% when combined with VNIR bands, while SWIR-1 (1195–1225 nm) yielded the lowest improvement (1.55%) for OA. These research outcomes provide useful information for efficiently mapping agricultural landscape, and indicate the potential practices of dPEN and contributions of spectral bands in WV-3 for plant phenotyping, weed control, and crop residue retention.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Vaughn Smith, Carlos Portillo-Quintero, Arturo Sanchez-Azofeifa, Jose L. Hernandez-Stefanoni〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tracking the occurrence of deforestation events is an essential task in tropical dry forest (TDF) conservation efforts. Ideally, deforestation monitoring systems would identify a TDF clearing with near real time precision and high spatial detail, and alert park managers and environmental practitioners of illegal forest clearings occurring anywhere in a region of interest. Over the past several years there have been significant advances in the design and application of continuous land cover change mapping algorithms with these capabilities, but no studies have implemented such methods over human dominated TDF environments where small–scale deforestation (〈5 ha) is widespread and hard to detect with moderate resolution sensors. The general objective for this research was to evaluate the overall accuracy of the BFASTSpatial R Package for detecting and monitoring small-scale deforestation in four sites located in tropical dry forest landscapes of Mexico and Costa Rica using greenness and moisture spectral indices derived from Landsat time series. Results show a high degree of spatial agreement (90%–94%) between the distribution of TDF clearings occurred during the 2013–2016 period (as indicated by VHR imagery interpretation) and BFASTSpatial outputs. NDMI and NBR2 had the best performance than other indices and this is evidenced by the combined overall, user's and producer's accuracies. In particular, NBR2 were the most accurate predictor of deforestation with an overall accuracy of 94.5%. Our results also imply that monitoring sites at an annual basis is feasible using BFASTSpatial and LTS, but that lower confidence should be given to sub-annual products given significant systematic temporal differences between the BFASTSpatial monthly product and reference data. The possibility of including more clear observations at the spatial resolution of Landsat (30-m) or higher will greatly increase the spatial and temporal accuracies of the method. Given its performance, BFASTSpatial can help monitor hotspots of small-scale TDF loss across Central and North America at little or no cost. Users of the method should have a strong knowledge of the local land use and land cover dynamics and the ecophysiology of vegetation types present in the landscape. This local expertise is necessary for interpreting and validating results as well as communicating its output to decision-makers and stakeholders.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Jianbo Qi, Donghui Xie, Tiangang Yin, Guangjian Yan, Jean-Philippe Gastellu-Etchegorry, Linyuan Li, Wuming Zhang, Xihan Mu, Leslie K. Norford〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km〈sup〉2〈/sup〉) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BRFs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Lian Feng, Xuejiao Hou, Yi Zheng〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Various aspects of the water qualities of the large lakes in the Yangtze Plain (YP) have been studied, while the efforts have been spent on one of the most important parameters -water transparency were only limited to several large lakes. Using in situ remote sensing reflectance and Secchi-disk depth (Z〈sub〉sd〈/sub〉) datasets, we assessed the performance of a semi-analytical model recently proposed by Lee et al. (2015) for remote sensing of the Z〈sub〉sd〈/sub〉 (Z〈sub〉sd, Lee〈/sub〉). The results show that a linear scaling correction over Z〈sub〉sd, Lee〈/sub〉 (Z′〈sub〉sd, Lee〈/sub〉) could lead to improved agreement between remote sensing estimates and field measurements (root mean square error 〈 35%) in the study region. The Z′〈sub〉sd, Lee〈/sub〉 scheme was then applied to MODIS/Aqua observations between 2003 and 2016 to obtain the spatial and temporal dynamics of water transparency in 50 large lakes in the YP. The long-term mean Z〈sub〉sd〈/sub〉 of the entire region was 0.39 ± 1.17 m during the observation period, with high and low values occurring in warm and cold seasons, respectively. Of the 50 examined lakes, half demonstrated decreasing or increasing Z〈sub〉sd〈/sub〉 trends, and the number of lakes exhibiting significantly decreasing trends also comparable to the number exhibiting increasing trends. The relative contributions of the seven potential driving factors (from both human activities and natural processes) to the interannual changes in water clarity were quantified for each lake using a multiple general linear model regression analysis. The responses of Z〈sub〉sd〈/sub〉 to these drivers showed considerable differences among lakes, and human activities demonstrated significant roles in more lakes than those affected by natural variability, accounting for 50% (25/50) and 20% (10/50) of the lakes, respectively. This study provides the first comprehensive basin-scale estimate of the water transparency in the lakes in the YP, and the Z〈sub〉sd〈/sub〉 results and analysis of driving forces can provide important information for local water quality conservation and restoration.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 222〈/p〉 〈p〉Author(s): Tihomir S. Kostadinov, Rina Schumer, Mark Hausner, Kat J. Bormann, Rowan Gaffney, Kenneth McGwire, Thomas H. Painter, Scott Tyler, Adrian A. Harpold〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The distribution of snow cover is critical for predicting ecohydrological processes and underpins mountain water supplies in ranges like the Sierra Nevada in the Western United States. Many key water supply areas are covered by montane forests, which have substantial effects on the amount and timing of snowmelt. In-situ observations of snow-forest interactions have limited spatial coverage and remote sensing using optical sensors (e.g. MODIS) cannot observe snow cover below the canopy. In this study, we developed and verified a lidar-based method to detect snow cover under canopy, investigated how fractional snow covered area (fSCA) varies with topography in open versus under canopy areas and developed a correction factor that could be used to improve satellite-derived fSCA products. We developed our new method using three snow-on lidar overflights and verified it with in-situ distributed temperature sensor (DTS) observations at Sagehen Creek watershed in the Sierra Nevada, California, USA. DTS validation of lidar classifications showed excellent agreement at 85–96%, including high agreement and large number of returns in under canopy locations. The lidar-derived fSCA observations generally showed earlier snow disappearance under the canopy than in open positions, which is consistent with relatively warm temperatures and greater longwave radiation. However, in contrast to expectations, areas with high solar exposure (i.e. high southwestness) exhibited higher fSCA under the canopy. Results indicated that the 〈em〉k〈/em〉 factor (the ratio of under canopy fSCA to open fSCA) varied systematically with southwestness and elevation. Using this factor to correct the study domain fSCA indicated that the typical assumption that 〈em〉k〈/em〉 = 1 could lead to an up to ~0.05 bias (in fSCA units) towards overestimation. However, within 10 and 100-m individual pixels the fSCA overprediction bias can be 25–30% for higher fSCA values. Although uncertainty would be reduced using higher snow-on lidar point densities, our method shows promise to improve the typical assumption that snow disappearance is identical in under the canopy and in the open (〈em〉k〈/em〉 = 1). Future applications of our lidar-based method at different sites with varying climate, topography and vegetation structure has the dual potential to expand understanding of snow-forest interactions in complex terrain and improve operational fSCA products.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 222〈/p〉 〈p〉Author(s): E. Roteta, A. Bastarrika, M. Padilla, T. Storm, E. Chuvieco〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A locally-adapted multitemporal two-phase burned area (BA) algorithm has been developed using as inputs Sentinel-2 MSI reflectance measurements in the short and near infrared wavebands plus the active fires detected by Terra and Aqua MODIS sensors. An initial burned area map is created in the first step, from which tile dependent statistics are extracted for the second step. The whole Sub-Saharan Africa (around 25 M km〈sup〉2〈/sup〉) was processed with this algorithm at a spatial resolution of 20 m, from January to December 2016. This period covers two half fire seasons on the Northern Hemisphere and an entire fire season in the South. The area was selected as existing BA products account it to include around 70% of global BA. Validation of this product was based on a two-stage stratified random sampling of Landsat multitemporal images. Higher accuracy values than existing global BA products were observed, with Dice coefficient of 77% and omission and commission errors of 26.5% and 19.3% respectively. The standard NASA BA product (MCD64A1 c6) showed a similar commission error (20.4%), but much higher omission errors (59.6%), with a lower Dice coefficient (53.6%). The BA algorithm was processed over 〉11,000 Sentinel-2 images to create a database that would also include small fires (〈100 ha). This is the first time a continental BA product is generated from medium resolution sensors (spatial resolution = 20 m), showing their operational potential for improving our current understanding of global fire impacts. Total BA estimated from our product was 4.9 M km〈sup〉2〈/sup〉, around 80% larger area than what the NASA BA product (MCD64A1 c6) detected in the same period (2.7 M km〈sup〉2〈/sup〉). The main differences between the two products were found in regions where small fires (〈100 ha) account for a significant proportion of total BA, as global products based on coarse pixel sizes (500 m for MCD64A1) unlikely detect them. On the negative side, Sentinel-2 based products have lower temporal resolution and consequently are more affected by cloud/cloud shadows and have less temporal reporting accuracy than global BA products. The product derived from S2 imagery would greatly contribute to better understanding the impacts of small fires in global fire regimes, particularly in tropical regions, where such fires are frequent. This product is named FireCCISFD11 and it is publicly available at: 〈a href="https://www.esa-fire-cci.org/node/262" target="_blank"〉https://www.esa-fire-cci.org/node/262〈/a〉, last accessed on November 2018.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Wenyu Gong, Margaret M. Darrow, Franz J. Meyer, Ronald P. Daanen〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Frozen debris lobes (FDLs) are slow-moving landslides along permafrost-affected slopes, and consist of soil, rock, organic debris, and areas of massive infiltration ice. Based on their proximity to the adjacent infrastructure, their size, and their flow dynamics, FDLs represent potential geohazards. Eight FDLs (FDL-A, -B, -C, -D, -4, -5, -7, -11) within the Dalton Highway corridor in the Brooks Range, Alaska, USA are the subject of this paper. We examined temporal and spatial variation of FDL displacement using medium-resolution Synthetic Aperture Radar (SAR) images and differential SAR Interferometry (dInSAR) techniques. European Remote Sensing satellite 1/2 (ERS 1/2) and Phased Array type L-band Synthetic Aperture Radar (PALSAR) acquisitions were used to generate coherent interferograms to study the displacement history of FDLs for 1995–1996 and 2006–2010. We conducted an initial assessment of the capability of satellite InSAR to monitor FDL movement depending on data resolution, season, and vegetation coverage, which also helped us to select useful interferograms. With multi-temporal interferometric displacement maps, we found that seven investigated FDLs (FDL-7 was excluded from this sub-experiment due to limited data coverage) demonstrated strong spatial and seasonal variations in their movement patterns, with maximum displacement rates typically occurring in October and minimum displacement rates during February or March, which is consistent with previously published field study results. Overall, through this study we: (1) delineated the active FDLs during the winter period using a wrapped PALSAR interferogram; (2) analyzed the spatial variation of the deformation field within each FDL body; (3) modeled the seasonal changes of FDL deformation rates through the analysis of multi-temporal ERS tandem interferograms; and (4) integrated InSAR-derived deformation rates with those obtained through historical imagery analysis to determine long-term deformation rates. Results from this study fill the gaps left in the historical imagery analysis and provide important seasonal and spatial deformation data, which are essential in the development of a mitigation plan as these features approach infrastructure. We also summarize our research experience studying these moving features using satellite radar interferometry and believe this can be useful for future studies of similar features.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Zifeng Hu, Yiquan Qi, Xianqiang He, Yu-Huai Wang, Dong-Ping Wang, Xuhua Cheng, Xiaohui Liu, Tao Wang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The Taiwan Strait is the only channel connecting the South China Sea (SCS) and the East China Sea (ECS), and plays a key role in the material exchanges between SCS and ECS. The region however is poorly sampled during the northeast (NE) monsoon when severe weather hinders shipboard measurements. In this study, the surface circulations in the Taiwan Strait and the adjacent East China Sea are derived from the Geostationary Ocean Color Imager (GOCI) with a high spatiotemporal resolution. The satellite observations are limited by cloud over. Nevertheless, they provide the only surface flow observations useful in hypothesis testing. It is demonstrated that the transient surface currents can be classified into three regimes: a southwestward flow for strong winds, a northeastward flow for weak winds, and a cross-strait flow for moderate winds. The satellite observations are also valuable in validating model currents. Two high-resolution global ocean reanalysis products, the Hybrid Coordinate Ocean Model (HYCOM) and the Copernicus Marine Environment Monitoring Service (CMEMS), are examined. The reanalysis products are generally consistent with the satellite observations. The CMEMS moreover shows a superior performance. This study illustrates the unique capability of the geostationary ocean color satellite in providing spatially extensive surface flow observations in broad shelf regions where current meter data are sparse or non-existent.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 17 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Lan H. Nguyen, Deepak R. Joshi, David E. Clay, Geoffrey M. Henebry〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Over the last 20 years, substantial amounts of grassland have been converted to other land uses in the Northern Great Plains. Most of land cover/land use (LCLU) assessments in this region have been based on the U.S. Department of Agriculture - Cropland Data Layer (USDA - CDL), which may be inconsistent. Here, we demonstrate an approach to map land cover utilizing multi-temporal Earth Observation data from Landsat and MODIS. We first built an annual time series of accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperatures. Using the Enhanced Vegetation Index (EVI) derived from Landsat Collection 1's surface reflectance, we then fit at each pixel a downward convex quadratic model to each year's progression of AGDD (〈em〉i.e.〈/em〉, EVI = α + β × AGDD − γ × AGDD〈sup〉2〈/sup〉). Phenological metrics derived from fitted model and the goodness of fit then are submitted to a random forest classifier (RFC) to characterize LCLU for four sample counties in South Dakota in three years (2006, 2012, 2014) when reference point datasets are available for training and validation. To examine the sensitivity of the RFC to sample size and design, we performed classifications under different sample selection scenarios. The results indicate that our proposed method accurately mapped major crops in the study area but showed limited accuracy for non-vegetated land covers. Although all RFC models exhibit high accuracy, estimated land cover areas from alternative models could vary widely, suggesting the need for a careful examination of model stability in any future land cover supervised classification study. Among all sampling designs, the “same distribution” models (proportional distribution of the sample is like proportional distribution of the population) tend to yield best land cover prediction. RFC used only the most eight important variables (〈em〉e.g.〈/em〉, three fitted parameter coefficients [α, β, and γ]; maximum modeled EVI; AGDD at maximum modeled EVI; the number of observations used to fit CxQ model; and the number of valid observations) have slightly higher accuracy compared to those using all variables. By summarizing annual image time series through land surface phenology modeling, LCLU classification can embrace both seasonality and interannual variability, thereby increasing the accuracy of LCLU change detection.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Dirk Pflugmacher, Andreas Rabe, Mathias Peters, Patrick Hostert〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study analyzed, for the first time, the potential of combining the large European-wide land survey LUCAS (Land Use/Cover Area frame Survey) and Landsat-8 data for mapping pan-European land cover and land use. We used annual and seasonal spectral-temporal metrics and environmental features to map 12 land cover and land use classes across Europe. The spectral-temporal metrics provided an efficient means to capture seasonal variations of land surface spectra and to reduce the impact of clouds and cloud-shadows by relaxing the otherwise strong cloud cover limitations imposed by image-based classification methods. The best classification model was based on Landsat-8 data from three years (2014–2016) and achieved an accuracy of 75.1%, nearly 2 percentage points higher than the classification model based on a single year of Landsat data (2015). Our results indicate that annual pan-European land cover maps are feasible, but that temporally dynamic classes like artificial land, cropland, and grassland still benefit from more frequent satellite observations. The produced pan-European land cover map compared favorably to the existing CORINE (Coordination of Information on the Environment) 2012 land cover dataset. The mapped country-wide area proportions strongly correlated with LUCAS-estimated area proportions (〈em〉r〈/em〉 = 0.98). Differences between mapped and LUCAS sample-based area estimates were highest for broadleaved forest (map area was 9% higher). Grassland and seasonal cropland areas were 7% higher than the LUCAS estimate, respectively. In comparison, the correlation between LUCAS and CORINE area proportions was weaker (〈em〉r〈/em〉 = 0.84) and varied strongly by country. CORINE substantially overestimated seasonal croplands by 63% and underestimated grassland proportions by 37%. Our study shows that combining current state-of-the-art remote sensing methods with the large LUCAS database improves pan-European land cover mapping. Although this study focuses on European land cover, the unique combination of large survey data and machine learning of spectral-temporal metrics, may also serve as a reference case for other regions. The pan-European land cover map for 2015 developed in this study is available under 〈a href="https://doi.pangaea.de/10.1594/PANGAEA.896282" target="_blank"〉https://doi.pangaea.de/10.1594/PANGAEA.896282〈/a〉.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): M.G. Hethcoat, D.P. Edwards, J.M.B. Carreiras, R.G. Bryant, F.M. França, S. Quegan〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (〉20 m〈sup〉3〈/sup〉 ha〈sup〉−1〈/sup〉). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (〈15 m〈sup〉3〈/sup〉 ha〈sup〉−1〈/sup〉). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes.〈/p〉〈/div〉 〈/div〉 〈div xml:lang="en"〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0034425718305534-ga1.jpg" width="301" alt="Unlabelled Image" title="Unlabelled Image"〉〈/figure〉〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 5 December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Xinxin Wang, Xiangming Xiao, Zhenhua Zou, Bangqian Chen, Jun Ma, Jinwei Dong, Russell B. Doughty, Qiaoyan Zhong, Yuanwei Qin, Shengqi Dai, Xiangping Li, Bin Zhao, Bo Li〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986–1992, an increasing period during 1993–2001 and a decreasing period during 2002–2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Tongren Xu, Xinlei He, Sayed M. Bateni, Thomas Auligne, Shaomin Liu, Ziwei Xu, Ji Zhou, Kebiao Mao〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Estimation of turbulent heat fluxes by assimilating sequences of land surface temperature (LST) measurements into variational data assimilation (VDA) frameworks has been the subject of several studies. The VDA approaches estimate turbulent heat fluxes by minimizing the difference between LST observations and estimations from the heat diffusion equation. The VDA methods have been tested only with high temporal resolution LST observations (e.g., from geostationary satellites) when applied at regional scales. Geostationary satellites can capture the diurnal cycle of LST, but they have a relatively low spatial resolution and mainly focus on low latitudes. To overcome these shortcomings, this study assimilates high spatial resolution LST data from polar orbiting satellites (e.g., Moderate Resolution Imaging Spectroradiometer, MODIS) into the combined-source (CS) and dual-source (DS) VDA schemes. An expression is developed to obtain an a priori evaporative fraction (EF) estimate from leaf area index (LAI) or apparent thermal inertia (ATI). The a priori EF estimate is used as an initial guess in the VDA approach. The results indicate that the VDA method is able to find the optimal value of EF by assimilating the low-temporal resolution MODIS LST data. The predicted turbulent heat fluxes from VDA are compared with the measurements from the large-aperture scintillometer at three sites (Arou, Daman, and Sidaoqiao) in the Heihe River Basin (located in northwest China). The findings indicate that the CS and DS VDA models perform well in various hydrological and vegetative conditions. The three-site-average root mean square errors (RMSEs) of sensible and latent heat fluxes estimates from the CS scheme are 37.44 W m〈sup〉−2〈/sup〉 and 94.30 W m〈sup〉−2〈/sup〉, respectively. The DS model reduces the abovementioned RMSEs by 19.82% and 21.37%, respectively. Overall, the results show that using the a priori EF estimate from the proposed expression in the VDA approach eliminates the need for the high resolution LST data from geostationary satellites, and allows the VDA method to estimate turbulent heat fluxes by assimilating LST data from polar orbiting satellites. Finally, several numerical tests are conducted to assess the effect of LST temporal sampling on the turbulent heat fluxes estimates. The results show that the LST measurement at 1400 Local Time (LT) has the most amount of information for partitioning the available energy into sensible and latent heat fluxes.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): Liheng Zhong, Lina Hu, Hang Zhou〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study aims to develop a deep learning based classification framework for remotely sensed time series. The experiment was carried out in Yolo County, California, which has a very diverse irrigated agricultural system dominated by economic crops. For the challenging task of classifying summer crops using Landsat Enhanced Vegetation Index (EVI) time series, two types of deep learning models were designed: one is based on Long Short-Term Memory (LSTM), and the other is based on one-dimensional convolutional (Conv1D) layers. Three widely-used classifiers were also tested for comparison, including a gradient boosting machine called XGBoost, Random Forest, and Support Vector Machine. Although LSTM is widely used for sequential data representation, in this study its accuracy (82.41%) and F1 score (0.67) were the lowest among all the classifiers. Among non-deep-learning classifiers, XGBoost achieved the best result with 84.17% accuracy and an F1 score of 0.69. The highest accuracy (85.54%) and F1 score (0.73) were achieved by the Conv1D-based model, which mainly consists of a stack of Conv1D layers and an inception module. The behavior of the Conv1D-based model was inspected by visualizing the activation on different layers. The model employs EVI time series by examining shapes at various scales in a hierarchical manner. Lower Conv1D layers of the optimized model capture small scale temporal variations, while upper layers focus on overall seasonal patterns. Conv1D layers were used as an embedded multi-level feature extractor in the classification model which automatically extracts features from input time series during training. The automated feature extraction reduces the dependency on manual feature engineering and pre-defined equations of crop growing cycles. This study shows that the Conv1D-based deep learning framework provides an effective and efficient method of time series representation in multi-temporal classification tasks.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 221〈/p〉 〈p〉Author(s): George Azzari, Patricio Grassini, Juan Ignacio Rattalino Edreira, Shawn Conley, Spyridon Mourtzinis, David B. Lobell〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Low-intensity tillage has become more popular among farmers in the United States and many other regions. However, accurate data on when and where low-intensity tillage methods are being used remain scarce, and this scarcity impedes understanding of the factors affecting the adoption and the agronomic or environmental impacts of these practices. In this study, we used composites of satellite imagery from Landsat 5, 7, and 8, and Sentinel-1 in combination with producer data from about 5900 georeferenced fields to train a random forest classifier and generate annual large-scale maps of tillage intensity from 2005 to 2016. We tested different combinations of hyper-parameters using cross-validation, splitting the training and testing data alternatively by field, year, and state to assess the influence of clustering on validation results and evaluate the generalizability of the classification model. We found that the best model was able to map tillage practices across the entire North Central US region at 30 m-resolution with accuracies spanning between 75% and 79%, depending on the validation approach. We also found that although Sentinel-1 provides an independent measure that should be sensitive to surface moisture and roughness, it currently adds relatively little to classification performance beyond what is possible with Landsat. When aggregated to the state level, the satellite estimates of percentage low- and high-intensity tillage agreed well with a USDA survey on tillage practices in 2006 (R〈sup〉2〈/sup〉 = 0.55). The satellite data also revealed clear increases in low-intensity tillage area for most counties in the past decade. Overall, the ability to accurately map spatial and temporal patterns in tillage should facilitate further study of this important practice in the United States, as well as other regions with fewer survey-based estimates.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 234〈/p〉 〈p〉Author(s): Jiong Wang, Monika Kuffer, Debraj Roy, Karin Pfeffer〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Machine learning techniques have been frequently applied to map urban deprivation (commonly referred to as slums) in very high-resolution satellite images. Among these, Deep Convolutional Neural Networks have shown exceptional efficiency in automated deprivation mapping at the local scale. Yet these networks have never been used to map very small heterogeneous deprivation areas (pockets) at large scale. This study proposes and evaluates a U-Net-Compound model to map deprivation pockets in Bangalore, India. The model only relies on RGB satellite images with a resolution of 2 m as these are more commonly accessible to local urban planning departments. The experiment assumes a practical situation where only limited reference data is available for the model to learn the spatial morphology of deprivation pockets. It tests whether an updated map of deprivation pockets can be obtained with limited information. The model performance to map a large number of deprivation pockets is examined by incrementally changing the model architecture and the amount of training data. Results show that the proposed model is sensitive to the amount of spatial information contained in the training data. Once sufficient spatial information is learnt through a few samples, the city scale mapping accuracy outperforms existing models in mapping small deprivation pockets, achieving a Jaccard Index of 54%. This study demonstrated that a well-designed convolutional neural network can map the existence, extent, as well as distribution patterns of deprivation pockets at the city scale with limited training data, which is essential for upscaling research outputs to provide important information for the formulation of pro-poor policies.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 234〈/p〉 〈p〉Author(s): Yuhan Rao, Shunlin Liang, Dongdong Wang, Yunyue Yu, Zhen Song, Yuan Zhou, Miaogen Shen, Baiqing Xu〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract:〈/h5〉 〈div〉〈p〉The Tibetan Plateau (TP) has experienced rapid warming in recent decades. However, the meteorological stations of the TP are scarce and mostly located at the eastern and southern parts of the TP where the elevation is relatively low, which increases the uncertainty of regional and local climate studies. Recently, the remotely sensed land surface temperature (LST) has been used to estimate the surface air temperature (SAT). However, the thermal infrared based LST is prone to cloud contamination, which limits the availability of the estimated SAT. This study presents a novel all sky model based on the rule-based Cubist regression to estimate all sky daily average SAT using LST, incident solar radiation (ISR), top-of-atmosphere (TOA) albedo and outgoing longwave radiation (OLR). The model is trained using station data of the Chinese Meteorological Administration (CMA) and corresponding satellite products. The output is evaluated using independent station data with the bias of −0.07 °C and RMSE of 1.87 °C. Additionally, the 25-fold cross validation shows a stable model performance (RMSE: 1.6–2.8 °C). Moreover, the all sky Cubist model increases the availability of the estimated SAT by nearly three times. We used the all sky Cubist model to estimate the daily average SAT of the TP for 2002–2016 at 0.05° × 0.05°. We compared our all sky Cubist model estimated daily average SAT with three existing data sets (i.e., GLDAS, CLDAS, and CMFD). Our model estimation shows similar spatial and temporal dynamics with these existing data sets but outperforms them with lower bias and RMSE when benchmarked against the CMA station data. The estimated SAT data could be very useful for regional and local climate studies over the TP. Although the model is developed for the TP, the framework is generic and may be extended to other regions with proper model training using local data.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 234〈/p〉 〈p〉Author(s): Yao Zhang, Sha Zhou, Pierre Gentine, Xiangming Xiao〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Plant water use strategy is one of the key factors to predict drought impact on vegetation and land-atmosphere fluxes. Vegetation optical depth (VOD) based on microwave radiative transfer inversion has recently been used to assess plant water use strategy. However, VOD is sensitive to both total aboveground biomass (AGB) and leaf water content, with only the latter being a proxy of leaf water potential whose diurnal variation can be used to characterize vegetation iso/anisohydricity. In this study, by using a network of soil water measurements (used as a proxy for predawn leaf water potential), satellite retrieved normalized difference vegetation index (NDVI, as a proxy for AGB), and two satellite VOD products from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor, we compare three linear models and one machine learning model to investigate to what extent can VOD be used to represent leaf water potential changes during soil moisture dry-down periods. Linear models with both NDVI and leaf water potential, on average, can explain 33% and 51% of VOD variations of each product respectively. Models using only NDVI explain 27% and 46% of the VOD variance, compared to less than 10% by models using leaf water potential only. With the NDVI and leaf water potential (full) model, leaf water potential contributes around 17% of the VOD variance, which is smaller than NDVI (33%). The machine learning model has overall better performance than the linear models, and also highlight the dominant contribution of AGB to VOD signals. After the AGB contribution to VOD is eliminated by normalizing daytime VOD with nighttime VOD, the residuals carry the information of diurnal variations of leaf water potential and calculations from both VOD datasets are consistent with each other (〈em〉r〈/em〉 = 0.42〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mrow〉〈mo linebreak="goodbreak" linebreakstyle="after"〉±〈/mo〉〈/mrow〉〈/math〉0.17, 〈em〉P〈/em〉 〈 0.01 for 88 out of 94 sites). The response of 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈mrow〉〈mfrac〉〈mrow〉〈msub〉〈mrow〉〈mtext〉VOD〈/mtext〉〈/mrow〉〈mrow〉〈mtext〉daytime〈/mtext〉〈/mrow〉〈/msub〉〈/mrow〉〈mrow〉〈msub〉〈mrow〉〈mtext〉VOD〈/mtext〉〈/mrow〉〈mrow〉〈mtext〉nighttime〈/mtext〉〈/mrow〉〈/msub〉〈/mrow〉〈/mfrac〉〈/mrow〉〈/math〉 to soil water potential can also be used as a new metric for ecosystem iso/anisohydricity. Our study demonstrates that a large proportion of variations in VOD are caused by AGB for temperate ecosystems, and higher accuracy VOD products with additional root-zone soil water potential are needed for ecosystem iso/anisohydricity estimations.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: November 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 233〈/p〉 〈p〉Author(s): Erin L. Bunting, Seth M. Munson, John B. Bradford〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Climate variability and change acting at broad scales can lead to divergent changes in plant production at local scales. Quantifying how production responds to variation in climate at local scales is essential to understand underlying ecological processes and inform land management decision-making, but has historically been limited in spatiotemporal scale based on the use of discrete ground-based measurements or coarse resolution satellite observations. With the advent of cloud-based computing through Google Earth Engine (GEE), production responses to climate can be evaluated across broad landscapes though time at a resolution useful for ecological and land management applications. Here, GEE was employed to synthesize a multi-platform Landsat time series (1988–2014) and evaluate relationships between the soil-adjusted vegetation index (a proxy for plant production) and climate across deserts and plant communities of the southwestern U.S. A “climate pivot point” approach was adopted in GEE to assess the trade-off between production responses to increasing wetness and resistances to drought at 30-m resolution. Consistent with a long-term seasonal climate gradient, production was most related to climate variance during the cool-season in the western deserts, during the warm-season in the eastern deserts, and equally related to both seasons within several desert areas. Communities dominated by grasses and deciduous trees displayed large production responses to an increase in wetness and low resistances to water deficit, while shrublands and evergreen woodlands had variable responses and high drought resistances. Production in plant communities that spanned multiple deserts responded differently to seasonal climate variability in each desert. Defining these plant production sensitivities to climate at 30-m resolution in GEE advances forecasts of how long-term climate trajectories may affect carbon storage, wildlife habitat, and the vulnerability of water-limited ecosystems.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 1 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 234〈/p〉 〈p〉Author(s): S. Pepe, L. De Siena, A. Barone, R. Castaldo, L. D'Auria, M. Manzo, F. Casu, M. Fedi, R. Lanari, F. Bianco, P. Tizzani〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Observations from satellites provide high-resolution images of ground deformation allowing to infer deformation sources by developing advanced modeling of magma ascent and intrusion processes. Nevertheless, such models can be strongly biased without a precise model of the internal structure of the volcano. In this study, we jointly exploited two interferometric techniques to interpret the 2011–2013 unrest at Campi Flegrei caldera (CFc). The first is the Interferometric Synthetic Aperture Radar (InSAR) technique, which provides highly-resolved spatial and temporal images of ground deformation. The second is the Ambient Noise Tomography (ANT), which images subsurface structures, providing the constraints necessary to infer the depth of the shallow source at CFc (between 0.8 and 1.2 km). We applied for the first time a tool to delineate the deformation source boundaries from the observed deformation maps: the Total Horizontal Derivative (THD) technique. The THD processes the vertical component of the ground deformation field detected through InSAR applied to COSMO-SkyMed data. The patterns retrieved by applying the THD technique show consistent spatial correlations with (1) the seismic group-velocity maps achieved through the ANT and (2) the distribution of the earthquakes nucleated during the unrest at ~1 km. High-velocity anomalies, the retrieved geometrical features of the deformation field, and the spatial distribution of seismicity coincide with extinct volcanic vents in the eastern part of the caldera (Solfatara/Pisciarelli and Astroni). Such a coincidence hints at a significant role of the extinct plumbing system in either constraining or channeling the eastward propagation of magmatic fluids. Here, we demonstrated that a joint analysis of the InSAR patterns, seismic structures, and seismicity allows us to model in space and time the characteristics and nature of the shallow deformation source at CFc. Using published literature, we show that the effects of structural heterogeneities at shallow depths may have a more significant early-stage impact on the evolution of the surface displacement signals than deeper magmatic sources: these secondary structural effects may produce local amplification in the deformation records which can be mistakenly interpreted as early signals of impending eruptions. The achieved results are particularly relevant for the understanding of the origin of deformation signal at volcanoes where magma propagation within sills is expected, as at CFc.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 235〈/p〉 〈p〉Author(s): Liujun Zhu, Jeffrey P. Walker, Leung Tsang, Huanting Huang, Nan Ye, Christoph Rüdiger〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increased availability of spaceborne radar data projected over the next decade provides a great opportunity for operational soil moisture mapping with high spatial (〈50 m) and temporal (〈3 days) resolution, by combining the data from multiple SAR missions. Accordingly, a multi-frequency soil moisture retrieval framework has been proposed, being applicable for SAR missions operating at the commonly used remote sensing frequencies of L-, C- and X-band. A combination of numerical, physical and semi-empirical scattering models was selected to build a series of forward modeling look up tables (LUTs) covering the typical radar configurations and nature surface conditions. An unsupervised change detection method was integrated to identify areas with abrupt roughness and vegetation changes, so that time-series data collected from different SARs can be combined with the assumption of time-invariant roughness and vegetation. The multi-frequency backscattering coefficient (σ〈sup〉0〈/sup〉) with negligible scattering from soil surface (equivalent to calibration uncertainty) was then removed before soil moisture retrieval. Finally, soil moisture retrieval was carried out independently for each landcover type using an optimization method and forward LUTs. Evaluation based on the Soil Moisture Active Passive Experiment-5 dataset consisting of L-band airborne data, C-band RADARSAT-2 data and X-band COSMO-SkyMed data showed an acceptable overall root mean square error (RMSE) of 0.058 cm〈sup〉3〈/sup〉/cm〈sup〉3〈/sup〉 at the paddock scale (~0.1 – 0.5 km). The comparison with single and dual frequency retrieval suggests that multi-frequency retrieval is not necessary to have the highest accuracy. However, it is still valuable to joint use multi-frequency data consider the limited deterioration in accuracy and the significantly enhanced temporal resolution.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 235〈/p〉 〈p〉Author(s): Victoria Vanthof, Richard Kelly〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Monitoring small water bodies (〈50 ha) is difficult due to their size, limiting accurate assessments of surface water (SW) resources required for agricultural production and watershed hydrology. In environments where livelihoods depend on surface-water storage (SWS) structures, satellite altimetry-derived water levels are often unavailable. Therefore, seasonal reservoirs require a robust and cost-effective approach for estimating SWS. To approximate SWS throughout a typical environmental setting where irrigation reservoirs are found, we investigate the utility of TanDEM-X digital elevation model (DEM) to extract bathymetry of seasonal reservoir structures. Empirically-derived SWS relationships are combined with estimates of SW area from radar and multi-source optical satellites to illustrate the potential for rapid SWS estimation using satellite-based SW extent as input. Two application examples illustrate the approach: (i) estimating the maximum volume of water in a southern Indian river basin for two monsoon seasons; and (ii) a time-series analysis using a high-volume of satellite observations to show the cycle of water (inflow and outflow) at the reservoir scale. SWS volumes at water levels below 1.5 m were estimated within an absolute volume error range of 6–8%. This study illustrates the applicability and challenges of using satellite remote sensing observations to continuously monitor reservoir SWS. Despite the cloud independent capability for operational monitoring of SW area, Sentinel-1 data should be combined with frequent and high spatial resolution CubeSat observations for hydrometric monitoring of reservoirs to reduce observation errors. Furthermore, we highlight the multi-sensor approach (optical and radar) to achieve high spatio-temporal resolution monitoring of small reservoirs over large spatial scales.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: 15 December 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 235〈/p〉 〈p〉Author(s): Lauren Zweifel, Katrin Meusburger, Christine Alewell〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Soil degradation on Alpine grasslands is triggered mainly by extreme topography, prevailing climate conditions and land use practices. Suitable monitoring tools are required to assess soil erosion with high temporal and spatial resolution. In this study, we present an unprecedented and comprehensive approach based on object-based image analysis (OBIA) to map and assess all occurring erosion processes within a catchment (Urseren Valley, Switzerland). Five high-resolution (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"〉〈mtext〉0.25−0.5〈/mtext〉〈mspace width="0.25em"〉〈/mspace〉〈mtext〉m〈/mtext〉〈/math〉) orthophotos with RGB spectral information (SwissImage) produced during a 16-yr period were analyzed. Soil erosion sites are classified according to their type (shallow landslide or sites with reduced vegetation cover affected by sheet erosion) or the triggering land use management impacts (haying, trampling) with the Overall Accuracy ranging between 78 and 88% (Kappa 0.65–0.81) for the different years. The area affected by soil erosion increases for all classes during the study period (2000–2016) by a total of 156 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"〉〈mo〉±〈/mo〉〈/math〉 18% (increase consisting of 3% shallow landslides, 5% livestock trails, 46% sheet erosion and 46% management effects). Slopes at lower elevations (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"〉〈mtext〉〈1800〈/mtext〉〈mspace width="0.25em"〉〈/mspace〉〈mtext〉m〈/mtext〉〈mspace width="0.25em"〉〈/mspace〉〈mtext〉asl〈/mtext〉〈/math〉) are increasingly affected by livestock trails and sheet erosion caused by trampling and grazing as well as other management practices. For areas located above the agricultural land use, an increase in shallow landslides, as well as sheet erosion, can be observed. This points to climate change as a triggering factor of soil degradation, which has not been identified so far as a factor for soil erosion in the Urseren Valley. While OBIA yields conservative estimations mainly due to limitations of spatial resolutions, the method facilitates a comprehensive overview of the ongoing temporal and spatial development regarding soil degradation within the Urseren Valley.〈/p〉〈/div〉 〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0034425719304602-fx1.jpg" width="500" alt="Image 1" title="Image 1"〉〈/figure〉〈/p〉〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Paul M. Montesano, Christopher S.R. Neigh, William Wagner, Margaret Wooten, Bruce D. Cook〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Surface elevation estimates from high resolution spaceborne image (HRSI) stereogrammetry are used to examine fine-scaled structure of boreal forest canopies. These data can depict detailed spatial patterns of vertical forest structure at remote sites across the circumpolar domain where these estimates would otherwise be unavailable. This work examines where these estimates are most effective at describing vertical forest structure to explain which canopy surfaces they represent. We evaluated the variation in canopy surface estimates captured from four general types of HRSI digital surface models (DSMs) across the full range of boreal canopy cover. These DSMs, classified into 4 types by grouping them according to the acquisition's (1) sun elevation angle (low or high) and (2) seasonality-driven ground surface condition (snow presence/absence), vary with acquisition characteristics and the details of this variation continues to be studied. We explored some of this variation by comparing the distributions of differences in boreal canopy percentile heights derived from reference small footprint lidar in Tanana Valley, Alaska with canopy surface elevations derived from these 4 types of HRSI DSMs. We examined how canopy surface estimates from HRSI DSMs differ according to acquisition characteristics and canopy cover, and ultimately which canopy surfaces are represented in these DSMs. Our results help clarify which boreal canopy surfaces are representative of those captured with HRSI DSMs. They show that in the Tanana Valley (1) DSMs grouped by sun elevation angle and ground surface condition provide different surface estimates of boreal canopies; (2) the two DSM types that appear to most differently capture boreal forest canopy surfaces are DSMs from snow-free images acquired at sun elevation angles 〈30° (Low sun elev. & snow-free) and those with snow-cover at sun elevation angles ≥30° (High sun elev. & snow-free); (3) DSMs with snow most often do not capture upper canopy surfaces; (4) the “Low sun elev. & snow-free” DSMs resolve surfaces that are most representative of upper canopy surfaces (dense forests 〉60% cover, 70th–80th percentile heights); and (5) in the most dense forests (〉80% cover) where canopy gaps are least likely to bias downward the average surface estimates, the snow-free DSM types are representative of 70th - 80th percentile heights (“Low sun elev. & snow-free”) and 60th–70th percentile heights (“High sun elev. & snow-free”). The combination of horizontal structure (canopy cover) and acquisition characteristics affect the boreal vertical structure (canopy surface height) estimates from spaceborne stereogrammetry. These effects should be considered when analyzing products derived from HRSI DSMs, and as part of a comprehensive approach to spaceborne remote sensing of circumpolar boreal forests.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Chengfeng Le, Yiyang Gao, Wei-Jun Cai, John C. Lehrter, Yan Bai, Zong-Pei Jiang〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉A satellite-based semi-mechanistic model was developed to estimate the sea surface partial pressure of CO〈sub〉2〈/sub〉 (〈em〉p〈/em〉CO〈sub〉2〈/sub〉) on the river-dominated Louisiana Continental Shelf (LCS) in summer months using satellite-derived products including chlorophyll-a concentration (Chl-a〈sub〉_sat〈/sub〉), sea surface salinity (SSS〈sub〉_sat〈/sub〉), and sea surface temperature (SST〈sub〉_sat〈/sub〉). The model analytically expresses 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 as the sum of physical (〈em〉p〈/em〉CO〈sub〉2,mix〈/sub〉) and biological (Δ〈em〉p〈/em〉CO〈sub〉2,bio〈/sub〉) contributions which are the primary controlling factors associated with the 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 variability on the LCS. The SSS〈sub〉_sat〈/sub〉, derived from a locally calibrated retrieval algorithm, was used in a two-end member river-ocean mixing model to estimate the concentrations of dissolved inorganic carbon (DIC) and total alkalinity (TA) resulting from physical mixing. The mixing-induced DIC and TA were used together with SST〈sub〉_sat〈/sub〉 to calculate the 〈em〉p〈/em〉CO〈sub〉2,mix〈/sub〉 resulting from the mixing processes at the in situ temperature. The biological uptake of 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 through net community production (Δ〈em〉p〈/em〉CO〈sub〉2,bio〈/sub〉) was derived from a previously validated local Chl-a〈sub〉_sat〈/sub〉 product along with the difference between the 〈em〉p〈/em〉CO〈sub〉2,mix〈/sub〉 and the extensive in situ underway 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 data. The satellite-derived 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 agreed with in situ measurements with a median absolute difference of 37 μatm, mean difference of −5 μatm, and a root mean square difference of 58 μatm for five summer cruises spanning 2006–2009. The semi-mechanistic remote sensing model differentiates physical and biological influences on the 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 variability, which will help to better investigate the variability and the underlying controlling mechanisms of 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 and CO〈sub〉2〈/sub〉 flux on the LCS. This study demonstrates that the use of locally calibrated satellite products (Chl-a〈sub〉_sat〈/sub〉, SSS〈sub〉_sat〈/sub〉) should be promoted to improve the satellite estimation of 〈em〉p〈/em〉CO〈sub〉2〈/sub〉 in coastal waters. This method can potentially be applied in other river-dominated continental shelves with local tuning.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: Available online 13 March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment〈/p〉 〈p〉Author(s): Zhe Zhu, Junxue Zhang, Zhiqiang Yang, Amal H. Aljaddani, Warren B. Cohen, Shi Qiu, Congliang Zhou〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We developed a new algorithm for COntinuous monitoring of Land Disturbance (COLD) using Landsat time series. COLD can detect many kinds of land disturbance continuously as new images are collected and provide historical land disturbance maps retrospectively. To better detect land disturbance, we tested different kinds of input data and explored many time series analysis techniques. We have several major observations as follows. 〈em〉First〈/em〉, time series of surface reflectance provides much better detection results than time series of Top-Of-Atmosphere (TOA) reflectance, and with some adjustments to the temporal density, time series from Landsat Analysis Ready Data (ARD) is better than it is from the same Landsat scene. 〈em〉Second〈/em〉, the combined use of spectral bands is always better than using a single spectral band or index, and if all the essential spectral bands have been employed, the inclusion of other indices does not further improve the algorithm performance. 〈em〉Third〈/em〉, the remaining outliers in the time series can be removed based on their deviation from model predicted values based on probability-based thresholds derived from normal or chi-squared distributions. 〈em〉Fourth〈/em〉, model initialization is pivotal for monitoring land disturbance, and a good initialization stability test can influence algorithm performance substantially. 〈em〉Fifth〈/em〉, time series model estimation with eight coefficients model, updated in every year, based on all available clear observations achieves the best result. 〈em〉Sixth〈/em〉, a change probability of 0.99 (chi-squared distribution) with six consecutive anomaly observations and a mean included angle 〈 45° to confirm a change provide the best results, and the combined use of temporally-adjusted Root Mean Square Error (RMSE) and minimum RMSE is recommended. 〈em〉Finally〈/em〉, spectral changes (or “breaks”) contributed from vegetation regrowth should be excluded from the land disturbance maps. The COLD algorithm was developed and calibrated based on all these lessons learned above. The accuracy assessment shows that COLD results were accurate for detecting land disturbance, with an omission error of 27% and a commission error of 28%.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Michael A. Wulder, Thomas R. Loveland, David P. Roy, Christopher J. Crawford, Jeffrey G. Masek, Curtis E. Woodcock, Richard G. Allen, Martha C. Anderson, Alan S. Belward, Warren B. Cohen, John Dwyer, Angela Erb, Feng Gao, Patrick Griffiths, Dennis Helder, Txomin Hermosilla, James D. Hipple, Patrick Hostert, M. Joseph Hughes, Justin Huntington〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality.〈/p〉 〈p〉Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat.〈/p〉 〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Liujun Zhu, Jeffrey P. Walker, Nan Ye, Christoph Rüdiger〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Multi-temporal analysis has been widely acknowledged as a promising method to derive soil moisture from radar backscatter observations. The method assumes that only soil moisture varies in the period of interest, while all other parameters such as vegetation water content and soil surface roughness are sufficiently time invariant. However, this assumption is not easy to satisfy in agricultural areas where cultivation practices such as ploughing and irrigation are irregularly conducted between radar acquisitions. The paper has proposed an unsupervised change detection method to serve as a pre-processing procedure for multi-temporal retrieval. Briefly, the temporal ratio of HV and the temporal difference of HV/VV and VV polarizations were selected as the optimal feature space, using a genetic algorithm based feature selection algorithm and an extensive synthetic data set. The change map is determined from a two-step procedure with the first step producing multiple over-detected change maps for the period of interest using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. The second step merges the multiple change maps to remove the false alarms with a principle similar to the ensemble machine learning. Evaluation on a synthetic data set demonstrated that the proposed method can largely remove the error in multi-temporal soil moisture retrieval that is caused by abrupt roughness and vegetation changes. Evaluation on real radar data sets, including airborne L-band radar, RADARSAT-2 at C-band and COSMO SkyMed at X-band, demonstrated an accurate identification (〉0.9) while yielding a low false-alarm rate (〈0.1). These results suggest that the method may be used as a pre-processing stage of global soil moisture retrieval from radar satellite missions with a high revisit frequency, such as Sentinel-1 and SAOCOM-1.〈/p〉〈/div〉 〈/div〉

Description: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Remote Sensing of Environment, Volume 225〈/p〉 〈p〉Author(s): Matteo Nannini, Michele Martone, Paola Rizzoli, Pau Prats-Iraola, Marc Rodriguez-Cassola, Andreas Reigber, Alberto Moreira〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Future SAR missions will provide three-dimensional images of semi-transparent media, such as vegetation and ice, through SAR tomography. Access to information on the internal structure of these volume scatterers is a key factor for a better understanding of ecosystem dynamics and climate change. Because of this, several concepts are nowadays examined to implement SAR tomography in a spaceborne framework.〈/p〉 〈p〉In order to do that, it is necessary to gather different observations of the area of interest. Unfortunately, a consequence of the time that elapses between acquisitions is that the electromagnetic properties of the medium may vary. This implies that, there may be inconsistencies in the acquired data, leading to errors in the final inversion. A solution to partially cope with this temporal decorrelation, is to acquire data employing two or more sensors operating with a reduced (or even absent) temporal gap and then to collect several acquisitions at different time instants. By means of this imaging concept, the required line-of-sight diversity is granted and the desired resolution in the height direction ensured. In this way, sets of temporal decorrelation-free interferometric coherences can be built and the vertical scattering profile can be retrieved via coherence-based tomography.〈/p〉 〈p〉This contribution analyzes a two-sensor system like TanDEM-X (Krieger et al., 2007), Tandem-L (Moreira et al., 2015), or SAOCOM-CS (Davidson et al., 2014). In particular, the potential of coherence-based tomography are shown with data acquired with the TanDEM-X sensors for boreal and Amazon forest. In addition, a technique to partially cope with temporal decorrelation through covariance matrix filtering is also presented in the paper.〈/p〉 〈/div〉 〈/div〉