ALBERT

Description: Remote Sensing, Vol. 10, Pages 1171: Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations Remote Sensing doi: 10.3390/rs10081171 Authors: Jesús Revuelto Grégoire Lecourt Matthieu Lafaysse Isabella Zin Luc Charrois Vincent Vionnet Marie Dumont Antoine Rabatel Delphine Six Thomas Condom Samuel Morin Alessandra Viani Pascal Sirguey This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.

Description: Remote Sensing, Vol. 10, Pages 1169: Multi-Year Analyses of Columnar Aerosol Optical and Microphysical Properties in Xi’an, a Megacity in Northwestern China Remote Sensing doi: 10.3390/rs10081169 Authors: Xiaoli Su Junji Cao Zhengqiang Li Kaitao Li Hua Xu Suixin Liu Xuehua Fan A thorough understanding of aerosol optical properties and their spatio-temporal variability are required to accurately evaluate aerosol effects in the climate system. In this study, a multi-year study of aerosol optical and microphysical properties was firstly performed in Xi’an based on three years of sun photometer remote sensing measurements from 2012 to 2015. The multi-year average of aerosol optical depth (AOD) at 440 nm was about 0.88 ± 0.24 (mean ± SD), while the averaged Ångström Exponent (AE) between 440 and 870 nm was 1.02 ± 0.15. The mean value of single scattering albedo (SSA) was around 0.89 ± 0.03. Aerosol optical depth and AE showed different seasonal variation patterns. Aerosol optical depth was slightly higher in winter (0.99 ± 0.36) than in other seasons (~0.85 ± 0.20), while AE showed its minimum in spring (0.85 ± 0.05) due to the impact of dust episodes. The seasonal variations of volume particle size distribution, spectral refractive index, SSA, and asymmetry factor were also analyzed to characterize aerosols over this region. Based on the aerosol products derived from sun photometer measurements, the classification of aerosol types was also conducted using two different methods in this region. Results show that the dominant aerosol types are absorbers in all seasons, especially in winter, demonstrating the strong absorptivity of aerosols in Xi’an.

Description: Water, Vol. 10, Pages 969: Assessment of Runoff Components Simulated by GLDAS against UNH–GRDC Dataset at Global and Hemispheric Scales Water doi: 10.3390/w10080969 Authors: Meizhao Lv Hui Lu Kun Yang Zhongfeng Xu Meixia Lv Xiaomeng Huang The current evaluations of global land data assimilation system (GLDAS) runoff were generally limited to the observation-rich areas. At the global and hemispheric scales, we assessed different runoff components performance of GLDAS (1.0 and 2.1) using the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC) dataset. The results suggest that GLDAS simulations show considerable uncertainties, particularly in partition of surface and subsurface runoffs, in snowmelt runoff modeling, and in capturing the northern peak time. GLDAS1.0-CLM (common land model) produced more surface runoff almost globally; GLDAS-Noah generated more surface runoff over the northern middle-high latitudes and more subsurface runoff in the remaining areas; while the partition in GLDAS1.0-VIC (variable infiltration capacity) is almost opposite to that in Noah. Comparing to GLDAS1.0-Noah, GLDAS2.1-Noah improved the premature snow-melting tendency, but its snowmelt-runoff peak magnitude was excessively high in June and July. The discrepancies in northern primary peak times among precipitation and runoff is partly caused by the combination of rainfall and melting-snow over high-latitude, as well as the very different temporal–spatial distributions for snowmelt runoff simulated by GLDAS models. This paper can provide valuable guidance for GLDAS users, and contribute to the further improvement of hydrological parameterized schemes.

Description: Remote Sensing, Vol. 10, Pages 1166: Inventory of Glaciers in the Shaksgam Valley of the Chinese Karakoram Mountains, 1970–2014 Remote Sensing doi: 10.3390/rs10081166 Authors: Haireti Alifu Yukiko Hirabayashi Brian Alan Johnson Jean-Francois Vuillaume Akihiko Kondoh Minoru Urai The Shaksgam Valley, located on the north side of the Karakoram Mountains of western China, is situated in the transition zone between the Indian monsoon system and dry arid climate zones. Previous studies have reported abnormal behaviors of the glaciers in this region compared to the global trend of glacier retreat, so the region is of special interest for glacier-climatological studies. For this purpose, long-term monitoring of glaciers in this region is necessary to obtain a better understanding of the relationships between glacier changes and local climate variations. However, accurate historical and up-to-date glacier inventory data for the region are currently unavailable. For this reason, this study conducted glacier inventories for the years 1970, 1980, 1990, 2000 and 2014 (i.e., a ~10-year interval) using multi-temporal remote sensing imagery. The remote sensing data used included Corona KH-4A/B (1965–1971), Hexagon KH-9 (1980), Landsat Thematic Mapper (TM) (1990/1993), Landsat Enhanced Thematic Mapper Plus (ETM+) (2000/2001), and Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) (2014/2015) multispectral satellite images, as well as digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM), DEMs generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (2005–2014), and Advanced Land Observing Satellite (ALOS) World 3D 30 m mesh (AW3D30). In the year 2014, a total of 173 glaciers (including 121 debris-free glaciers) (>0.5 km2), covering an area of 1478 ± 34 km2 (area of debris-free glaciers: 295 ± 7 km2) were mapped. The multi-temporal glacier inventory results indicated that total glacier area change between 1970–2014 was not significant. However, individual glacier changes showed significant variability. Comparisons of the changes in glacier terminus position indicated that 55 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) between 1970–2014, and 74 (32 debris-covered) glaciers experienced significant advances (~40–1400 m) during the most recent period (2000–2014). Notably, small glaciers showed higher sensitivity to climate changes, and the glaciers located in the western part of the study site were exhibiting glacier area expansion compared to other parts of the Shaksgam Valley. Finally, regression analyses indicated that topographic parameters were not the main driver of glacier changes. On the contrary, local climate variability could explain the complex behavior of glaciers in this region.

Description: Water, Vol. 10, Pages 981: Scenario-Based Economic Impact Analysis for Bridge Closures Due to Flooding: A Case Study of North Gyeongsang Province, South Korea Water doi: 10.3390/w10080981 Authors: Byungil Kim Sha Chul Shin Du Yon Kim Flooding has the ability to severely reduce the capacity of a transportation network. The closure of even a single bridge, which often acts as a critical link in transportation networks, can have a severe impact on the entire network. This impact can lead to significant economic costs resulting from increased travel distances for drivers. Despite the significance of these costs, however, notably few studies have been conducted to determine the societal economic cost that would be incurred due to bridge closures. One possible reason for the lack of studies investigating bridge closures due to flooding could stem from the difficultly in collecting data. To address this issue, the methodology presented in this paper uses modeling and data resources that are available for major cities in most developed countries, including those in South Korea. We evaluate the economic impact of the bridge closures using the new administrative capital of North Gyeongsang Province, South Korea as a case study. Scenarios for the closure of bridges are derived from channel surveys and hydraulic analyses. These methods are used to overcome a lack of adequate data on historical floods in the new city. Traffic is forecasted to estimate the number of road users that would be forced to take detours due to inundated bridges. Contrasting travel distances when bridges are and are not operational, economic costs incurred by bridge closures due to flooding are estimated. The results indicated that bridge closures would result in an economic cost of 1563 USD to 44,180 USD per day, depending on how many bridges are closed and how many people are living in the new city. The estimates from this study will act as guidelines for identifying cost-effective mitigation and preparedness strategies aimed at reducing the frequency and impact of bridge closures due to flooding.

Description: Water, Vol. 10, Pages 972: Microbial Function and Hydrochemistry within a Stratified Anchialine Sinkhole: A Window into Coastal Aquifer Interactions Water doi: 10.3390/w10080972 Authors: Madison C. Davis James R. Garey Anchialine sinkholes provide insight into coastal aquifer systems and coastal mixing processes. Aquifer microbial community function is usually inferred from hydrochemical information, but there are few direct studies of microbial communities in the Floridan Aquifer. Hospital Hole is a 43 m-deep stratified sinkhole under the Weeki Wachee River, FL, with three distinct brackish layers: a hypoxic layer, a chemocline and a sulfidic anoxic layer. Illumina sequencing and bioinformatic tools were used to reconstruct metabolic functions and interactions of microbial communities in each layer. Each layer appears to originate from different parts of the coastal mixing zone and has a distinct microbial community with unique functions, which are influenced by the respective hydrochemistry. Sulfide oxidation and nitrate reduction are the most abundant functions. Syntrophy between methane oxidizers, methanogens and sulfate reducers is present. Similarities between the hydrochemistry and potential connectivity of Hospital Hole and the Floridan Aquifer coastal mixing zone suggest that microbial communities of Hospital Hole could be a surrogate for the coastal mixing zone of the aquifer in the absence of direct studies. Understanding how groundwater microbial communities react to saltwater intrusion and nutrient flux will be useful in predicting how coastal aquifer regions might react to anthropogenic change.

Description: Remote Sensing, Vol. 10, Pages 1181: Improved Albedo Estimates Implemented in the METRIC Model for Modeling Energy Balance Fluxes and Evapotranspiration over Agricultural and Natural Areas in the Brazilian Cerrado Remote Sensing doi: 10.3390/rs10081181 Authors: Bruno Silva Oliveira Elisabete Caria Moraes Marcos Carrasco-Benavides Gabriel Bertani Guilherme Augusto Verola Mataveli In this study we assessed METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model performance to estimate energy balance fluxes and evapotranspiration (ET) in two heterogeneous landscapes in the Brazilian Cerrado, including fluxes and ET in both agricultural and natural vegetation. The estimates were evaluated by comparing them to flux tower data collected over sugarcane (USR site), woody savanna (PDG site) and stricto-sensu savanna (RECOR site) areas. The selection of the study years (2005–2007 for USR/PDG sites and 2011–2015 for RECOR site) was based on the availability of meteorological data (to be used as inputs in METRIC) and of flux tower data for energy balance fluxes and ET comparisons. The broadband albedo submodel was adjusted in order to improve Net Radiation estimates. For this adjustment, we applied at-surface solar radiation simulations obtained from the SMARTS2 model under different conditions of land elevation, precipitable water content and solar angles. We also tested the equivalence between the measured crop coefficient (Kc_ec) and the reference evapotranspiration fraction (ETrF or F), seeking to extrapolate from instantaneous to daily values of actual evapotranspiration (ETa). Surface albedo was underestimated by 10% at the USR site (showing a better performance for full crop coverage), by 15% at the PDG site (following the woody savanna dynamics pattern through dry and wet seasons) and was overestimated by 21% at the RECOR site. METRIC was effective in simulating the spatial and temporal variability of energy balance fluxes and ET over agricultural and natural vegetation in the Brazilian Cerrado, with errors within those reported in the literature. Net radiation (Rn) presented consistent results (coefficient of determination (R2) > 0.94) but it was overestimated by 8% and 9% in sugarcane and woody savanna, respectively. METRIC-derived ET estimates showed an agreement with ground data at USR and PDG sites (R2 > 0.88, root mean square error (RMSE) up to 0.87 mm day−1), but at the RECOR site, ET was overestimated by 14% (R2 = 0.96, mean absolute error (MAE) = 0.62 mm.day−1 and RMSE = 0.75 mm day−1). Surface energy balance fluxes and ET were marked by seasonality, with direct dependence on available energy, rainfall distribution, soil moisture and other parameters like albedo and NDVI.

Description: Remote Sensing, Vol. 10, Pages 1182: Detection of Frozen Soil Using Sentinel-1 SAR Data Remote Sensing doi: 10.3390/rs10081182 Authors: Nicolas Baghdadi Hassan Bazzi Mohammad El Hajj Mehrez Zribi The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar “SAR” data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and −5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.

Description: Water, Vol. 10, Pages 989: Computational Study of a Vertical Plunging Jet into Still Water Water doi: 10.3390/w10080989 Authors: Zegao Yin Qianqian Jia Yuan Li Yanxu Wang Dejun Yang The behavior of a vertical plunging jet was numerically investigated using the coupled Level Set and Volume of Fluid method. The computational results were in good agreement with the experimental results reported in the related literature. Vertical plunging jet characteristics, including the liquid velocity field, air void fraction, and turbulence kinetic energy, were explored by varying the distance between the nozzle exit and the still water level. It was found that the velocity at the nozzle exit plays an unimportant role in the shape and size of ascending bubbles. A modified prediction equation between the centerline velocity ratio and the axial distance ratio was developed using the data of the coupled Level Set and Volume of Fluid method, and it showed a better predicting ability than the Level Set and Mixture methods. The characteristics of turbulence kinetic energy, including its maximum value location and its radial and vertical distribution, were also compared with that of submerged jets.

Description: Water, Vol. 10, Pages 984: Vulnerability Analysis of the Venetian Littoral and Adopted Mitigation Strategy Water doi: 10.3390/w10080984 Authors: Piero Ruol Luca Martinelli Chiara Favaretto This paper discusses the key aspects of the recent Coastal Plan of the Veneto Region (IT). Its aim is to propose a single mitigation strategy for coastal erosion that is valid for the whole Veneto Region, and possibly elsewhere, as well as a method to assign a priority level to any action. The suggested mitigation action against erosion depends on urbanization level, beach width, as well as cross-shore and long-shore sediment transport. The criterion used to give a priority level to mitigation actions is based on a vulnerability index that takes into account erosive tendency, existing coastal flooding hazards, coast value, environmental relevance, tourist pressure, urbanization level, the presence of production activities, and cultural heritage. A sample case featuring the littoral of Rosolina is also provided and includes a site description, the sediment budget, critical issues and possible mitigation measures.

Description: Water, Vol. 10, Pages 982: Multivariate Chemometric Analysis of Membrane Fouling Patterns in Biofilm Ceramic Membrane Bioreactor Water doi: 10.3390/w10080982 Authors: Olga Kulesha Zakhar Maletskyi Harsha Ratnaweera Membrane fouling highly limits the development of Membrane bioreactor technology (MBR), which is among the key solutions to water scarcity. The current study deals with the determination of the fouling propensity of filtered biomass in a pilot-scale biofilm membrane bioreactor to enable the prediction of fouling intensity. The system was designed to treat domestic wastewater with the application of ceramic microfiltration membranes. Partial least squares regression analysis of the data obtained during the long-term operation of the biofilm-MBR (BF-MBR) system demonstrated that Mixed liquor suspended solids (MLSS), diluted sludge volume index (DSVI), chemical oxygen demand (COD), and their slopes are the most significant for the estimation and prediction of fouling intensity, while normalized permeability and its slope were found to be the most reliable fouling indicators. Three models were derived depending on the applied operating conditions, which enabled an accurate prediction of the fouling intensities in the system. The results will help to prevent severe membrane fouling via the change of operating conditions to prolong the effective lifetime of the membrane modules and to save energy and resources for the maintenance of the system.

Description: Water, Vol. 10, Pages 983: The Mechanical Properties of High Strength Reinforced Cured-in-Place Pipe (CIPP) Liner Composites for Urban Water Infrastructure Rehabilitation Water doi: 10.3390/w10080983 Authors: Hyun Wook Ji Sung Soo Yoo Jonghoon Kim Dan Daehyun Koo Most urban areas in the world have water infrastructure systems, including the buried sewer and water pipelines, which are assessed as in need of extensive rehabilitation. Deterioration by many other factors affects structural integrity. Trenchless technologies such as Cured-in-Place Pipe (CIPP) are now applied in numerous projects while minimizing disturbance in an urban environment. The main purpose of this study is to develop a high strength CIPP material using various composite materials (e.g., glass fiber, carbon fiber, polyester felt, unsaturated polyester resin, and others). Composite samples were made of the materials and tested using three-point bend apparatus to find mechanical properties, which include the flexural modulus, strength, and deflection. A composite combination with glass fibers with thin felt layers shows the best results in mechanical properties. Flexural modulus is a key factor for CIPP liner thickness design. Glass fiber composite yields between four and nine times higher values than the minimum value specified in the American Society for Testing and Materials (ASTM) F1216. This study provides a fundamental baseline for high strength CIPP liners that are capable of using conventional curing technologies.

Description: Remote Sensing, Vol. 10, Pages 1186: Application of Multi-Sensor Satellite Data for Exploration of Zn–Pb Sulfide Mineralization in the Franklinian Basin, North Greenland Remote Sensing doi: 10.3390/rs10081186 Authors: Amin Beiranvand Pour Tae-Yoon S. Park Yongcheol Park Jong Kuk Hong Basem Zoheir Biswajeet Pradhan Iman Ayoobi Mazlan Hashim Geological mapping and mineral exploration programs in the High Arctic have been naturally hindered by its remoteness and hostile climate conditions. The Franklinian Basin in North Greenland has a unique potential for exploration of world-class zinc deposits. In this research, multi-sensor remote sensing satellite data (e.g., Landsat-8, Phased Array L-band Synthetic Aperture Radar (PALSAR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) were used for exploring zinc in the trough sequences and shelf-platform carbonate of the Franklinian Basin. A series of robust image processing algorithms was implemented for detecting spatial distribution of pixels/sub-pixels related to key alteration mineral assemblages and structural features that may represent potential undiscovered Zn–Pb deposits. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) was applied to some selected Landsat-8 mineral indices for mapping gossan, clay-rich zones and dolomitization. Major lineaments, intersections, curvilinear structures and sedimentary formations were traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. Mixture Tuned Matched Filtering (MTMF) algorithm was applied to ASTER VNIR/SWIR bands for sub-pixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, calcite and dolomite in the prospective targets. Using the remote sensing data and approaches, several high potential zones characterized by distinct alteration mineral assemblages and structural fabrics were identified that could represent undiscovered Zn–Pb sulfide deposits in the study area. This research establishes a straightforward/cost-effective multi-sensor satellite-based remote sensing approach for reconnaissance stages of mineral exploration in hardly accessible parts of the High Arctic environments.

Description: Remote Sensing, Vol. 10, Pages 1184: An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States Remote Sensing doi: 10.3390/rs10081184 Authors: Ian W. Housman Robert A. Chastain Mark V. Finco The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

Description: Water, Vol. 10, Pages 993: Understanding Fundamental Phenomena Affecting the Water Conservation Technology Adoption of Residential Consumers Using Agent-Based Modeling Water doi: 10.3390/w10080993 Authors: Kambiz Rasoulkhani Brianne Logasa Maria Presa Reyes Ali Mostafavi More than one billion people will face water scarcity within the next ten years due to climate change and unsustainable water usage, and this number is only expected to grow exponentially in the future. At current water use rates, supply-side demand management is no longer an effective way to combat water scarcity. Instead, many municipalities and water agencies are looking to demand-side solutions to prevent major water loss. While changing conservation behavior is one demand-based strategy, there is a growing movement toward the adoption of water conservation technology as a way to solve water resource depletion. Installing technology into one’s household requires additional costs and motivation, creating a gap between the overall potential households that could adopt this technology, and how many actually do. This study identified and modeled a variety of demographic and household characteristics, social network influence, and external factors such as water price and rebate policy to see their effect on residential water conservation technology adoption. Using Agent-based Modeling and data obtained from the City of Miami Beach, the coupled effects of these factors were evaluated to examine the effectiveness of different pathways towards the adoption of more water conservation technologies. The results showed that income growth and water pricing structure, more so than any of the demographic or building characteristics, impacted household adoption of water conservation technologies. The results also revealed that the effectiveness of rebate programs depends on conservation technology cost and the affluence of the community. Rebate allocation did influence expensive technology adoption, with the potential to increase the adoption rate by 50%. Additionally, social network connections were shown to have an impact on the rate of adoption independent of price strategy or rebate status. These findings will lead the way for municipalities and other water agencies to more strategically implement interventions to encourage household technology adoption based on the characteristics of their communities.

Description: Water, Vol. 10, Pages 994: Predicting Lake Eutrophication Responses to Multiple Scenarios of Lake Restoration: A Three-Dimensional Modeling Approach Water doi: 10.3390/w10080994 Authors: Yanping Wang Weiping Hu Zhaoliang Peng Ye Zeng Karsten Rinke To improve the water quality and alleviate the eutrophication of Lake Yangchenghu, the third largest freshwater body within the Lake Taihu basin in China and an important source of drinking water, nutrient reduction strategies should be urgently addressed by decision makers, since virtually no improvement of water quality has taken place since the mid-1990s. Due to the lack of sufficient observation data and simulation results, a vertically compressed three-dimensional numerical model, the EcoTaihu model, was used to study the impact of three restoration measures on the water quality—namely, total nitrogen (TN), total phosphorus (TP) and biomass of phytoplankton (BP)—of Lake Yangchenghu: (i) total nutrient reduction, (ii) intensification of flushing by water transfer, and (iii) spatial adjustment of inflow channels. In particular, the spatial effects of the three restoration measures on the water quality were investigated. The results showed that the EcoTaihu model is applicable to other shallow lakes in China. The water quality responses to the different restoration scenarios showed significant spatio-temporal differences. The reduction of nutrient loads from inflows appeared to be the most effective measure for controlling the eutrophication and algal blooms in Lake Yangchenghu. The effectiveness of water transfer on the improvement of water quality for TN and TP was more influenced by the differences of nutrient concentrations between the transferred water and lake water, rather than flow rate, since no proportionate increase of improvement was observable in the case of larger transferred rates (60 m3 s−1). The spatial narrowing of inflowing rivers in the southwestern lake could preferentially improve the water quality in the southern bay of the western lake, but would also result in a deterioration trend of water quality in the total lake and drinking water abstraction areas.

Description: Remote Sensing, Vol. 10, Pages 1196: Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China Remote Sensing doi: 10.3390/rs10081196 Authors: Davide Fornacca Guopeng Ren Wen Xiao Remote mountainous regions are among the Earth’s last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

Description: Remote Sensing, Vol. 10, Pages 1192: Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps Remote Sensing doi: 10.3390/rs10081192 Authors: Chen-Chieh Feng Zhou Guo The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

Description: Remote Sensing, Vol. 10, Pages 1197: Quantifying Drought Propagation from Soil Moisture to Vegetation Dynamics Using a Newly Developed Ecohydrological Land Reanalysis Remote Sensing doi: 10.3390/rs10081197 Authors: Yohei Sawada Despite the importance of the interaction between soil moisture and vegetation dynamics to understand the complex nature of drought, few land reanalyses explicitly simulate vegetation growth and senescence. In this study, I provide a new land reanalysis which explicitly simulates the interaction between sub-surface soil moisture and vegetation dynamics by the sequential assimilation of satellite microwave brightness temperature observations into a land surface model (LSM). Assimilating satellite microwave brightness temperature observations improves the skill of a LSM to simultaneously simulate soil moisture and the seasonal cycle of leaf area index (LAI). By analyzing soil moisture and LAI simulated by this new land reanalysis, I identify the drought events which significantly damage LAI on the climatological day-of-year of the LAI’s seasonal peak and quantify drought propagation from soil moisture to LAI in the global snow-free region. On average, soil moisture in the shallow soil layers (0–0.45 m) quickly recovers from the drought condition before the climatological day-of-year of the LAI’s seasonal peak while soil moisture in the deeper soil layer (1.05–2.05 m) and LAI recover from the drought condition approximately 100 days after the climatological day-of-year of the LAI’s seasonal peak.

Description: Water, Vol. 10, Pages 1008: Inherent Relationship between Flow Duration Curves at Different Time Scales: A Perspective on Monthly Flow Data Utilization in Daily Flow Duration Curve Estimation Water doi: 10.3390/w10081008 Authors: Lei Ye Wei Ding Xiaofan Zeng Zhuohang Xin Jian Wu Chi Zhang Modelling flow duration curves (FDCs) has long been a topic of interest since it is widely used in various hydrological applications. Most studies related to the estimation of FDCs in ungauged or partial gauged basins focus primarily on using climate and catchment characteristics to regionalize FDC at some single time scale. However, the relationship of FDCs at various time scales are rarely analyzed or studied. Here, we propose two methods, which are Modelled FDC Parameter comparison (M-FDC-P) and Empirical FDC Ratio comparison (E-FDC-R), to study the quantitative relationship between daily and monthly FDCs. One method M-FDC-P, selects a Kappa (KAP) distribution to represent the characteristics of the FDCs and then analyzes the relationship between KAP parameters of modelled FDCs at different time scales. Results indicate that three out of four parameters have strong correlations between FDCs at daily and monthly time scales. The other method, E-FDC-R, compares the quantitative relationship between daily and monthly empirical FDCs with given exceedance probabilities. The Power function is used for fitting the ratio-exceedance probability curves. In addition, the simulated daily FDC derived from monthly FDC can be very consistent with the observed daily flow records when the two parameters of power function are quantified precisely. These results clearly indicate that there are strong connections between daily and monthly FDCs, and monthly FDC can provide valuable information for daily FDC estimation. Since flow records at a large time scale are easier to obtain, daily FDC could be derived from monthly FDC by considering the inherent relationships between FDCs at different time scales, which is not sufficiently realized in previous studies.

Description: Water, Vol. 10, Pages 1006: Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China Water doi: 10.3390/w10081006 Authors: Xiuna Wang Yongjian Ding Chuancheng Zhao Jian Wang Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

Description: Remote Sensing, Vol. 10, Pages 1202: Potential of Photochemical Reflectance Index for Indicating Photochemistry and Light Use Efficiency in Leaves of European Beech and Norway Spruce Trees Remote Sensing doi: 10.3390/rs10081202 Authors: Daniel Kováč Petra Veselovská Karel Klem Kristýna Večeřová Alexander Ač Josep Peñuelas Otmar Urban Hyperspectral reflectance is becoming more frequently used for measuring the functions and productivity of ecosystems. The purpose of this study was to re-evaluate the potential of the photochemical reflectance index (PRI) for evaluating physiological status of plants. This is needed because the reasons for variation in PRI and its relationships to physiological traits remain poorly understood. We examined the relationships between PRI and photosynthetic parameters in evergreen Norway spruce and deciduous European beech grown in controlled conditions during several consecutive periods of 10–12 days between which the irradiance and air temperature were changed stepwise. These regime changes induced significant changes in foliar biochemistry and physiology. The responses of PRI corresponded particularly to alterations in the actual quantum yield of photosystem II photochemistry (ΦPSII). Acclimation responses of both species led to loss of PRI sensitivity to light use efficiency (LUE). The procedure of measuring PRI at multiple irradiance-temperature conditions has been designed also for testing accuracy of ΔPRI in estimating LUE. A correction mechanism of subtracting daily measured PRI from early morning PRI has been performed to account for differences in photosynthetic pigments between irradiance-temperature regimes. Introducing ΔPRI, which provided a better estimate of non-photochemical quenching (NPQ) compared to PRI, also improved the accuracy of LUE estimation. Furthermore, ΔPRI was able to detect the effect of drought, which is poorly observable from PRI.

Description: Remote Sensing, Vol. 10, Pages 1200: Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images Remote Sensing doi: 10.3390/rs10081200 Authors: Xin Zhang Bingfang Wu Guillermo E. Ponce-Campos Miao Zhang Sheng Chang Fuyou Tian Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

Description: Water, Vol. 10, Pages 1025: Monitoring the Chloride Concentration in International Scheldt River Basin District Water Using a Low-Cost Multifunction Data Acquisition Board Water doi: 10.3390/w10081025 Authors: Wanda J. Guedens Monique Reynders Koen Van Vinckenroye Jan Yperman Robert Carleer In analytical chemistry laboratories, to gather in the shortest time as many data as possible with the utmost accuracy and precision, high throughput automated setups are indispensable. In the present study, to determine the chloride concentration in the international Scheldt river basin district, experiments are carried out utilizing a thermostatically controlled semi-automated setup. A novel ICT-based method is developed using a low-cost multifunction Data Acquisition Board (DAQ) controlled by a homebuilt LabVIEW™ program. Specifically, this approach enables a correlation between different parameters i.e., droplet volume, temperature, A/D voltage conversions. Here, processing experimental data of a potentiometric precipitation titration utilizing a silver nitrate standard solution as titrant in a manual burette equipped with a controllable electronic valve allows for a preliminary indication of the titration end point via the Virtual Instrument (VI) numerical first derivative tool in the LabVIEW software. The LabVIEW tool is compared with the well-known Gran method implemented in the LabVIEW program, emphasizing an accurate performance of the setup to determine the chloride concentration in fresh river water. We are confident that our findings are evidence of the versatile and powerful features of the LabVIEW controlled DAQ in the analytical chemistry laboratory.

Description: Remote Sensing, Vol. 10, Pages 1211: Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China Remote Sensing doi: 10.3390/rs10081211 Authors: Chao Zhou Shengbo Chen Yuanzhi Zhang Jianhua Zhao Derui Song Dawei Liu This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

Description: Remote Sensing, Vol. 10, Pages 1234: HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images Remote Sensing doi: 10.3390/rs10081234 Authors: Zhitao Fu Qianqing Qin Bin Luo Hong Sun Chun Wu Local region description of multi-sensor images remains a challenging task in remote sensing image analysis and applications due to the non-linear radiation variations between images. This paper presents a novel descriptor based on the combination of the magnitude and phase congruency information of local regions to capture the common features of images with non-linear radiation changes. We first propose oriented phase congruency maps (PCMs) and oriented magnitude binary maps (MBMs) using the multi-oriented phase congruency and magnitude information of log-Gabor filters. The two feature vectors are then quickly constructed based on the convolved PCMs and MBMs. Finally, a dense descriptor named the histograms of oriented magnitude and phase congruency (HOMPC) is developed by combining the histograms of oriented phase congruency (HPC) and the histograms of oriented magnitude (HOM) to capture the structure and shape properties of local regions. HOMPC was evaluated with three datasets composed of multi-sensor remote sensing images obtained from unmanned ground vehicle, unmanned aerial vehicle, and satellite platforms. The descriptor performance was evaluated by recall, precision, F1-measure, and area under the precision-recall curve. The experimental results showed the advantages of the HOM and HPC combination and confirmed that HOMPC is far superior to the current state-of-the-art local feature descriptors.

Description: Remote Sensing, Vol. 10, Pages 1236: Progressive Degradation of an Ice Rumple in the Thwaites Ice Shelf, Antarctica, as Observed from High-Resolution Digital Elevation Models Remote Sensing doi: 10.3390/rs10081236 Authors: Seung Hee Kim Duk-jin Kim Hyun-Cheol Kim Ice rumples are locally-grounded features of flowing ice shelves, elevated tens of meters above the surrounding surface. These features may significantly impact the dynamics of ice-shelf grounding lines, which are strongly related to shelf stability. In this study, we used TanDEM-X data to construct high-resolution DEMs of the Thwaites ice shelf in West Antarctica from 2011 to 2013. We also generated surface deformation maps which allowed us to detect and monitor the elevation changes of an ice rumple that appeared sometime between the observations of a grounding line of the Thwaites glacier using Double-Differential Interferometric SAR (DDInSAR) in 1996 and 2011. The observed degradation of the ice rumple during 2011–2013 may be related to a loss of contact with the underlying bathymetry caused by the thinning of the ice shelf. We subsequently used a viscoelastic deformation model with a finite spherical pressure source to reproduce the surface expression of the ice rumple. Global optimization allowed us to fit the model to the observed deformation map, producing reasonable estimates of the ice thickness at the center of the pressure source. Our conclusion is that combining the use of multiple high-resolution DEMs and the simple viscoelastic deformation model is feasible for observing and understanding the transient nature of small ice rumples, with implications for monitoring ice shelf stability.

Description: Remote Sensing, Vol. 10, Pages 1232: Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics Remote Sensing doi: 10.3390/rs10081232 Authors: Semyon A. Grodsky Douglas Vandemark Hui Feng Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.

Description: Remote Sensing, Vol. 10, Pages 1230: Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment Remote Sensing doi: 10.3390/rs10081230 Authors: Guy J-P. Schumann G. Robert Brakenridge Albert J. Kettner Rashid Kashif Emily Niebuhr Floods are among the top-ranking natural disasters in terms of annual cost in insured and uninsured losses. Since high-impact events often cover spatial scales that are beyond traditional regional monitoring operations, remote sensing, in particular from satellites, presents an attractive approach. Since the 1970s, there have been many studies in the scientific literature about mapping and monitoring of floods using data from various sensors onboard different satellites. The field has now matured and hence there is a general consensus among space agencies, numerous organizations, scientists, and end-users to strengthen the support that satellite missions can offer, particularly in assisting flood disaster response activities. This has stimulated more research in this area, and significant progress has been achieved in recent years in fostering our understanding of the ways in which remote sensing can support flood monitoring and assist emergency response activities. This paper reviews the products and services that currently exist to deliver actionable information about an ongoing flood disaster to emergency response operations. It also critically discusses requirements, challenges and perspectives for improving operational assistance during flood disaster using satellite remote sensing products.

Description: Water, Vol. 10, Pages 1040: Storm Water Management and Flood Control in Sponge City Construction of Beijing Water doi: 10.3390/w10081040 Authors: Shuhan Zhang Yongkun Li Meihong Ma Ting Song Ruining Song To solve the problems of increasing local flooding, water shortage, and water pollution caused by the traditional model of urban development, the Chinese government proposed a new model of urban development—the Sponge City. In Beijing, the capital of China, research on storm water management in urban areas has been carried out since 1989 and has put forward the concept of urban storm water harvesting and flood control. The further research and demonstration application started in 2000. So far, a series of policies and technology standards on storm water management have been formulated, which promote the application of technologies on comprehensive urban storm water harvesting and flood control. A significant number of storm water harvesting and flood control projects have been built in Beijing, which are now playing important roles in runoff reduction, local flood control, non-point source pollution reduction, and storm water utilization. However, it does not solve the above problem completely. Storm water management and flood control needs to be further strengthened. The “Sponge City” is based on natural and ecological laws, which allows storm water to be managed with natural infiltration, natural retention and detention, and natural cleaning facilities. Through in-depth analysis of the connotation, characteristics, and construction path of “Sponge City”, this paper summarizes the status quo of urban rainwater flooding, flood control technology development and application, and Beijing policy and engineering to introduce the overall ideas and methods of Sponge City construction. All the above will provide a reference for cities with similar problems in the construction of sponge cities.

Description: Remote Sensing, Vol. 10, Pages 1228: Generic and Automatic Markov Random Field-Based Registration for Multimodal Remote Sensing Image Using Grayscale and Gradient Information Remote Sensing doi: 10.3390/rs10081228 Authors: Li Yan Ziqi Wang Yi Liu Zhiyun Ye The automatic image registration serves as a technical prerequisite for multimodal remote sensing image fusion. Meanwhile, it is also the technical basis for change detection, image stitching and target recognition. The demands of subpixel level registration accuracy can be rarely satisfied with a multimodal image registration method based on feature matching. In light of this, we propose a Generic and automatic Markov Random Field (MRF)-based registration framework of multimodal image using grayscale and gradient information. The proposed approach performs non-rigid registration and formulates an MRF model while grayscale and gradient statistical information of a multimodal image is employed for the evaluation of similarity while the spatial weighting function is optimized simultaneously. Besides, the value space is discretized to improve the convergence speed. The developed automatic approach was validated both qualitatively and quantitatively, demonstrating its potential for a variety of multimodal remote sensing datasets and scenes. As for the registration accuracy, the average target registration error of the proposed framework is less than 1 pixel, while the maximum displacement error is less than 1 pixel. Compared with the polynomial model registration based on manual selection, the registration accuracy has been significantly improved. In the meantime, the proposed approach had the partial applicability for the multimodal image registration of large deformation scenes. It is also proved that the proposed registration framework using grayscale and gradient information outperforms the MRF-based registration using only grayscale information and only gradient information while the proposed registration framework using Gaussian function as spatial weighting function is superior to that using distance inverse weight method.

Description: Water, Vol. 10, Pages 1037: Multivariate and Spatial Analysis of Physicochemical Parameters in an Irrigation District, Chihuahua, Mexico Water doi: 10.3390/w10081037 Authors: Jesús Alejandro Prieto-Amparán Beatriz Adriana Rocha-Gutiérrez María de Lourdes Ballinas-Casarrubias María Cecilia Valles-Aragón María del Rosario Peralta-Pérez Alfredo Pinedo-Alvarez Water quality is relevant due to the complexity of the interaction of physicochemical and biological parameters. The Irrigation District 005 (ID005) is one of the most important agricultural region in Chihuahua, México; for that reason, it was proposed to investigate the water quality of the site. Water samples were collected in two periods: Summer (S1) and Fall (S2). The samples were taken from 65 wells in S1, and 54 wells in S2. Physicochemical parameters (PhP) such as Arsenic (As), Temperature, Electrical Conductivity (EC), Oxide Reduction Potential (ORP), Hardness, pH, Total Dissolved Solids (TDS), and Turbidity were analyzed. The data were subjected to statistical principal component analysis (PCA), cluster analysis (CA) and spatial variability tests. In both seasons, the TDS exceeded the Mexican maximum permissible level (MPL) (35% S1, 39% S2). Turbidity exceeded the MPL in S1 (29%) and in S2 (12%). Arsenic was above the MPL for water of agricultural use in 9% (S1) and 13% (S2) of the wells. The PCA results suggested that most variations in water quality in S1 were due to As, pH and Temperature, followed by EC, TDS and Hardness; while in S2 to EC, TDS and Hardness, followed by As and pH.

Description: Water, Vol. 10, Pages 1036: Hydraulic Conductivity Characteristics of Desert Plant Organs: Coping with Drought Tolerance Strategy Water doi: 10.3390/w10081036 Authors: Shanjia Li Peixi Su Haina Zhang Zijuan Zhou Rui Shi Wei Gou Plant hydraulic conductivity (K) refers to the rate of water flow (kg s−1) per unit pressure drop (MPa), which drives flow through the plant organ system. It is an important eco-physiology index for measuring plant water absorption and transport capacity. A field study was conducted in the arid region of the Heihe River Basin in northwestern China, plant hydraulic conductivity was measured by high-pressure flowmeter (HPFM) to investigate the characteristics of hydraulic conductivity of typical dominant desert plants (Reaumuria soongarica M., Nitraria sphaerocarpa M., and Sympegma regelii B.) and their relationship with functional traits of leaves, stems, and roots, and explaining their adaptation strategies to desert environment from the perspective of plant organs hydraulic conductivity. The results showed that the hydraulic conductivity of the leaves and stems of R. soongarica and N. sphaerocarpa (KLA, leaf hydraulic conductivity per unit leaf area; KLW, leaf hydraulic conductivity per unit leaf weight; KSLA, stem hydraulic conductivity per unit leaf area; KSLW, stem hydraulic conductivity per unit leaf weight) were significantly lower than those of S. regelii, while their fine root (KRL, root hydraulic conductivity per unit leaf length; KRSA, root hydraulic conductivity per unit root surface area) and whole root (KTRW, whole root hydraulic conductivity per unit root weight) of hydraulic conductivity were significantly higher than those of S. regelii. In addition, KLA and KLW, KSLA and KSLW, and KRL and KRSA in three desert plants all exhibited consistent trends. Correlation analysis illustrated that the hydraulic conductivity of leaves and stems had a significantly positive correlation, but they had no significant negative correlation with the specific leaf weight (SLW, specific leaf weight). The hydraulic conductivity of fine root weight (KRW, root hydraulic conductivity per unit root weight) and specific root surface area (SRSA, specific root surface area) showed significantly positive correlation (r = 0.727, P < 0.05). The results demonstrated that the R. soongarica and N. sphaerocarpa preserved their water content through the strong leaf absorption capacity of soil water and the low water dispersion rates of leaves to adapt to the harsher arid habitat, which is more drought tolerant than S. regelii.

Description: Water, Vol. 10, Pages 1038: Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year Water doi: 10.3390/w10081038 Authors: Fernando Delgado-Ramos Carmen Hervás-Gámez Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasting monthly and yearly streamflows during the current hydrological year, which is applicable to headwater catchments. The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model performance statistics metrics (including the Coefficient of Determination R2; the Root-Mean-Square Error RMSE; the Mean Absolute Error MAE; the Index of Agreement IOA; the Mean Absolute Percent Error MAPE; the Coefficient of Nash-Sutcliffe Efficiency NSE; and the Inclusion Coefficient IC) were used and the results showed good levels of accuracy (improving as the number of observed months increases). The model forecast outputs are the mean monthly and yearly streamflows along with the 10th and 90th percentiles. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain, achieving an accuracy of 92% and 80% in March 2017. These risk-based predictions are of great value, especially before the intensive irrigation campaign starts in the middle of the hydrological year, when Water Authorities have to ensure that the right decision is made on how to best allocate the available water volume between the different water users and environmental needs.

Description: Remote Sensing, Vol. 10, Pages 1242: Ocean Wave Measurement Using Short-Range K-Band Narrow Beam Continuous Wave Radar Remote Sensing doi: 10.3390/rs10081242 Authors: Jian Cui Ralf Bachmayer Brad deYoung Weimin Huang We describe a technique to measure ocean wave period, height and direction. The technique is based on the characteristics of transmission and backscattering of short-range K-band narrow beam continuous wave radar at the sea surface. The short-range K-band radar transmits and receives continuous signals close to the sea surface at a low-grazing angle. By sensing the motions of a dominant facet at the sea surface that strongly scatters signals back and is located directly in front of the radar, the wave orbital velocity can be measured from the Doppler shift of the received radar signal. The period, height and direction of ocean wave are determined from the relationships among wave orbital velocity, ocean wave characteristics and the Doppler shift. Numerical simulations were performed to validate that the dominant facet exists and ocean waves are measured by sensing its motion. Validation experiments were conducted in a wave tank to verify the feasibility of the proposed ocean wave measurement method. The results of simulations and experiments demonstrate the effectiveness of the short-range K-band narrow beam continuous wave radar for the measurement of ocean waves.

Description: Remote Sensing, Vol. 10, Pages 1241: Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach Remote Sensing doi: 10.3390/rs10081241 Authors: Qingzhi Zhao Yibin Yao Wanqiang Yao Global Navigation Satellite System (GNSS) troposphere tomography has become one of the most cost-effective means to obtain three-dimensional (3-d) image of the tropospheric water vapour field. Traditional methods divide the tomography area into a number of 3-d voxels and assume that the water vapour density at any voxel is a constant during the given period. However, such behaviour breaks the spatial continuity of water vapour density in a horizontal direction and the number of unknown parameters needing to be estimated is very large. This is the focus of the paper, which tries to reconstruct the water vapor field using the tomographic technique without imposing empirical horizontal and vertical constraints. The proposed approach introduces the layered functional model in each layer vertically and only an a priori constraint is imposed for the water vapor information at the location of the radiosonde station. The elevation angle mask of 30° is determined according to the distribution of intersections between the satellite rays and different layers, which avoids the impact of ray bending and the error in slant water vapor (SWV) at low elevation angles on the tomographic result. Additionally, an optimal weighting strategy is applied to the established tomographic model to obtain a reasonable result. The tomographic experiment is performed using Global Positioning System (GPS) data of 12 receivers derived from the Satellite Positioning Reference Station Network (SatRef) in Hong Kong. The quality of the established tomographic model is validated under different weather conditions and compared with the conventional tomography method using 31-day data, respectively. The numerical result shows that the proposed method is applicable and superior to the traditional one. Comparisons of integrated water vapour (IWV) of the proposed method with that derived from radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data show that the root mean square (RMS)/Bias of their differences are 3.2/−0.8 mm and 3.3/−1.7 mm, respectively, while the values of traditional method are 5.1/−3.9 mm and 6.3/−5.9 mm, respectively. Furthermore, the water vapour density profiles are also compared with radiosonde and ECMWF data, and the values of RMS/Bias error for the proposed method are 0.88/0.06 g/m3 and 0.92/−0.08 g/m3, respectively, while the values of the traditional method are 1.33/0.38 g/m3 and 1.59/0.40 g/m3, respectively.

Description: Remote Sensing, Vol. 10, Pages 1237: Detection of Methane Plumes Using Airborne Midwave Infrared (3–5 µm) Hyperspectral Data Remote Sensing doi: 10.3390/rs10081237 Authors: Rebecca Del’ Papa Moreira Scafutto Carlos Roberto de Souza Filho Methane (CH4) display spectral features in several regions of the infrared range (0.75–14 µm), which can be used for the remote mapping of emission sources through the detection of CH4 plumes from natural seeps and leaks. Applications of hyperspectral remote sensing techniques for the detection of CH4 in the near and shortwave infrared (NIR-SWIR: 0.75–3 µm) and longwave infrared (LWIR: 7–14 µm) have been demonstrated in the literature with multiple sensors and scenarios. However, the acquisition and processing of hyperspectral data in the midwave infrared (MWIR: 3–5 µm) for this application is rather scarce. Here, a controlled field experiment was used to evaluate the potential for CH4 plume detection in the MWIR based on hyperspectral data acquired with the SEBASS airborne sensor. For comparison purposes, LWIR data were also acquired simultaneously with the same instrument. The experiment included surface and undersurface emission sources (ground stations), with flow rates ranging between 0.6–40 m3/h. The data collected in both ranges were sequentially processed using the same methodology. The CH4 plume was detected, variably, in both datasets. The gas plume was detected in all LWIR images acquired over nine gas leakage stations. In the MWIR range, the plume was detected in only four stations, wherein 18 m3/h was the lowest flux sensed. We demonstrate that the interference of target reflectance, the low contrast between plume and background and a low signal of the CH4 feature in the MWIR at ambient conditions possibly explain the inferior results observed for this range when compared to LWIR. Furthermore, we show that the acquisition time and weather conditions, including specific limits of temperature, humidity, and wind speed, proved critical for plume detection using daytime MWIR hyperspectral data.

Description: Remote Sensing, Vol. 10, Pages 1238: Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images Remote Sensing doi: 10.3390/rs10081238 Authors: Guoqing Cui Zhiyong Lv Guangfei Li Jón Atli Benediktsson Yudong Lu Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies.

Description: Water, Vol. 10, Pages 1046: Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China Water doi: 10.3390/w10081046 Authors: Min Luo Tie Liu Fanhao Meng Yongchao Duan Amaury Frankl Anming Bao Philippe De Maeyer The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate change. This paper compares the performance of available bias correction methods that focus on the performance of precipitation and temperature projections. The hydrological effects of these correction methods are evaluated by the modelled discharges of the Kaidu River Basin. The results show that all used methods improve the performance of the original RCM precipitation and temperature simulations across a number of levels. The corrected results obtained by precipitation correction methods demonstrate larger diversities than those produced by the temperature correction methods. The performance of hydrological modelling is highly influenced by the choice of precipitation correction methods. Furthermore, no substantial differences can be identified from the results of the temperature-corrected methods. The biases from input data are often greater from the works of hydrological modelling. The suitability of these approaches depends upon the regional context and the RCM model, while their application procedure and a number of results can be adapted from region to region.

Description: Water, Vol. 10, Pages 1047: Assessing Decadal Trends of a Nitrate-Contaminated Shallow Aquifer in Western Nebraska Using Groundwater Isotopes, Age-Dating, and Monitoring Water doi: 10.3390/w10081047 Authors: Martin J. Wells Troy E. Gilmore Aaron R. Mittelstet Daniel Snow Steven S. Sibray Shallow aquifers are prone to nitrate contamination worldwide. In western Nebraska, high groundwater nitrate concentrations ([NO3−]) have resulted in the exploration of new groundwater and nitrogen management regulations in the North Platte Natural Resources District (NPNRD). A small region of NPNRD (“Dutch Flats”) was the focus of intensive groundwater sampling by the United States Geological Survey from 1995 to 1999. Nearly two decades later, notable shifts have occurred in variables related to groundwater recharge and [NO3−], including irrigation methods. The objective of this study was to evaluate how changes in these variables, in part due to regulatory changes, have impacted nitrate-contaminated groundwater in the Dutch Flats area. Groundwater samples were collected to assess changes in: (1) recharge rates; (2) biogeochemical processes; and (3) [NO3−]. Groundwater age increased in 63% of wells and estimated recharge rates were lower for 88% of wells sampled (n = 8). However, mean age and recharge rate estimated in 2016 (19.3 years; R = 0.35 m/year) did not differ significantly from mean values determined in 1998 (15.6 years; R = 0.50 m/year). δ15N-NO3− (n = 14) and dissolved oxygen data indicate no major changes in biogeochemical processes. Available long-term data suggest a downward trend in normalized [NO3−] from 1998 to 2016, and lower [NO3−] was observed in 60% of wells sampled in both years (n = 87), but median values were not significantly different. Collectively, results suggest the groundwater system is responding to environmental variables to a degree that is detectable (e.g., trends in [NO3−]), although more time and/or substantial changes may be required before it is possible to detect significantly different mean recharge.

Description: Remote Sensing, Vol. 10, Pages 929: Intercomparison and Validation of SAR-Based Ice Velocity Measurement Techniques within the Greenland Ice Sheet CCI Project Remote Sensing doi: 10.3390/rs10060929 Authors: John Peter Merryman Boncori Morten Langer Andersen Jørgen Dall Anders Kusk Martijn Kamstra Signe Bech Andersen Noa Bechor Suzanne Bevan Christian Bignami Noel Gourmelen Ian Joughin Hyung-Sup Jung Adrian Luckman Jeremie Mouginot Julia Neelmeijer Eric Rignot Kilian Scharrer Thomas Nagler Bernd Scheuchl Tazio Strozzi Ice velocity is one of the products associated with the Ice Sheets Essential Climate Variable. This paper describes the intercomparison and validation of ice-velocity measurements carried out by several international research groups within the European Space Agency Greenland Ice Sheet Climate Change Initiative project, based on space-borne Synthetic Aperture Radar (SAR) data. The goal of this activity was to survey the best SAR-based measurement and error characterization approaches currently in practice. To this end, four experiments were carried out, related to different processing techniques and scenarios, namely differential SAR interferometry, multi aperture SAR interferometry and offset-tracking of incoherent as well as of partially-coherent data. For each task, participants were provided with common datasets covering areas located on the Greenland ice-sheet margin and asked to provide mean velocity maps, quality characterization and a description of processing algorithms and parameters. The results were then intercompared and validated against GPS data, revealing in several cases significant differences in terms of coverage and accuracy. The algorithmic steps and parameters influencing the coverage, accuracy and spatial resolution of the measurements are discussed in detail for each technique, as well as the consistency between quality parameters and validation results. This allows several recommendations to be formulated, in particular concerning procedures which can reduce the impact of analyst decisions, and which are often found to be the cause of sub-optimal algorithm performance.

Description: Remote Sensing, Vol. 10, Pages 926: Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe Remote Sensing doi: 10.3390/rs10060926 Authors: Najib Djamai Richard Fernandes Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was −0.02 cm (−0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and R2 above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and R2 as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and R2 as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors.

Description: Remote Sensing, Vol. 10, Pages 870: Low-Frequency Sea Surface Radar Doppler Echo Remote Sensing doi: 10.3390/rs10060870 Authors: Yury Yu. Yurovsky Vladimir N. Kudryavtsev Semyon A. Grodsky Bertrand Chapron The sea surface normalized radar backscatter cross-section (NRCS) and Doppler velocity (DV) exhibit energy at low frequencies (LF) below the surface wave peak. These NRCS and DV variations are coherent and thus may produce a bias in the DV averaged over large footprints, which is important for interpretation of Doppler scatterometer measurements. To understand the origin of LF variations, the platform-borne Ka-band radar measurements with well-pronounced LF variations at frequencies below wave peak (0.19 Hz) are analyzed. These data show that the LF NRCS is coherent with wind speed at 21 m height while the LF DV is not. The NRCS-wind correlation is significant only at frequencies below 0.01 Hz indicating either differences between near-surface wind (affecting radar signal) and 21-m height wind (actually measured) or contributions of other mechanisms of LF radar signal variations. It is shown that non-linearity in NRCS-wave slope Modulation Transfer Function (MTF) and inherent averaging within radar footprint account for NRCS and DV LF variance, with the exception of VV NRCS for which almost half of the LF variance is unexplainable by these mechanisms and perhaps attributable to wind fluctuations. Although the distribution of radar DV is quasi-Gaussian, suggesting virtually little impact of non-linearity, the LF DV variations arise due to footprint averaging of correlated local DV and non-linear NRCS. Numerical simulations demonstrate that MTF non-linearity weakly affects traditional linear MTF estimate (less than 10% for typical MTF magnitudes less than 20). Thus the linear MTF is a good approximation to evaluate the DV averaged over large footprints typical of satellite observations.

Description: Remote Sensing, Vol. 10, Pages 869: The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes Remote Sensing doi: 10.3390/rs10060869 Authors: Wenqing Tang Simon Yueh Daqing Yang Alexander Fore Akiko Hayashi Tong Lee Severine Fournier Benjamin Holt Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability to address the critical question of how climate variability and change affect the Arctic Ocean freshwater. The L-band microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) mission has been providing SSS measurements since April 2015, at approximately 60 km resolution with Arctic Ocean coverage in 1–2 days. With improved land/ice correction, the SMAP SSS algorithm that was developed at the Jet Propulsion Laboratory (JPL) is able to retrieve SSS in ice-free regions 35 km of the coast. SMAP observes a large-scale contrast in salinity between the Atlantic and Pacific sides of the Arctic Ocean, while retrievals within the Arctic Circle vary over time, depending on the sea ice coverage and river runoff. We assess the accuracy of SMAP SSS through comparative analysis with in situ salinity data collected by Argo floats, ships, gliders, and in field campaigns. Results derived from nearly 20,000 pairs of SMAP and in situ data North of 50°N collocated within a 12.5-km radius and daily time window indicate a Root Mean Square Difference (RMSD) less than ~1 psu with a correlation coefficient of 0.82 and a near unity regression slope over the entire range of salinity. In contrast, the Hybrid Coordinate Ocean Model (HYCOM) has a smaller RMSD with Argo. However, there are clear systematic biases in the HYCOM for salinity in the range of 25–30 psu, leading to a regression slope of about 0.5. In the region North of 65°N, the number of collocated samples drops more than 70%, resulting in an RMSD of about 1.2 psu. SMAP SSS in the Kara Sea shows a consistent response to discharge anomalies from the Ob’ and Yenisei rivers between 2015 and 2016, providing an assessment of runoff impact in a region where no in situ salinity data are available for validation. The Kara Sea SSS anomaly observed by SMAP is missing in the HYCOM SSS, which assimilates climatological runoffs without interannual changes. We explored the feasibility of using SMAP SSS to monitor the sea surface salinity variability at the major Arctic Ocean gateways. Results show that although the SMAP SSS is limited to about 1 psu accuracy, many large salinity changes are observable. This may lead to the potential application of satellite SSS in the Arctic monitoring system as a proxy of the upper ocean layer freshwater exchanges with subarctic oceans.

Description: Water, Vol. 10, Pages 759: Effects of Water Diversion from Yangtze River to Lake Taihu on the Phytoplankton Habitat of the Wangyu River Channel Water doi: 10.3390/w10060759 Authors: Jiangyu Dai Shiqiang Wu Xiufeng Wu Wanyun Xue Qianqian Yang Senlin Zhu Fangfang Wang Dan Chen To reveal the effects of water diversion from the Yangtze River to Lake Taihu on the phytoplankton habitat of the main water transfer channel of the Wangyu River, we investigated the water’s physicochemical parameters and phytoplankton communities during the water diversion and non-diversion periods over the winters between 2014–2016, respectively. During the water diversion periods in the winter of 2014 and 2015, the nutrients and organic pollutant contents of the Wangyu River channel were significantly lower than those during the non-diversion period in 2016. Moreover, the phytoplankton diversities and relative proportions of Bacillariophyta during the diversion periods evidently increased during the water diversion periods in winter. The increase in the water turbidity content, the decrease in the contents of the permanganate index, and the total phosphorus explained only 21.4% of the variations in the phytoplankton communities between the diversion and non-diversion periods in winter, which revealed significant contributions of the allochthonous species from the Yangtze River and tributaries of the Wangyu River to phytoplankton communities in the Wangyu River. The increasing gradient in the contents of nutrients and organic pollutants from the Yangtze River to Lake Taihu indicated the potential allochthonous pollutant inputs along with the Wangyu River. Further controlling the pollutants from the tributaries of the Wangyu River is critical in order to improve the phytoplankton habitats in river channels and Lake Taihu.

Description: Water, Vol. 10, Pages 731: What Germany’s University Beginners Think about Water Reuse Water doi: 10.3390/w10060731 Authors: Sarah Schmid Franz X. Bogner Water reuse is a new technology, not yet implemented, but discussed for use in Germany. Public opinion plays a major role in the success of the introduction of this new technology and was not yet analyzed for Germany. When monitoring 340 university beginners’ conceptions regarding water reuse, a variety of conceptions appeared. While usage of tap water is accepted for drinking purposes, acceptance of recycled water for oral consumption was low. When asked for reasons for (not) using recycled water, three groups of respondents were extracted: (a) The acceptors (convinced of quality, or naming sustainability as a reason); (b) the undecided (doubts about quality, rejection of its use for consumption, and psychological conflicts of logic and disgust); (c) the non-acceptors (unconvinced of quality and preference for bottled water). When asked about factors that would lead to accepting the use of recycled water, insights into treatment processes were identified as the most convincing, followed by educational films and guided tours. Participants showed high conviction about currently existing tap-water qualities. Having water that is cleaned before it reaches the consumer was reported to have high priority. To increase acceptance of water reuse, recommendations for appropriate outreach programs are discussed.

Description: Water, Vol. 10, Pages 730: A Comparative Study of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition Water doi: 10.3390/w10060730 Authors: Yicheng Gong Zhongjing Wang Guoyin Xu Zixiong Zhang The reliable and accurate prediction of groundwater levels is important to improve water-use efficiency in the development and management of water resources. Three nonlinear time-series intelligence hybrid models were proposed to predict groundwater level fluctuations through a combination of ensemble empirical mode decomposition (EEMD) and data-driven models (i.e., artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference systems (ANFIS)), respectively. The prediction capability of EEMD-ANN, EEMD-SVM, and EEMD-ANFIS hybrid models was investigated using a monthly groundwater level time series collected from two observation wells near Lake Okeechobee in Florida. The statistical parameters correlation coefficient (R), normalized mean square error (NMSE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), and Akaike information criteria (AIC) were used to assess the performance of the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models. The results achieved from the EEMD-ANN, EEMD-SVM and EEMD-ANFIS models were compared with those from the ANN, SVM and ANFIS models. The three hybrid models (i.e., EEMD-ANN, EEMD-SVM, and EEMD-ANFIS) proved to be applicable to forecast the groundwater level fluctuations. The values of the statistical parameters indicated that the EEMD-ANFIS and EEMD-SVM models achieved better prediction results than the EEMD-ANN model. Meanwhile, the three models coupled with EEMD were found have better prediction results than the models that were not. The findings from this study indicate that the proposed nonlinear time-series intelligence hybrid models could improve the prediction capability in forecasting groundwater level fluctuations, and serve as useful and helpful guidelines for the management of sustainable water resources.

Description: Water, Vol. 10, Pages 729: Geothermometry and Isotope Geochemistry of CO2-Rich Thermal Waters in Choygan, East Tuva, Russia Water doi: 10.3390/w10060729 Authors: Anastasia Shestakova Natalia Guseva Yulia Kopylova Albina Khvaschevskaya David A. Polya Igor Tokarev The Choygan area of southern Siberia, Russia hosts a variety of CO2-rich thermal mineral and other waters emerging from springs at temperatures between 7 °C and 39 °C. Chemical analyses of the spring waters (n = 33) were carried out to characterise the waters and determine their origin. A continuum of compositions was observed between relatively lower temperature (7 °C) HCO3-Ca-Na dominated waters with relatively low amount of total dissolved solids (TDS) and high Eh, and higher temperature (39 °C) HCO3-Na-Ca dominated waters with higher TDS and lower Eh—this reflects largely conservative mixing of these components between near surface low temperature, oxidising groundwaters and higher temperature, more reducing thermal waters derived from a deeper geothermal reservoir. Stable isotopic data are consistent with all the water ultimately being derived from meteoric water that has undergone varying degrees of isotopic fractionation following evaporation. The inferred δ18O and δ2H isotopic composition of the unfractionationed meteoric waters is lighter than that expected that of mean annual local precipitation, which together with a strong negative correlation between δ18O and the elevation of the sampled discharging springs, suggests recharge at higher elevations (1600 m to 3000 m; average 2600 m). Reservoir temperature, calculated using geothermometers and an analysis of saturation indices of plausible reservoir minerals, ranged from 70 °C to 100 °C at an inferred depth of 2 to 3 km. Not all chemical components were found to follow conservative mixing behaviour. In particular, (i) the CO2 contents of the waters were highly variable, suggesting either varying degrees of degassing and/or near discharge admixture with air, and (ii) SO4 concentrations in the lower temperature thermal CO2-rich waters were highly variable, suggesting a role of near surface oxidation processes, for example of pyrite, in modifying the concentration of redox sensitive components. Limited δ13C data are consistent with the CO2 predominately being derived from dissolution of metamorphic/igneous carbonate minerals in the reservoir. Based on geological conditions, isotope and chemical data, a conceptual circulation model of the Choygan hydrothermal system is proposed.

Description: Water, Vol. 10, Pages 728: An Integration Approach for Mapping Field Capacity of China Based on Multi-Source Soil Datasets Water doi: 10.3390/w10060728 Authors: Xiaotao Wu Guihua Lu Zhiyong Wu Hai He Jianhong Zhou Zhenchen Liu Field capacity is one of the most important soil hydraulic properties in water cycle, agricultural irrigation, and drought monitoring. It is difficult to obtain the distribution of field capacity on a large scale using manual measurements that are both time-consuming and labor-intensive. In this study, the field capacity ensemble members were established using existing pedotransfer functions (PTFs) and multiple linear regression (MLR) based on three soil datasets and 2388 in situ field capacity measurements in China. After evaluating the accuracy of each ensemble member, an integration approach was proposed for estimating the field capacity distribution and development of a 250 m gridded field capacity dataset in China. The spatial correlation coefficient (R) and root mean square error (RMSE) between the in situ field capacity and ensemble field capacity were 0.73 and 0.048 m3·m−3 in region scale, respectively. The ensemble field capacity shows great consistency with practical distribution of field capacity, and the deviation is revised when compared with field capacity datasets provided by previous researchers. It is a potential product for estimating field capacity in hydrological and agricultural practices on both large and fine scales, especially in ungauged regions.

Description: Remote Sensing, Vol. 10, Pages 934: Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification Remote Sensing doi: 10.3390/rs10060934 Authors: Kunlun Qi Qingfeng Guan Chao Yang Feifei Peng Shengyu Shen Huayi Wu Convolutional neural networks (CNNs) have been increasingly used in remote sensing scene classification/recognition. The conventional CNNs are sensitive to the rotation of the image scene, which will inevitably result in the misclassification of remote sensing scene images that belong to the same category. In this work, we equip the networks with a new pooling strategy, “concentric circle pooling”, to alleviate the above problem. The new network structure, called CCP-net can generate a concentric circle-based spatial-rotation-invariant representation of an image, hence improving the classification accuracy. The square kernel is adopted to approximate the circle kernels in concentric circle pooling, which is much more efficient and suitable for CNNs to propagate gradients. We implement the training of the proposed network structure with standard back-propagation, thus CCP-net is an end-to-end trainable CNNs. With these advantages, CCP-net should in general improve CNN-based remote sensing scene classification methods. Experiments using two publicly available remote sensing scene datasets demonstrate that using CCP-net can achieve competitive classification results compared with the state-of-art methods.

Description: Water, Vol. 10, Pages 779: Study of the Scale Effect on Permeability in the Interlayer Shear Weakness Zone Using Sequential Indicator Simulation and Sequential Gaussian Simulation Water doi: 10.3390/w10060779 Authors: Meng Chen Zhifang Zhou Lei Zhao Mu Lin Qiaona Guo Mingwei Li The interlayer shear weakness zone (ISWZ) is a deformation zone in stratified rock masses, with different width and spacing, due to tectonic stresses. It represents the main flow path in rocks due to higher permeability compared with massive rocks. The permeability values of an ISWZ can vary significantly depending on the scale. This study focuses on the correlations between the permeability properties of ISWZs and their geometry properties. A range of realistic 3-D numerical models of ISWZs is developed using geostatistical modeling, with fine-scale geometry and permeability information taken into consideration. These ISWZs represent a set of mud content and width distributions that are typical for ISWZs. Horizontal and vertical permeability values for all ISWZs are found to change in small-scale samples, whereas these fluctuations decrease with increasing sample size. For different types of ISWZs, the results show that ISWZs with variable width will show a significantly larger scale effect on the permeability than that of ISWZs with constant width. Furthermore, ISWZs with a higher mud content display greater variation in horizontal permeability, while the opposite is true for vertical permeability. Based on the coefficient of permeability variation, a criterion is proposed to identify the calculated permeability of a sample is locally homogeneous. The size for this sample relies on the properties estimated (horizontal and vertical permeability) and geometry features. These findings could provide a basis for the selection of permeability values of an ISWZ in hydraulic engineering. Additionally, the procedures used in this article can be applied to any type of ISWZs.

Description: Water, Vol. 10, Pages 774: Development of an Integrated Modelling System for Evaluating Water Quantity and Quality Effects of Individual Wetlands in an Agricultural Watershed Water doi: 10.3390/w10060774 Authors: Yongbo Liu Wanhong Yang Hui Shao Zhiqiang Yu John Lindsay A GIS-based fully-distributed model, IMWEBs-Wetland (Integrated Modelling for Watershed Evaluation of BMPs—Wetland), is developed to simulate hydrologic processes of site-specific wetlands in an agricultural watershed. This model, powered by the open-source GIS Whitebox Geospatial Analysis Tools (GAT) and advanced database technologies, allows users to simulate and assess water quantity and quality effects of individual wetlands at site and watershed scales. A case study of the modelling system is conducted in a subbasin of the Broughton’s Creek Watershed in southern Manitoba of Canada. Modelling results show that the model is capable of simulating wetland processes in a complex watershed with various land management practices. The IMWEBs-Wetland model is unique in simulating the water quantity and quality effects of individual wetlands, which can be used to examine location-specific targeting of wetland retention and restoration at a watershed scale.

Description: Water, Vol. 10, Pages 770: Electrochemical Degradation of Phenol and Resorcinol Molecules through the Dissolution of Sacrificial Anodes of Macro-Corrosion Galvanic Cells Water doi: 10.3390/w10060770 Authors: Boguslaw Pierozynski Grazyna Piotrowska This paper reports on the processes of phenol and resorcinol electrodegradation carried-out through continuous anodic dissolution of aluminum alloy and carbon steel sacrificial anodes for artificially aerated Cu-Al alloy and Cu-Fe-based galvanic (macro-corrosion) cells and synthetically prepared wastewater solutions. Electrochemical experiments were carried-out by means of a laboratory size, PMMA (Poly-methyl methacrylate)-made electrolyser unit, where significant degrees of phenol (10–89%) and resorcinol (13–37%) decomposition were obtained and visualized through the respective chemical/spectroscopy analyses. In addition, quantitative determination of phenol, as well as resorcinol (and possible electrodegradation products) for the selected experimental conditions was performed by means of instrumental high-performance liquid chromatography/mass spectrometry analysis.

Description: Water, Vol. 10, Pages 766: The Influences of Sponge City on Property Values in Wuhan, China Water doi: 10.3390/w10060766 Authors: Shiying Zhang Chris Zevenbergen Paul Rabé Yong Jiang Rapid urbanization in China and global climate change have increased urban flood exposure in Wuhan, and the increased flood risk has reduced property values in flood-prone areas. The central government of China is promoting the application of the sponge city concept to reduce urban flood risk and improve the environment in cities. Wuhan is one of the pilot cities of this initiative. A shortage of funds is one of the main obstacles to sponge city construction, as is the lack of a suitable business model. To test residents’ willingness to pay for sponge city construction, this research analyzed the impact of sponge city construction on the housing values of areas covered by sponge city interventions. The authors conducted interviews and analyzed secondary data to gauge residents’ awareness and perceptions of sponge city interventions. The results show that more than half of residents in Wuhan are willing to pay for sponge city measures, but the amount they are willing to pay is limited. Residents are more willing to pay for improvements of their living environment than for flood reduction measures.

Description: Remote Sensing, Vol. 10, Pages 961: Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China Remote Sensing doi: 10.3390/rs10060961 Authors: Dong He Guihua Yi Tingbin Zhang Jiaqing Miao Jingji Li Xiaojuan Bie The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

Description: Remote Sensing, Vol. 10, Pages 959: The Temperature Vegetation Dryness Index (TVDI) Based on Bi-Parabolic NDVI-Ts Space and Gradient-Based Structural Similarity (GSSIM) for Long-Term Drought Assessment Across Shaanxi Province, China (2000–2016) Remote Sensing doi: 10.3390/rs10060959 Authors: Ying Liu Hui Yue Traditional NDVI-Ts space is triangular or trapezoidal, but Liu et al. (2015) discovered that the NDVI-Ts space was bi-parabolic when the study area was covered with low biomass vegetation. Moreover, the numerical value of the indicator was considered in most of the study when the drought conditions in the space domain were evaluated. In addition, quantitatively assessing the spatial-temporal changes of the drought was not enough. In this study, first, we used MODIS NDVI and Ts data to reexamine if the NDVI-Ts space with “time” and a single pixel domain is bi-parabolic in the Shaanxi province of China, which is vegetated with low biomass to high biomass. This is compared with the triangular NDVI-Ts space and one of the well-known drought indexes called the temperature-vegetation index (TVX). The results demonstrated that dry and wet edges exhibited a parabolic shape again in scatter plots of Ts and NDVI in the Shaanxi province, which was linear in the triangular NDVI-Ts space. The Temperature Vegetation Dryness Index (TVDIc) was obtained from bi-parabolic NDVI-Ts andTVDIt was obtained from the triangular NDVI-Ts space and TVX were compared with 10-cm depth relative soil moisture. By estimating the 10-cm depth soil moisture, TVDIc was better than TVDIt, which were all apparently better than TVX. Second, combined with MODIS data, the drought conditions of the study area were assessed by TVDIc between 2000 to 2016. Spatially, the drought in the Shaanxi Province between 2000 to 2016 were mainly distributed in the northwest, North Shaanxi, and the North and East Guanzhong plain. The drought area of the Shaanxi province accounted for 31.95% in 2000 and 27.65% in 2016, respectively. Third, we quantitatively evaluated the variation of the drought status by using Gradient-based Structural Similarity (GSSIM) methods. The area of the drought conditions significantly changed and moderately changed at 5.34% and 40.22%, respectively, between 2000 and 2016. Finally, the possible reasons for drought change were discussed. The change of precipitation, temperature, irrigation, destruction or betterment of vegetation, and the enlargement of opening mining, etc., can lead to the variations of drought.

Description: Remote Sensing, Vol. 10, Pages 963: A Seismic Capacity Evaluation Approach for Architectural Heritage Using Finite Element Analysis of Three-Dimensional Model: A Case Study of the Limestone Hall in the Ming Dynasty Remote Sensing doi: 10.3390/rs10060963 Authors: Siliang Chen Shaohua Wang Chen Li Qingwu Hu Hongjun Yang A lot of architectural heritage in China are urgently in need to carry out seismic assessment for further conservation. In this paper, a seismic capacity evaluation approach for architectural heritage using finite element analysis with precision three-dimensional data was proposed. The Limestone Hall of Shaanxi Province was taken as an example. First, low attitude unmanned aerial vehicle photogrammetry and a close-range photogrammetry camera were used to collect multiple view images to obtain the precision three-dimensional current model of the Limestone. Second, the dimensions of internal structures of Limestone Hall are obtained by means of structural analysis; re-establishing the ideal model of Limestone Hall based on the modeling software. Third, a finite element analysis was conducted to find out the natural frequency and seismic stress in various conditions with the 3D model using ANSYS software. Finally, the seismic capacity analysis results were comprehensively evaluated for the risk assessment and simulation. The results showed that for architectural heritage with a multilayer structure, utilizing photogrammetric surveying and mapping, 3D software modeling, finite element software simulation, and seismic evaluation for simulation was feasible where the precision of the modeling and parameters determine the accuracy of the simulation. The precise degree of the three-dimensional model, the accurate degree of parameter measurement and estimation, the setting of component attributes in the finite element model and the strategy of finite element analysis have an important effect on the result of seismic assessment. The main body structure of the Limestone Hall could resist an VII-degree earthquake at most, and the ridge of the second floor could not resist a V-degree earthquake due to unsupported conditions. The maximum deformation of the Limestone Hall during the earthquake occurred in the tabia layer below the second roof.

Description: Water, Vol. 10, Pages 794: Navigating the Water-Energy Governance Landscape and Climate Change Adaptation Strategies in the Northern Patagonia Region of Argentina Water doi: 10.3390/w10060794 Authors: Laura Forni Marisa Escobar Pablo Cello Marta Marizza Gustavo Nadal Leonidas Girardin Fernando Losano Lisandro Bucciarelli Charles Young David Purkey Water scientists often find themselves interacting with decision-makers with varying levels of technical background. The sustainable management of water resources is complex by nature, and future conditions are highly uncertain, requiring modeling approaches capable of accommodating a variety of parameters and scenarios. Technical findings from these analyses need to be positioned and conducted within the governance institutions to ensure decision-makers utilize them. This paper examines the water resource challenges for a large basin in northern Patagonia, Argentina and utilizes the Robust Decision Support (RDS) framework to evaluate trade-offs and strategies in a participatory process that included researchers and decision-makers. Integrated water resources models using simulation modeling and decision space visualization show significant climate change impacts, which are augmented with irrigated agriculture expansion and increasing hydropower production.

Description: Water, Vol. 10, Pages 789: Spatio-Temporal Analysis of Meteorological Elements in the North China District of China during 1960–2015 Water doi: 10.3390/w10060789 Authors: Jinsong Ti Yuhao Yang Xiaogang Yin Jing Liang Liangliang Pu Yulin Jiang Xinya Wen Fu Chen The North China District (NCD) is one of the main grain production regions in China. The double cropping system of irrigation has been leading to the groundwater table decline at the speed of 1–2 m per year. Climate change leads to uncertainty surrounding the future of the NCD agricultural system, which will have great effects on crop yields and crop water demands. In this research, the Meteorological dataset from 54 weather station sites over the period 1960–2015 were collected to quantify the long-term spatial and temporal trends of meteorological data, including daily minimum temperature (Tmin), maximum temperature (Tmax), precipitation, solar radiation, reference evapotranspiration (ET0), and aridity index (AI). The results show that the long-term wheat and maize growing season and annual average air temperatures (Tmin and Tmax) showed strong north to south increasing trends throughout the NCD. The average annual precipitation was 632.9 mm across the NCD, more than 70% of which was concentrated in the maize growing season. The regional average annual ET0 was 1026.1 mm, which was 531.2 and 497.4 mm for the wheat and maize growing season, respectively. The regional precipitation decreased from northwest to southeast in each growing season and annual timescale. The funnel areas have lower precipitation and higher ET0 than the regional average, which may lead to the mining of the groundwater funnel area. The regional average annual AI is 0.63, which lies in the humid class. For temporal analysis, the regional average trends in annual Tmin, Tmax, solar radiation, ET0, precipitation, and AI were 0.37 °C/10a, 0.15 °C/10a, −0.28 MJ/day/m2/10a, −2.98 mm/10a, −12.04 mm/10a, and 0.005/10a, respectively. The increasing trend of temperature and the decreasing trend of solar radiation may have a negative effect on the regional food security. The funnel area AI showed a significant increasing trend for the winter wheat growing season and a decreasing trend for maize, which indicated that more irrigation will be needed for the maize growing season and the winter fallow policy may lead to the increasing trend precipitation being wasted. Analyzing the growing season and the annual meteorological elements of the spatiotemporal trends can help us better understand the influence of climate change on the natural resources and agricultural development in both the past and the future, and will provide us with invaluable information for the modification of cropping patterns to protect the regional and national water and food security.

Description: Water, Vol. 10, Pages 797: Disinfection Methods for Swimming Pool Water: Byproduct Formation and Control Water doi: 10.3390/w10060797 Authors: Huma Ilyas Ilyas Masih Jan Peter van der Hoek This paper presents a comprehensive and critical comparison of 10 disinfection methods of swimming pool water: chlorination, electrochemically generated mixed oxidants (EGMO), ultraviolet (UV) irradiation, UV/chlorine, UV/hydrogen peroxide (H2O2), UV/H2O2/chlorine, ozone (O3)/chlorine, O3/H2O2/chlorine, O3/UV and O3/UV/chlorine for the formation, control and elimination of potentially toxic disinfection byproducts (DBPs): trihalomethanes (THMs), haloacetic acids (HAAs), haloacetonitriles (HANs), trihaloacetaldehydes (THAs) and chloramines (CAMs). The statistical comparison is carried out using data on 32 swimming pools accumulated from the reviewed studies. The results indicate that O3/UV and O3/UV/chlorine are the most promising methods, as the concentration of the studied DBPs (THMs and HANs) with these methods was reduced considerably compared with chlorination, EGMO, UV irradiation, UV/chlorine and O3/chlorine. However, the concentration of the studied DBPs including HAAs and CAMs remained much higher with O3/chlorine compared with the limits set by the WHO for drinking water quality. Moreover, the enhancement in the formation of THMs, HANs and CH with UV/chlorine compared with UV irradiation and the increase in the level of HANs with O3/UV/chlorine compared with O3/UV indicate the complexity of the combined processes, which should be optimized to control the toxicity and improve the quality of swimming pool water.

Description: Water, Vol. 10, Pages 802: The Impact of Climate on Hydrological Extremes Water doi: 10.3390/w10060802 Authors: Salvatore Manfreda Vito Iacobellis Andrea Gioia Mauro Fiorentino Krzysztof Kochanek High and low flows and associated floods and droughts are extreme hydrological phenomena mainly caused by meteorological anomalies and modified by catchment processes and human activities. They exert increasing on human, economic, and natural environmental systems around the world. In this context, global climate change along with local fluctuations may eventually trigger a disproportionate response in hydrological extremes. This special issue focuses on observed extreme events in the recent past, how these extremes are linked to a changing global/regional climate, and the manner in which they may shift in the coming years.

Description: Water, Vol. 10, Pages 800: Quantification of Seasonal Precipitation over the upper Chao Phraya River Basin in the Past Fifty Years Based on Monsoon and El Niño/Southern Oscillation Related Climate Indices Water doi: 10.3390/w10060800 Authors: Tsuyoshi Kinouchi Gakuji Yamamoto Atchara Komsai Winai Liengcharernsit For better water resources management, we proposed a method to estimate basin-scale seasonal rainfall over selected areas of the Chao Phraya River Basin, Thailand, from existing climate indices that represent variations in the Asian summer monsoon, the El Niño/Southern Oscillation, and sea surface temperatures (SST) in the Pacific Ocean. The basin-scale seasonal rainfall between 1965 and 2015 was calculated for the upper Ping River Basin (PRB) and the upper Nan River Basin (NRB) from a gridded rainfall dataset and rainfall data collected at several gauging stations. The corresponding climate indices, i.e., the Equatorial-Southern Oscillation Index (EQ-SOI), Indian Monsoon Index (IMI), and SST-related indices, were examined to quantify seasonal rainfall. Based on variations in the rainfall anomaly and each climate index, we found that IMI is the primary variable that can explain variations in seasonal rainfall when EQ-SOI is negative. Through a multiple regression analysis, we found that EQ-SOI and two SST-related indices, i.e., Pacific Decadal Oscillation Index (PDO) and SST anomalies in the tropical western Pacific (SSTNW), can quantify the seasonal rainfall for years with positive EQ-SOI. The seasonal rainfall calculated for 1975 to 2015 based on the proposed method was highly correlated with the observed rainfall, with correlation coefficients of 0.8 and 0.86 for PRB and NRB, respectively. These results suggest that the existing indices are useful for quantifying basin-scale seasonal rainfall, provided a proper classification and combination of the climate indices are introduced. The developed method could forecast seasonal rainfall over the target basins if well-forecasted climate indices are provided with sufficient leading time.

Description: Remote Sensing, Vol. 10, Pages 975: A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment Remote Sensing doi: 10.3390/rs10060975 Authors: Mohsen Alizadeh Ibrahim Ngah Mazlan Hashim Biswajeet Pradhan Amin Beiranvand Pour Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management.

Description: Water, Vol. 10, Pages 811: Simulation of Fluid and Complex Obstacle Coupling Based on Narrow Band FLIP Method Water doi: 10.3390/w10060811 Authors: Changjun Zou Yong Yin With the continuous development of fluid simulation theory and technology, there are increasingly higher requirements for simulation of complex fluid interaction. Fluid simulation based on the Eulerian method is limited by the grid resolution, and the sawtooth phenomenon occurs near the obstacle boundary. To enhance the fluid interaction performance with complex obstacle, an advanced fluid interaction method was proposed based on NBFLIP. Improved from FLIP method, the NBFLIP method combines the advantages of Euler method and Lagrangian method. The SDF method is proposed in complex obstacle discretion, with an expectation to facilitate the processing with obstacle boundary and efficiency improvement. Compared with FLIP method, particle number in NBFLIP method is reduced by 86.2% and the average running time per frame is reduced by 36.1%.

Description: Water, Vol. 10, Pages 817: Reuse and Recycling of Livestock and Municipal Wastewater in Chilean Agriculture: A Preliminary Assessment Water doi: 10.3390/w10060817 Authors: Cristina-Alejandra Villamar Ismael Vera-Puerto Diego Rivera Felipe De la Hoz Chile is an agricultural power, but also one of the most vulnerable countries to climate change and water shortage. About 50% of the irrigated agriculture land in Chile is in the central zone, thanks to its agricultural-climatic characteristics that provide an adequate water supply (100–4000 m3/s). However, the vulnerability scenario in this zone is high due to the seasonal availability of water resources. Therefore, opportunities to use non-conventional alternative sources (e.g., wastewater) become an appealing and feasible option due to the high population and animal density (>76%) in this part of the country. Moreover, the physicochemical characteristics of the municipal and livestock wastewater suggest that there are potential opportunities to recycle nutrients for agricultural production. In Chile, wastewater reuse opportunities are noted by the wide coverage of wastewater treatment programs, with municipal and intensified livestock production taking up most of the percentage (>99%). Nevertheless, more than 70% of wastewater treatment systems reach biological secondary treatment, which suggests reuse possibilities only for non-food crops. Therefore, this paper is focused on a preliminary analysis of the potential of reusing and recycling municipal and livestock wastewater for Chilean agriculture. There is some reuse work occurring in Chile, specifically in the use of municipal and livestock wastewater for cereal crops (animal feed), forests, and grasslands. However, aspects related to the long-term effects of these practices have not yet been evaluated. Therefore, municipal and livestock wastewater in Chile could be re-valued in agriculture, but the current quality and condition of treated wastewater do not ensure its safe use in food crops. In addition, state policies are needed to provide sustainability (circular and ethic economy) to water reusing/recycling in agriculture.

Description: Water, Vol. 10, Pages 806: Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network Water doi: 10.3390/w10060806 Authors: Siyu Chen Guohua Fang Xianfeng Huang Yuhong Zhang Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field.

Description: Remote Sensing, Vol. 10, Pages 976: Physical Retrieval of Land Surface Emissivity Spectra from Hyper-Spectral Infrared Observations and Validation with In Situ Measurements Remote Sensing doi: 10.3390/rs10060976 Authors: Guido Masiello Carmine Serio Sara Venafra Giuliano Liuzzi Laurent Poutier Frank-M. Göttsche A fully physical retrieval scheme for land surface emissivity spectra is presented, which applies to high spectral resolution infrared observations from satellite sensors. The surface emissivity spectrum is represented with a suitably truncated Principal Component Analysis (PCA) transform and PCA scores are simultaneously retrieved with surface temperature and atmospheric parameters. The retrieval methodology has been developed within the general framework of Optimal Estimation and, in this context, is the first physical scheme based on a PCA representation of the emissivity spectrum. The scheme has been applied to IASI (Infrared Atmospheric Sounder Interferometer) and the retrieved emissivities have been validated with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. It has been found that the retrieved emissivity spectra are independent of background information and in good agreement with in situ observations.

Description: Remote Sensing, Vol. 10, Pages 993: Landslide Identification and Monitoring along the Jinsha River Catchment (Wudongde Reservoir Area), China, Using the InSAR Method Remote Sensing doi: 10.3390/rs10070993 Authors: Chaoying Zhao Ya Kang Qin Zhang Zhong Lu Bin Li Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

Description: Water, Vol. 10, Pages 827: Optimization of Nitrogen Removal in Solid Carbon Source SND for Treatment of Low-Carbon Municipal Wastewater with RSM Method Water doi: 10.3390/w10070827 Authors: Liqiu Zhang Youwen Huang Shugeng Li Peifen He Dengmin Wang In this work, a loofah sponge was used as the solid carbon source and the carrier in a biofilm reactor. Simultaneous nitrification and denitrification (SND) technology was used to achieve nitrogen removal in low-carbon municipal wastewater in a sequencing batch biofilm reactor (SBBR). At room temperature, the effects of filling ratio, dissolved oxygen (DO), pH, C/N(CODCr/TN), and aeration time on the removal of nitrogen were systematically studied. In addition, the removal efficiency of total nitrogen (TN) was used as the evaluation index in response surface models (RSM) for optimization of nitrogen removal. The results showed that DO, pH, and aeration time affected nitrogen removal significantly. Therefore, DO, pH, and aeration time were used as the independent variables in RSM. The optimum conditions for nitrogen removal were found to be as follows in RSM: DO = 4.09 mg/L, pH = 7.58, aeration time = 10.47 h. Under the optimum conditions, the maximum TN removal efficiency reached 86.27%. The results also demonstrated that the deviation between the experimental and predicted TN removal efficiency was only 0.58%, the predicted model was reliable for future application.

Description: Remote Sensing, Vol. 10, Pages 1000: Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing Remote Sensing doi: 10.3390/rs10071000 Authors: Xuedong Li Hongyan Zhang Guangbin Yang Yanling Ding Jianjun Zhao The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22–45 g C·m−2·month−1 (30–80%) and 0.13–0.35 kg C·m−2·year−1 (20–60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests’ carbon reserves.

Description: Water, Vol. 10, Pages 835: Hydrogeologic and Paleo-Geographic Characteristics of Riverside Alluvium at an Artificial Recharge Site in Korea Water doi: 10.3390/w10070835 Authors: Soo-Hyoung Lee Se-Yeong Hamm Kyoochul Ha YongCheol Kim Dong-Chan Koh Heesung Yoon Sung-Wook Kim This study showed the hydrogeological characteristics of an alluvial aquifer that is composed of sand, silt, and clay layers in a small domain. It can be classified into a lower high-salinity layer and an upper freshwater layer and contains shells and remnant paleo-seawater (average 5000 μS/cm) due to sea level fluctuation. Geological and electrical conductivity logging, a long-term pumping test, and multi-depth water quality measurements were conducted at pumping, injection, and observational wells to evaluate the hydrogeologic properties, identify the optimal recharge rate, and assess artificial recharge. Using a hydraulic test, a large difference in drawdown and salinity appeared at the radially located observational wells because of the difference in hydraulic connectivity between the wells in the small study area. It was concluded that the hydraulic anisotropy and heterogeneity of the alluvial aquifer should be carefully examined when locating an injection well and considering the efficient design of artificial recharge.

Description: Water, Vol. 10, Pages 819: Nonstationary Flood Frequency Analysis Using Univariate and Bivariate Time-Varying Models Based on GAMLSS Water doi: 10.3390/w10070819 Authors: Ting Zhang Yixuan Wang Bing Wang Senming Tan Ping Feng With the changing environment, a number of researches have revealed that the assumption of stationarity of flood sequences is questionable. In this paper, we established univariate and bivariate models to investigate nonstationary flood frequency with distribution parameters changing over time. Flood peak Q and one-day flood volume W1 of the Wangkuai Reservoir catchment were used as basic data. In the univariate model, the log-normal distribution performed best and tended to describe the nonstationarity in both flood peak and volume sequences reasonably well. In the bivariate model, the optimal log-normal distributions were taken as marginal distributions, and copula functions were addressed to construct the dependence structure of Q and W1. The results showed that the Gumbel-Hougaard copula offered the best joint distribution. The most likely events had an undulating behavior similar to the univariate models, and the combination values of flood peak and volume under the same OR-joint and AND-joint exceedance probability both displayed a decreasing trend. Before 1970, the most likely combination values considering the variation of distribution parameters over time were larger than fixed parameters (stationary), while it became the opposite after 1980. The results highlight the necessity of nonstationary flood frequency analysis.

Description: Water, Vol. 10, Pages 818: Efficient Low-Cost Anaerobic Treatment of Wastewater Using Biochar and Woodchip Filters Water doi: 10.3390/w10070818 Authors: Korbinian Kaetzl Manfred Lübken Tito Gehring Marc Wichern Access to improved sanitation is often lacking in many low-income countries, and approximately 90% of the sewage is discharged without treatment into receiving water bodies. The aim of this study was the development and evaluation of an efficient low-cost wastewater treatment system for developing countries. Biochar and woodchips, potential locally available and inexpensive materials, were used for anaerobic wastewater filtration and their suitability evaluated in comparison to gravel as a common reference material. Filters were fed with raw sewage from a municipal full-scale wastewater treatment plant in Germany at 22 °C room temperature with a hydraulic loading rate (HLR) of 0.05 m∙h−1. This resulted in a mean organic loading rate (OLR) of 252 gCOD∙m−3∙d−1 and a mean organic surface load of 456 gCOD∙m−2∙d−1. To determine the influence of different filter materials, the removal efficiency of chemical oxygen demand (COD), total organic carbon (TOC), turbidity, and faecal indicator bacteria (FIB) E. coli and enterococci were tested. It was found that COD (up to 90%), TOC (up to 80%), FIB (up to 1.7 log10-units), and turbidity (effluent turbidity below 35 NTU) could be significantly reduced. The findings of this study demonstrate the potential of anaerobic filters (AFs) for wastewater treatment in low-income countries to reduce water pollution and comprehensively improve water quality. The performance of biochar filters was significantly better over the entire experiment compared to woodchip and gravel filters with respect to COD, TOC, turbidity, and FIB removal, indicating the superior properties of biochar for wastewater treatment.

Description: Water, Vol. 10, Pages 821: Changes in the Carbon and Water Fluxes of Subtropical Forest Ecosystems in South-Western China Related to Drought Water doi: 10.3390/w10070821 Authors: Lei Zhou Shaoqiang Wang Yonggang Chi Weimin Ju Kun Huang Robert A. Mickler Miaomiao Wang Quanzhou Yu Drought impacts carbon and water fluxes of terrestrial ecosystems, which are strongly coupled. However, the magnitudes of response of carbon and water fluxes to drought are dependent on many processes, which are more complex than previously expected. Southern China experienced regional climatic perturbation events in the past decade and a two-year drought in 2009–2010. We used a terrestrial ecosystem model coupled with remotely sensed observations and metrological data to simulate the variations of net primary productivity (NPP), evapotranspiration (ET), and water-use efficiency (WUE) (i.e., NPP/ET) in south-western China during the period 2001–2010. Using the standard precipitation index (SPI) classifying different drought stresses, we also quantified the effect of drought on the ecosystem by comparing changes in modelled estimates of monthly WUE, NPP and ET under normal (i.e., baseline) and drought conditions (i.e., 2009 and 2010). The results indicated that NPP and ET showed synchronized declines in drought periods, with time-lag effects. Furthermore, drought-induced NPP decline was larger than ET reduction. An increasing trend in WUE from the moderate to extreme drought classes occurred not only in baseline conditions but also in drought conditions. Especially in the extreme drought period (January, 2010), WUE for the forest ecosystem typically showed a positive response to drought, indicating a drought-resilient forest ecosystem. Our study has important implications for understanding climate extreme effects on the carbon and water cycle of the forest ecosystem.

Description: Remote Sensing, Vol. 10, Pages 1018: An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators Remote Sensing doi: 10.3390/rs10071018 Authors: Vassiliki Markogianni Dionissios Kalivas George P. Petropoulos Elias Dimitriou In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4+: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

Description: Remote Sensing, Vol. 10, Pages 1022: Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network Remote Sensing doi: 10.3390/rs10071022 Authors: Wenmin Qin Lunche Wang Aiwen Lin Ming Zhang Muhammad Bilal Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an area with low SSR, high AOD, and strong ARFB. The newly proposed AOD model may be of vital importance for improving the accuracy and computational efficiency of AOD, SSR and ARFB estimations for solar energy applications, ecological modeling, and energy policy.

Description: Water, Vol. 10, Pages 856: Rainfall Generation Using Markov Chain Models; Case Study: Central Aegean Sea Water doi: 10.3390/w10070856 Authors: Konstantinos Mammas Demetris Francis Lekkas Generalized linear models (GLMs) are popular tools for simulating daily rainfall series. However, the application of GLMs in drought-prone areas is challenging, as there is inconsistency in rainfall data during long and irregular periods. The majority of studies include regions where rainfall is well distributed during the year indicating the capabilities of the GLM approach. In many cases, the summer period has been discarded from the analyses, as it affects predictive performance of the model. In this paper, a two-stage (occurrence and amounts) GLM is used to simulate daily rainfall in two Greek islands. Summer (June–August) smooth adjustments have been proposed to model the low probability of rainfall during summer, and consequently, to improve the simulations during autumn. Preliminary results suggest that the fitted models simulate adequate rainfall occurrence and amounts in Milos and Naxos islands, and can be used as input in future hydrological applications.

Description: Water, Vol. 10, Pages 863: Research on Optimal Water Allocation Based on Water Rights Trade under the Principle of Water Demand Management: A Case Study in Bayannur City, China Water doi: 10.3390/w10070863 Authors: Lizhen Wang Yuefei Huang Yong Zhao Haihong Li Fan He Jiaqi Zhai Yongnan Zhu Qingming Wang Shan Jiang In water shortage regions, water rights trading would be much useful for increasing water use inefficiency through changing users’ water demand. In this study, a water optimal allocation modelling system is proposed by considering water rights trading and other governmental policies such as water prices, water savings and industrial policies. An agent-based model was developed to describe the behaviors and goals of individual agents using complex adaptive system theory, information transfers, and functional mechanisms between agents. The developed model was applied to Bayannur City, which suffers from severe water shortages. The water prices for different industries, the water rights transaction price, and the behaviors of various agents in 2020 were forecasted. The results reveal that the water resources optimal allocation model applied in this study can help realize the reasonable allocation of regional water resources under limited water supply. It is also valuable to guide the government in making water resources allocation policies and provide a practical reference for the formulation and adjustment of a water market transaction price.

Description: Water, Vol. 10, Pages 860: Simulation of Rain Garden Effects in Urbanized Area Based on Mike Flood Water doi: 10.3390/w10070860 Authors: Jiake Li Bei Zhang Yajiao Li Huaien Li An urban storm and surface water pollution model (MIKE FLOOD) was used to assess the impact of rain gardens on water quantity and quality for an urban area in Xi’an. After the rain garden measures were added, the results show that: (1) In the case where the total proportion of rain gardens was 2%, the overflow reduction rate was 6.74% to 65.23%, the number of overflow points reduction rate was 1.79% to 65.63%, the overload pipes reduction rate was 0% to 11.15%, the runoff reduction rate was 1.93% to 9.69%; (2) Under different rainfall conditions, the load reduction rate of suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were 2.36% to 30.35%, 2.37% to 30.11%, 2.34% to 30.08%, and 2.32% to 31.35%, respectively; (3) The submersion ranges of different submerged depths and submerged durations were reduced by 0.30% to 64.18% and 7.12% to 100%, respectively. The statistics of the automatic modeling and intelligent analysis system (AMIAS) showed that the rain garden regulation range of the waterlogging risk area was 0.78% to 100%. The rain garden has a good control effect on urban storm runoff in terms of water volume and water quality, but as the rainfall recurrence interval increases, the control effect will decrease.

Description: Water, Vol. 10, Pages 862: Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas Water doi: 10.3390/w10070862 Authors: Ana Claudia Callau Poduje Uwe Haberlandt Long and continuous series of precipitation in a high temporal resolution are required for several purposes, namely, urban hydrological applications, design of flash flood control structures, etc. As data of the temporally required resolution is often available for short period, it is advantageous to develop a precipitation model to allow for the generation of long synthetic series. A stochastic model is applied for this purpose, involving an alternating renewal process (ARP) describing a system consisting of spells that can take two possible states: wet or dry. Stochastic generation of rainfall time series using ARP models is straight forward for single site simulation. The aim of this work is to present an extension of the model to spatio-temporal simulations. The proposed methodology combines an occurrence model to define in which locations rainfall events occur simultaneously with a multivariate copula to generate synthetic events. Rainfall series registered in different regions of Germany are used to develop and test the methodology. Results are compared with an existing method in which long independent time series of rainfall events are transformed to spatially dependent ones by permutation of their order. The proposed model shows to perform as a satisfactory extension of the ARP model for multiple sites simulations.

Description: Water, Vol. 10, Pages 861: Characterization of Chromophoric Dissolved Organic Matter in the Littoral Zones of Eutrophic Lakes Taihu and Hongze during the Algal Bloom Season Water doi: 10.3390/w10070861 Authors: Bingfa Chen Wei Huang Shuzhan Ma Muhua Feng Cheng Liu Xiaozhi Gu Kaining Chen Chromophoric dissolved organic matter (CDOM) is a key component with a critical role in the littoral zones of eutrophic shallow lakes; yet the characteristics of CDOM in these zones remain seldom systematically reported. In this study, the differences in sources, biogeochemical characteristics, and fates of CDOM between the littoral zones of eutrophic lakes Taihu (LLT; frequently occurring algal blooms and longer lake residence time) and Hongze (LLH; no obvious algal blooms and shorter residence time) were compared during the algal bloom season using ultraviolet-visible spectra and excitation and emission matrix spectroscopy combined with parallel factor analysis. Three humic-like fluorescent dissolved organic matter (FDOM) components (C1, C3, and C4) and one protein-like component (C2) were identified. Results showed that FDOM components were dominated by protein-like fluorescent substances in LLT, and humic-like materials in LLH, respectively. The CDOM in LLT had a lower relative aromaticity and molecular weight, humification degree and a higher autotrophic productivity because of algal blooms. Furthermore, CDOM depletion rates in LLT were higher than those in LLH due to a longer lake residence time in LLT. In addition, CDOM shifted from high molecular weight to low molecular weight as the humification degree decreased during the CDOM depletion process. This comparative study showed that algal blooms and lake residence time were the significant factors for distinguishing characteristics of CDOM between littoral zones of shallow lakes on a similar trophic level. This study provides field-based knowledge for remote sensing CDOM measurement and serves as a reference for lakeshore aquatic environmental management.

Description: Water, Vol. 10, Pages 857: Cost-Optimal Flexibilization of Drinking Water Pumping and Treatment Plants Water doi: 10.3390/w10070857 Authors: Britta Zimmermann Hedda Gardian Kurt Rohrig This paper examines potential cost savings attained through the flexible operation of water treatment plants and drinking water pumping and the benefits gained by investing in extended water storage capacities that enhance flexibility. An existing plant in South Germany serves as an example. Mixed-integer linear programming is used to model the plant and the electricity procurement on the German spot market while ensuring the security of supply. Cost optimization considering the different cost components reveals potential cost savings through flexible operation. The paper evaluates the benefits of building additional water reservoirs and rates the financial effects of the measures used to enhance the flexibility of water pumping and treatment plants.

Description: Water, Vol. 10, Pages 859: Functional Channel Membranes for Drinking Water Production Water doi: 10.3390/w10070859 Authors: Alfredo Gonzalez-Perez Kenneth M. Persson Frank Lipnizki Drinking water production utilities rely on, among other processes, different filtration technologies like bank filtration and slow sand filters, as well as pressure, roughing, or rapid gravity filters that, together with low- and high-pressure operating membranes, help to ensure high quality drinking water for millions of customers all over the world. The global market of membrane separation technologies is projected to reach USD 11.95 Billion by 2021, encompassing water treatment, wastewater treatment, food and beverage processing, industrial gas processing, and pharmaceutical and biomedical applications. In addition to the current, polymer-based membrane separation technologies, new promising strategies using embedded functional motifs, water and ion channels, are expected to play a key role in the next generation of membranes for separation purposes, which are of paramount relevance for drinking water production utilities. In this review, we summarize the different strategies for developing new advanced membranes with a wide variety of functional motifs, like biological and artificial water and ion channels, and their possible impact on drinking water applications.

Description: Water, Vol. 10, Pages 970: Water and Nitrogen Productivity of Potato Growth in Desert Areas under Low-Discharge Drip Irrigation Water doi: 10.3390/w10080970 Authors: Pavel Trifonov Naftali Lazarovitch Gilboa Arye Narrow profit margins, resource conservation issues and environmental concerns are the main driving forces to improve fertilizer uptake, especially for potatoes. Potatoes are a high value crop with a shallow, inefficient root system and high fertilizer rate requirements. Of all essential nutrients, nitrogen (N) is often limiting to potato production. A major concern in potato production is to minimize N leaching from the root zone. Therefore, the main objective of this study was to examine the potato crop characteristics under drip irrigation with low-discharge (0.6 L h−1) and to determine the optimal combination of irrigation (40, 60, 80, and 100%) and fertigation (0, 50, and 100%) doses. In this study, the 80% (438.6 mm) irrigation dose and a 50% (50 mg N L−1) fertigation dose (W80%F50%) showed that these doses are sufficient for optimal potato yield (about 40 ton ha−1) in conjunction with water and fertilizer savings. Moreover, this treatment did not exhibit any qualitative changes in the potato tuber compared to the 100% treatments. When considering water productivity and yield, one may select a harsher irrigation regime if the available agricultural soils are not a limiting factor. Thus, higher yields can be obtained with lower irrigation and fertigation doses and a larger area.

Description: Water, Vol. 10, Pages 967: Modeling the Runoff Reduction Effect of Low Impact Development Installations in an Industrial Area, South Korea Water doi: 10.3390/w10080967 Authors: Jungho Kim Jungho Lee Yangho Song Heechan Han Jingul Joo Low-impact development (LID) methods are an important approach to storm-water mitigation. Modeling the effects of these installations using rainfall-runoff simulations can provide useful data for future design and implementation. In this study, we used the Storm Water Management Model to assess seven types of LID installations (vegetated areas, garden pots, tree filter boxes, permeable pavement, infiltration ditches, rain barrels, and infiltration blocks) at a South Korean industrial site. Using both short- and long-term simulation periods and distinct sub-basins within the study site, we were able to assess LID performance at the combined watershed, as well as at one LID facility. All LID types showed reasonable performance for storm-water runoff reduction, though rain barrels were the least effective. The effect of rainfall runoff reduction on LID facilities is changed according to rainfall depth (annual precipitation, monthly rainfall), the ratio of drainage area and facility capacity. We concluded that SWMM-LID modeling can effectively support the management of LID installations by providing additional design and planning data to better mitigate the effects of storm-water runoff.

Description: Remote Sensing, Vol. 10, Pages 1205: Examining the Accuracy of GlobCurrent Upper Ocean Velocity Data Products on the Northwestern Atlantic Shelf Remote Sensing doi: 10.3390/rs10081205 Authors: Hui Feng Douglas Vandemark Julia Levin John Wilkin This study provides a regional coastal ocean assessment of global upper ocean current data developed by the GlobCurrent (GC) project. These gridded data synthesize multiple satellite altimeter and wind model inputs to estimate both Geostrophic and Ekman-layer velocities. While the GC product was mostly devised and intended for open ocean studies, the present objective is to assess whether its data quality nearer the coast is suitable for other applications. The key ground truth sources are long-term mean and time series observations on the Northwestern Atlantic (NWA) shelf derived from Acoustic Doppler Current Profilers (ADCP) and high frequency (HF) radar networks in both the Mid-Atlantic Bight (MAB) and the Gulf of Maine (GoM). Results indicate that mean geostrophic currents across the MAB and the offshore GoM agree to roughly 10% in speed and 10 degree in direction with the in situ depth-averaged currents, with correlation levels of 0.5–0.8 at seasonal and longer time scales. Interior GoM comparisons at 5 coastal buoys show much less agreement. One likely source of GoM error is shown to be the GC mean dynamic topography near the coast. Comparison to near-surface MAB HF radar current measurements on the MAB shelf shows significant GC data improvement when including the surface Ekman term. Overall, the study results imply that application of GlobCurrent data may prove useful in coastal seas with broad continental shelves such as the MAB or Scotian shelf, but that large inaccuracies inside the GoM diminish its utility there.

Description: Water, Vol. 10, Pages 1016: Improved Mixed Distribution Model Considering Historical Extraordinary Floods under Changing Environment Water doi: 10.3390/w10081016 Authors: Jianzhu Li Yanchen Zheng Yimin Wang Ting Zhang Ping Feng Bernard A. Engel Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis.

Description: Water, Vol. 10, Pages 1011: Water Compensation and Its Implication of the Three Gorges Reservoir for the River-Lake System in the Middle Yangtze River, China Water doi: 10.3390/w10081011 Authors: Junhong Zhang Luojie Feng Lu Chen Dangwei Wang Minglong Dai Wensheng Xu Tao Yan Dam construction is an important means to improve water use efficiency and the aquatic environment. However, the flow regulation of the Three Gorges Reservoir (TGR) in the middle Yangtze River has attracted much attention because the severe drought occurred in the river-lake system downstream of the TGR. In this paper, the Dongting Lake was selected as a case study in order to detect the possible relationship between the flow regulation of the TGR and the extreme drought in the river-lake system based on a coupled hydrodynamic model. The results not only confirmed the significant role of the TGR to relieve drought in the river-lake system, but also indicated that the outflow of the TGR and the hydraulic gradient between the Zhicheng to Chenglingji stations were the crucial factors to affect the water exchange between the rivers and the Dongting Lake. The adjustment of hydraulic gradient within a proper range during the water compensation of the TGR will be an effective measure to improve the water exchange and water environment in the river-lake system. These findings present the quantitative influence of these important factors on the water exchange between rivers and lakes and provide a scientific reference for environmental and ecological management of other river-lake systems.

Description: Water, Vol. 10, Pages 1009: A Philosophical Justification for a Novel Analysis-Supported, Stakeholder-Driven Participatory Process for Water Resources Planning and Decision Making Water doi: 10.3390/w10081009 Authors: David R. Purkey Marisa Isabel Escobar Arias Vishal K. Mehta Laura Forni Nicholas J. Depsky David N. Yates Walter N. Stevenson Two trends currently shape water resources planning and decision making: reliance on participatory stakeholder processes to evaluate water management options; and growing recognition that deterministic approaches to the evaluation of options may not be appropriate. These trends pose questions regarding the proper role of information, analysis, and expertise in the inherently social and political process of negotiating agreements and implementing interventions in the water sector. The question of how one might discover the best option in the face of deep uncertainty is compelling. The question of whether the best option even exists to be discovered is more vexing. While such existential questions are not common in the water management community, they are not new to political theory. This paper explores early classical writing related to issues of knowledge and governance as captured in the work of Plato and Aristotle; and then attempts to place a novel, analysis-supported, stakeholder-driven water resources planning and decision making practice within this philosophical discourse, making reference to current decision theory. Examples from the Andes and California, where this practice has been used to structure participation by key stakeholders in water management planning and decision-making, argue that when a sufficiently diverse group of stakeholders is engaged in the decision making process expecting the discovery of the perfect option may not be warranted. Simply discovering a consensus option may be more realistic. The argument touches upon the diversity of preferences, model credibility and the visualization of model output required to explore the implications of various management options across a broad range of inherently unknowable future conditions.

Description: Remote Sensing, Vol. 10, Pages 1215: Filtering Stems and Branches from Terrestrial Laser Scanning Point Clouds Using Deep 3-D Fully Convolutional Networks Remote Sensing doi: 10.3390/rs10081215 Authors: Zhouxin Xi Chris Hopkinson Laura Chasmer Terrestrial laser scanning (TLS) can produce precise and detailed point clouds of forest environment, thus enabling quantitative structure modeling (QSM) for accurate tree morphology and wood volume allocation. Applying QSM to plot-scale wood delineation is highly dependent on wood visibility from forest scans. A common problem is to filter wood point from noisy leafy points in the crowns and understory. This study proposed a deep 3-D fully convolution network (FCN) to filter both stem and branch points from complex plot scans. To train the 3-D FCN, reference stem and branch points were delineated semi-automatically for 14 sampled areas and three common species. Among seven testing areas, agreements between reference and model prediction, measured by intersection over union (IoU) and overall accuracy (OA), were 0.89 (stem IoU), 0.54 (branch IoU), 0.79 (mean IoU), and 0.94 (OA). Wood filtering results were further incorporated to a plot-scale QSM to extract individual tree forms, isolated wood, and understory wood from three plot scans with visual assessment. The wood filtering experiment provides evidence that deep learning is a powerful tool in 3-D point cloud processing and parsing.

Description: Remote Sensing, Vol. 10, Pages 1208: Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery Remote Sensing doi: 10.3390/rs10081208 Authors: Javier Marcello Francisco Eugenio Javier Martín Ferran Marqués Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

Description: Remote Sensing, Vol. 10, Pages 1209: Fog and Low Cloud Frequency and Properties from Active-Sensor Satellite Data Remote Sensing doi: 10.3390/rs10081209 Authors: Jan Cermak An analysis of fog and low cloud properties and distribution is performed using satellite-based LiDAR. Recent years have seen great progress in the remote sensing of fog and low clouds using passive satellite-based sensors. On this basis, maps of fog distribution and frequency as well as baseline climatologies have been constructed. However, no information on fog altitude and vertical extent is available in this way, and fog/low cloud below other clouds cannot be detected in most cases. In this study, ten years of observations by the LiDAR aboard the CALIPSO (Cloud-Aerosol LiDAR and Pathfinder Satellite Observations) platform are used to construct a map and statistical evaluations of fog/low cloud distribution and properties. For the purpose of evaluation, a comparison is made to an evaluation of fog/low cloud distribution in Europe, derived from Meteosat measurements using the Satellite-Based Operation Fog Observation Scheme (SOFOS). Both maps show good agreement in spatial patterns in this region with very diverse fog formation mechanisms. It is found that fog/low cloud layers display distinct spatial differences in terms of geometrical thickness and detection accuracy. The number of fog/low cloud instances missed by passive-sensor retrievals due to multi-layer cloud situations is considerable, with clear regional differences.

Description: Remote Sensing, Vol. 10, Pages 1216: UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health Remote Sensing doi: 10.3390/rs10081216 Authors: Jonathan P. Dash Grant D. Pearse Michael S. Watt The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Description: Remote Sensing, Vol. 10, Pages 1219: Lidar Studies of Wind Turbulence in the Stable Atmospheric Boundary Layer Remote Sensing doi: 10.3390/rs10081219 Authors: Viktor A. Banakh Igor N. Smalikho The kinetic energy of turbulence, the dissipation rate of turbulent energy, and the integral scale of turbulence in the stable atmospheric boundary layer at the location heights of low-level jets (LLJs) have been measured with a coherent Doppler light detection and ranging (lidar) system. The turbulence is shown to be weak in the central part of LLJs. The kinetic energy of turbulence at the maximum velocity heights of the jet does not exceed 0.1 (m/s)2, while the dissipation rate is about 10−5 m2/s3. On average, the integral scale of turbulence in the central part of the jet is about 100 m, which is two to three times less than the effective vertical size of the LLJ.

Description: Remote Sensing, Vol. 10, Pages 1222: Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data Remote Sensing doi: 10.3390/rs10081222 Authors: Yanjun Wang Qi Chen Lin Liu Xiong Li Arun Kumar Sangaiah Kai Li Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

Description: Remote Sensing, Vol. 10, Pages 1220: Correction: Shao, Z.; et al. A Benchmark Dataset for Performance Evaluation of Multi-Label Remote Sensing Image Retrieval. Remote Sens. 2018, 10, 964 Remote Sensing doi: 10.3390/rs10081220 Authors: Zhenfeng Shao Ke Yang Weixun Zhou In our paper [1], we presented a dense labeling dataset that can be used for not only single-label and multi-label remote sensing image retrieval but also pixel-based problems such as semantic segmentation.[...]

Description: Remote Sensing, Vol. 10, Pages 1227: Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas Remote Sensing doi: 10.3390/rs10081227 Authors: Dimosthenis Traganos Bharat Aggarwal Dimitris Poursanidis Konstantinos Topouzelis Nektarios Chrysoulakis Peter Reinartz Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.

Description: Remote Sensing, Vol. 10, Pages 1229: A CNN-SIFT Hybrid Pedestrian Navigation Method Based on First-Person Vision Remote Sensing doi: 10.3390/rs10081229 Authors: Qi Zhao Boxue Zhang Shuchang Lyu Hong Zhang Daniel Sun Guoqiang Li Wenquan Feng The emergence of new wearable technologies, such as action cameras and smart glasses, has driven the use of the first-person perspective in computer applications. This field is now attracting the attention and investment of researchers aiming to develop methods to process first-person vision (FPV) video. The current approaches present particular combinations of different image features and quantitative methods to accomplish specific objectives, such as object detection, activity recognition, user–machine interaction, etc. FPV-based navigation is necessary in some special areas, where Global Position System (GPS) or other radio-wave strength methods are blocked, and is especially helpful for visually impaired people. In this paper, we propose a hybrid structure with a convolutional neural network (CNN) and local image features to achieve FPV pedestrian navigation. A novel end-to-end trainable global pooling operator, called AlphaMEX, has been designed to improve the scene classification accuracy of CNNs. A scale-invariant feature transform (SIFT)-based tracking algorithm is employed for movement estimation and trajectory tracking of the person through each frame of FPV images. Experimental results demonstrate the effectiveness of the proposed method. The top-1 error rate of the proposed AlphaMEX-ResNet outperforms the original ResNet (k = 12) by 1.7% on the ImageNet dataset. The CNN-SIFT hybrid pedestrian navigation system reaches 0.57 m average absolute error, which is an adequate accuracy for pedestrian navigation. Both positions and movements can be well estimated by the proposed pedestrian navigation algorithm with a single wearable camera.

Description: Remote Sensing, Vol. 10, Pages 1244: The Locking Depth of the Cholame Section of the San Andreas Fault from ERS2-Envisat InSAR Remote Sensing doi: 10.3390/rs10081244 Authors: Guillaume Bacques Marcello de Michele Daniel Raucoules Hideo Aochi The Cholame section of the San Andreas Fault (SAF), which has been considered locked since 1857, has been little studied using geodetic methods. In this study, we propose to use Interferometric Synthetic Aperture Radar (InSAR) to contribute to the improvement of the knowledge of this section of the SAF. In particular, the objective of this work is to provide a description of the transition between the Parkfield and Cholame-Carrizo segments further southeast by producing an estimate of the locking depth of the Cholame segment by combining ERS2 (European Remote Sensing) and Envisat Advanced SAR (ASAR) satellites data. Our results indicate that the locking depth between the Parkfield and the Cholame-Carrizo segments deepens to the southeast. We then use these results as a hint to refine the tectonic loading on this section of the SAF.

Description: Remote Sensing, Vol. 10, Pages 1240: Identifying Establishment Year and Pre-Conversion Land Cover of Rubber Plantations on Hainan Island, China Using Landsat Data during 1987–2015 Remote Sensing doi: 10.3390/rs10081240 Authors: Bangqian Chen Xiangming Xiao Zhixiang Wu Tin Yun Weili Kou Huichun Ye Qinghuo Lin Russell Doughty Jinwei Dong Jun Ma Wei Luo Guishui Xie Jianhua Cao Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China.