ALBERT

Description: Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler’s First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10–PM2.5 relationship constraint. In the site-based independent test in mainland China (2015–2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R2: 8–78%, decrease in RMSE: 14–48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.

Description: This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differentiation within three spatial contexts, including urban, rural and their combination, in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on a split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of the LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shenyang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool–heat and heat–cool island phenomena, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8 and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of the LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May, it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the combination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in an urban context compared with a rural context, while the land-cover LCZs within rural had a higher level of suitability.

Description: Accurate quantification of vertical structure (or 3D structure) and its change of a city is essential for understanding the evolution of urban form, and its social and ecological consequences. Previous studies have largely focused on the horizontal structure (or 2D structure), but few on 3D structure, especially for long time changes, due to the absence of such historical data. Here, we present a new approach for 3D reconstruction of urban history, which was applied to characterize the urban 3D structure and its change from 1986 to 2017 in Shenzhen, a megacity in southern China. This approach integrates the contemporary building height obtained from the increasingly available data of building footprint with building age estimated based on the long-term observations from time-series Landsat imagery. We found: (1) the overall accuracy for building change detection was 87.80%, and for the year of change was 77.40%, suggesting that the integrated approach provided an effective method to cooperate horizontal (i.e., building footprint), vertical (i.e., building height), and temporal information (i.e., building age) to generate the historical data for urban 3D reconstruction. (2) The number of buildings increased dramatically from 1986 to 2017, by eight times, with an increased proportion of high-rise buildings. (3) The old urban areas continued to have the highest density of buildings, with increased average height of buildings, but there were two emerging new centers clustered with high-rise buildings. The long-term urban 3D maps allowed characterizing the spatiotemporal patterns of the vertical dimension at the city level, which can enhance our understanding on urban morphology.

Description: The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.

Description: Monitoring of land use, land-use changes, and forestry (LULUCF) plays a crucial role in biodiversity and global environmental challenges. In 2015, the Food and Agriculture Organization of the United Nations (FAO) launched the Global Forest Survey (GFS) integrating medium- (MR) and very-high-resolution (VHR) images through the FAO’s Collect Earth platform. More than 11,150 plots were inventoried in the Temperate FAO ecozone in Europe to monitor LULUCF from 2000 to 2015. As a result, 2.19% (VHR) to 2.77% (MR/VHR) of the study area underwent LULUCF, including a 0.37% (VHR) to 0.43% (MR/VHR) net increase in forest lands. Collect Earth and VHR images have also (i) allowed for shaping a preliminary structure of the land-use network, showing that cropland was the land type that changed most and that cropland and grassland were the more frequent land uses that generated new forest land, (ii) shown that, in 2015, mixed and monospecific forests represented 44.3% and 46.5% of the forest land, respectively, unlike other forest sources, and (iii) shown that 14.9% of the area had been affected by disturbances, particularly wood harvesting (67.47% of the disturbed forests). According to other authors, the area showed a strong correlation between canopy mortality and reported wood removals due to the transition from past clear-cut systems to “close-to-nature” silviculture.

Description: In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94≤R2≤0.98, 5.37≤RMSE≤7.9 and 20≤MAE≤33.

Description: Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Description: The Fabry–Pérot interferometer (FPI) and meteor radar are two important techniques for measuring the horizontal wind field in mesopause region, the observations of which still lack comprehensive comparison. Kunming Observatory (25.6°N,103.8°E) has deployed both instruments in recent years and provides collocated meteor radar and FPI observations. The meteor radar measures the horizontal wind fields over 24 hours every day continuously, whereas the FPI can only work during the night with clear air condition. FPI horizontal wind data from the 892.0-nm airglow emission (with a peak height at ~87 km) from 26 January to 8 February 2019 were comparatively analyzed with simultaneous meteor radar observations, which cover the range between 80 and 90 km with a vertical resolution of 1.8 km. It was found that the temporal variations in the horizontal wind fields observed by the FPI and meteor radar were generally consistent with one another, with the highest 2-D correlation coefficients of 0.91 (0.88) at 88 (87) km for the meridional (zonal) wind, which agreed with the peak height of OH airglow emission observed by the TIMED/SABER instrument. In addition, the correlation coefficient for the weighted meteor radar horizontal wind by OH concentration between 86 and 88 km and 85 and 89 km increased slightly from 0.91 (0.89) to 0.92 (0.89) for the meridional (zonal) wind, which indicated the contribution of OH concentration beyond the peak height to the FPI wind observations. We also found that the absolute horizontal wind values detected by two instruments were linearly correlated with a slope of ~1.3 for both wind components, and meteor radar wind observations were usually larger than the FPI observations.

Description: Yilan Bay is in the northeast corner of Taiwan at the junction of the East China Sea (ECS) and the Pacific Ocean. This study clarified the composition of water masses adjacent to Yilan Bay. The upper seawater in the bay is characterized by Kuroshio surface water, Taiwan warm current water, and shelf mixed water masses. The flow field in this area is mainly determined by the inter-actions among the northeastern Taiwan countercurrent, Kuroshio Current (KC), and tidal currents. The fall season is the main rainfall period in Yilan Bay, which causes a large amount of river runoff and a further increase in chlorophyll concentration, and the salinity of the upper water layer is observed much lower than other seasons. Water with a high chlorophyll concentration can flow into the ECS with ebb currents and the KC with ebb and flood currents. Combining hourly geosynchronous ocean color imager data and numerical simulation flow field helps us understand short-term changes of chlorophyll concentration. The trajectories of the drifters and virtual particle simulations help us understand the sources and movement of ocean currents in Yilan Bay. The seasonal swing of the KC path outside the bay is an important factor affecting the flow field and hydrological characteristics.

Description: In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution operation on regular-grid image regions with a fixed kernel size and as a result, they neglect the inherent relation between HSI data. In recent years, graph convolutional networks (GCN) used for data representation in a non-Euclidean space, have been successfully applied to HSI classification. However, conventional GCN methods suffer from a huge computational cost since they construct the adjacency matrix between all HSI pixels, and they ignore the local spatial context information of hyperspectral images. To alleviate these shortcomings, we propose a novel method termed spectral-spatial offset graph convolutional networks (SSOGCN). Different from the usually used GCN models that compute the adjacency matrix between all pixels, we construct an adjacency matrix only using pixels within a patch, which contains rich local spatial context information, while reducing the computation cost and memory consumption of the adjacency matrix. Moreover, to emphasize important local spatial information, an offset graph convolution module is proposed to extract more robust features and improve the classification performance. Comprehensive experiments are carried out on three representative benchmark data sets, and the experimental results effectively certify that the proposed SSOGCN method has more advantages than the recent state-of-the-art (SOTA) methods.