Leishi Chen

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data.

In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km². The scale of this hazard underscores a critical need for long-term stability assessment of the affected slopes. While previous studies have primarily used optical remote sensing to map landslide distributions, quantitative evaluation of post-failure movement dynamics remains limited. This study developed an integrated monitoring framework that combines time-series SBAS-InSAR displacement measurements (using Sentinel-1 data from August 2024 to September 2025) with deep learning-based optical interpretation, rainfall analysis, and geological data. Our approach enables the quantitative, region-scale stability assessment of the Zixing landslide cluster one year after the initial event. Experimental results reveal sustained surface displacement with rates ranging from −30 to 30 mm/year, and localized displacements exceeding 40 mm/year. Notably, over 48% of the mapped landslides are classified as active or critically active, indicating widespread, ongoing instability. Correlation analysis further establishes precipitation as a key driver of accelerated movement. Beyond the Zixing case, this work provides a transferable methodology for assessing long-term post-disaster landslide behavior, offering direct value for regional hazard management and early-warning systems.

Land surface temperature (LST) is fundamental for monitoring surface energy balance and environmental dynamics, with remote sensing providing the primary means of acquisition. However, directional anisotropy (DA) introduces systematic bias in satellite-derived LST products, particularly over complex landscapes. This study examines the impact of angular effects on LST retrievals from three leading East Asian geostationary satellites (FengYun 4A, FengYun 4B, and Himawari 9) across Hunan Province, China, using integrated comparison with in situ measurements and reanalysis data. Results show that all products exhibit a systematic cold bias, with FY4B achieving the highest accuracy. Diurnal retrieval precision increases with higher solar zenith angles (SZA), while no consistent relationship is observed between viewing zenith angle (VZA) and retrieval accuracy. Notably, the retrieval bias of the FY4 series increases significantly when the sun and sensor are aligned in azimuth, particularly when the relative azimuth angle (RAA) is less than or equal to 30°. Parametric modeling reveals that emissivity kernel-induced anisotropy is the principal driver of significant LST deviations in central Hunan, while solar kernel effects result in LST overestimation in mountainous regions and underestimation in plains. Increases in elevation or vegetation density reduce emissivity-induced errors but amplify errors caused by shadowing and sunlit effects. Emissivity anisotropy is thus identified as the primary source of LST DA. These findings deepen the understanding of LST DA in remote sensing and provide essential guidance for refining retrieval algorithms and improving the applicability of LST products in complex terrains.

Wetlands have functions such as hydrological regulation, water purification, and climate regulation. However, under the dual influence of global climate change and human activities, the frequency and intensity of wetland ecological droughts are increasing, leading to shrinkage of wetland area and decline in ecological service functions, seriously threatening wetland ecological security. The East Dongting Lake Wetland is located in Hunan, China. According to the observation results of the Chenglingji Water Level Station, from August 2, 2022 to June 5, 2023, it entered an abnormal dry period of 305 days, which is the longest dry period since observation records began. Our goal is to understand the changes in water distribution in East Dongting Lake during this period based on satellite images.We used the Sentinel-1 GRD data provided on Google Earth Engine and developed a water area monitoring module based on the OTSU method, which provided us with water distribution data of the East Dongting Lake wetland during the target period. There are 44 available Sentinel-1 GRD images in these 305 days. The average water area in the dry season calculated from the 44 images is 230.75 square kilometers. In contrast, there were 49 available images in the same period of the previous year, and the average water area was 486.32 square kilometers. The average water area in the same period of the previous year was even larger than the maximum water area of 369.06 square kilometers during the target period. This reflects the continued impact of the severe drought in China's Yangtze River Basin in the second half of 2022 on the East Dongting Lake wetland.We analyzed the water distribution results on August 20, 2022, when the drought was severe. The results showed that contiguous exposed lake beds were exposed in the north and south of the main lake body of East Dongting Lake, and the main flood channel even experienced drying out. Drought has affected the core area of the East Dongting Lake Wetland, causing the water level to drop rapidly into the dry season, resulting in rapid changes in the spatial distribution of the Dongting Lake Wetland's shoals and water bodies. The advance of the dry season mainly affects the seasonal growth of wetland vegetation, including submersed plants (Echinacea, etc.), floating-leaf plants (Rhombus, etc.), emergent plants (Reeds, Typha, etc.), swampy meadows (Carex etc.) and swamp grasses (Nandi, etc.). The drought has even affected the habitat of elk and has had a complex impact on wetland ecology.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. We conducted a comprehensive evaluation and refined analysis of surface heat resources in Hunan Province, central China, over a two-year period using the 4 km/1 h resolution product in this study. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R = 0.893), albeit with a relatively high error level (Bias = −6.295 °C; RMSE = 8.58 °C), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang–Zhu–Tan urban agglomeration over two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite’s detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. In this study, we evaluated the performance of the product in Hunan Province and conducted refined analysis of surface heat resources based on the 4km/1h resolution product over a two-year period. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R=0.893), albeit with a relatively high error level (Bias=-6.295℃; RMSE=8.58℃), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by the terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang-Zhu-Tan urban agglomeration during two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite's detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generation of Chinese geostationary satellites, and the detection product of the satellite has not been extensively validated. Therefore, it is important to conduct a comprehensive assessment of this product. In this study, the performance of the FY-4A LST product in the Hunan Province was authenticity tested with in situ measurements, triple collocation analyzed with reanalysis products, and impact analyzed with environmental factors. The results confirm that FY-4A captures LST well (R = 0.893, Rho = 0.915), but there is a general underestimation (Bias = −0.6295 °C) and relatively high random error (RMSE = 8.588 °C, ubRMSE = 5.842 °C). In terms of accuracy, FY-4A LST is more accurate for central-eastern, northern, and south-central Hunan Province and less accurate for western and southern mountainous areas and Dongting Lake. FY-4A LST is not as accurate as Himawari-8 LST; its accuracy also varies seasonally and between day and night. The accuracy of FY-4A LST decreases as elevation, in situ measured LST, surface heterogeneity, topographic relief, slope, or NDVI increase and as soil moisture decreases. FY-4A LST is also more accurate when the land cover is cultivated land or artificial surfaces or when the landform is a platform for other land covers and landforms. The conclusions drawn from the comprehensive analysis of the large quantity of data are generalizable and provide a quantitative baseline for assessing the detection capability of the FY-4A satellite, a reference for determining improvement in the retrieval algorithm, and a foundation for the development and application of future domestic satellite products.