Various surface water bodies, such as rivers, lakes and reservoirs, provide water and essential services to human society. However, the long-term spatiotemporal dynamics of different types of surface water bodies and their possible driving factors over large areas remain very limited. Here, we used unprecedented surface water data layers derived from all available Landsat images and further developed two databases on China’s lakes and reservoirs larger than 1 km² to document and understand the characteristics of changes in different water body types during 2000 to 2019 in China. Our results show that China is dominated by permanent water bodies. The areas of permanent and seasonal water bodies in China increased by 16,631.02 km² (16.72%) and 16,994.95 km² (25.14%), respectively, between 2000 and 2019, with permanent and seasonal water bodies exhibiting divergent spatial variations. Lakes and artificial reservoirs larger than 1 km², which collectively represent a significant proportion of the permanent water bodies in China, displayed net increases of 6884.52 km² (10.71%) and 4075.13 km² (36.10%), respectively, from 2000 to 2019; these increases accounted for 41.40% and 24.50%, respectively, of the total permanent water body increment. The expanding lakes were mainly distributed on the Tibetan Plateau, whereas the rapidly growing reservoirs were mainly located on the Northeast Plain and Eastern Plain. Statistical analyses indicated that artificial reservoirs were an important factor controlling both permanent and seasonal water body changes in most of provinces. Climate factors, such as precipitation and temperature, were the main influencing factors affecting the changes in different water bodies in the sparsely populated Tibetan Plateau.

Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R²) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning.

The Mun River Basin is one of Thailand’s major grain-producing areas, but the production is insufficient, and most of the cultivated lands are rain-fed and always unused in the dry season. All this makes it necessary to determine the status of soil nutrients and soil quality in the dry season to improve soil conditions, which will be useful for cultivation in the farming period. The aim of this study was to construct a soil-quality assessment based on soil samples, and in the process the minimum data set theory was introduced to screen the assessment indicators. The geographically weighted regression method was used to complete the spatial interpolation process of indicators, and the fuzzy logic model was constructed to evaluate the soil quality. The results showed that the spatial distributions of soil quality and indicators were similar. The soil quality was the best in the upstream while poor in the downstream, and the dry fields in the west and the forests in the east of the basin were better than other areas nearby. However; the soil qualities of paddy fields in the middle and east of the basin were poor due to the lack of soil nutrient supply when the fields were unused