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The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current lack of comparative studies on vegetation changes in various countries in the GMS. Based on the MODIS normalized difference vegetation index (NDVI) time series data, this study analyzed the spatiotemporal patterns of vegetation coverage and their trends in the GMS from 2000 to 2022 using the Theil–Sen slope estimation, the Mann–Kendall mutation test, and the gravity center migration model. The key findings were as follows: (1) the NDVI in the GMS showed an overall upward fluctuating trend over the past 23 years, with an annual growth rate of 0.11%. The NDVI changes varied slightly between seasons, with the greatest increases recorded in summer and winter. (2) The spatial distribution of NDVI in the GMS varied greatly, with higher NDVI values in the north–central region and lower NDVI values in the south. (3) A total of 66.03% of the GMS area showed increments in vegetation during the studied period, mainly in south–central Myanmar, northeastern Thailand, Vietnam, and China. (4) From 2000 to 2022, the gravity center of vegetation greenness shifted northward in the GMS, especially from 2000 to 2005, indicating that the growth rates of vegetation in the north–central part of the GMS were higher than those in the south. Furthermore, the vegetation coverage in all countries, except Cambodia, increased, with the most pronounced growth recorded in China. Overall, these findings can provide scientific evidence for the GMS to enhance ecological protection and sustainable development. [ABSTRACT FROM AUTHOR]

By changing the physicochemical and biological properties of soil, erosion profoundly affects soil nitrogen levels, but knowledge about the erosion impact on soil nitrogen (N) dynamics is still rather incomplete. We compared soil N contents at the early stage of vegetation self-restoration in response to soil erosion thickness (0, 10, 20, 30 and 40 cm), by conducting a simulated erosion experiment on sloping arable land in the dry-hot valley of Yunnan Province, southwestern China. The results showed total nitrogen (TN), ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) contents reduced with increasing soil erosion thickness and decreased significantly at the soil erosion thickness of 10, 40 and 10 cm in the rainy season and 30, 10 and 10 cm in the dry season compared with 0 cm. Structural equation modeling (SEM) indicated that soil erosion thickness and seasonal variation were the important drivers of mineral nitrogen (NH4+-N and NO3−-N) content. Soil erosion thickness indirectly affected mineral nitrogen through negative on TN, carbon content and Diazotrophs (nifH genes). Dry–wet season change had an effect on mineral nitrogen mediated by arbuscular mycorrhizal fungi (AMF) and nifH genes. We also found AMF had a promotion to nifH genes in eroded soil, which can be expected to benefit nitrogen fixing. Our findings highlight the importance of considering soil erosion thickness and sampling time for nitrogen dynamics, in particular, the investigation of nitrogen limitation, in the early stage of vegetation self-restoration. [ABSTRACT FROM AUTHOR]

High‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China. Plain Language Summary: Reservoirs can supply water, control flood and provide electricity. Without high temporal frequency data on inundated area and water storage over a large spatial extent, we could not clearly understand how and to which degree numerous reservoirs have altered the natural water cycle and provided multiple benefits to people all across the world. The development of remote sensing technology has facilitated the long‐term monitoring of inundated areas and water levels of large quantities of reservoirs. However, high‐temporal‐resolution monitoring remains challenging. In this study, by developing a novel method which integrates active microwave remote sensing data with optical remote sensing observations, we largely improved the satellite‐based monthly inundated area and water storage change monitoring at almost all large reservoirs in China. The results reveal distinct seasonal patterns and interannual variations of reservoir water in different regions of China. Specifically, we found that reservoirs in Northeast China, the Yellow River basin, and Southwest China generally expanded, whereas those in Eastern and Northwest China generally shrank during 2017–2021. The climatic and anthropogenic impacts on the inter‐ and intra‐annual variations in reservoir water across different regions were also analyzed. Key Points: A new methodology for integrating synthetic aperture radar backscattering coefficients with optical remote sensing‐based inland surface water dynamics is developedMonthly inundated area and water storage changes of 721 large reservoirs in China during 2017–2021 are retrieved at improved accuracyImpacts of natural climate and human control on the inter‐ and intra‐annual variations in reservoir water across different regions in China are analyzed [ABSTRACT FROM AUTHOR]

The risk of water erosion in mainland China is intensifying due to climate change. A high-precision rainfall erosivity dataset is crucial for revealing the spatiotemporal patterns of rainfall erosivity and identifying key areas of water erosion. However, due to the insufficient spatiotemporal resolution of historical precipitation data, there are certain biases in the estimation of rainfall erosivity in China, especially in regions with complex terrain and climatic conditions. Over the past decade, the China Meteorological Administration has continuously improved its ground-based meteorological observation capabilities, forming a dense network of ground-based observation stations. These high-precision precipitation data provide a solid foundation for quantifying the patterns of rainfall erosivity in China. In this study, we first performed rigorous quality control on the 1-minute ground observation precipitation data from nearly 70,000 stations nationwide from 2014 to 2022, ultimately selecting 60,129 available stations. Using the precipitation data from these stations, we calculated event rainfall erosivity and generated a national mean annual rainfall erosivity dataset with a spatial resolution of 0.25°. This dataset shows that the mean annual rainfall erosivity in mainland China is approximately 1241 MJ·mm·ha−1·h−1·yr−1, with areas exceeding 4000 MJ·mm·ha−1·h−1·yr−1 mainly concentrated in the southern China and southern Tibetan Plateau. Compared to our study, previously released datasets overestimate China's mean annual rainfall erosivity by 31 %~65 %, and there are significant differences in performance across different river basins. In summary, the release of this dataset facilitates a more accurate assessment of the current water erosion intensity in China. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.301206 ; Chen, 2024). [ABSTRACT FROM AUTHOR]

The mapping and dynamic monitoring of large-scale cropland erosion rates are critical for agricultural planning but extremely challenging. In this study, using field investigation data collected from 20,155 land parcels in 2817 sample units in the National Soil Erosion Survey, as well as land use change data for two decades from the National Land Use/Cover Database of China (NLUD-C), we proposed a new point-to-surface approach to quantitatively assess long-term cropland erosion based on the CSLE model and non-homologous data voting. The results show that cropland in Yunnan suffers from serious problems, with an unsustainable mean soil erosion rate of 40.47 t/(ha·a) and an erosion ratio of 70.11%, which are significantly higher than those of other land types. Engineering control measures (ECMS) have a profound impact on reducing soil erosion; the soil erosion rates of cropland with and without ECMs differ more than five-fold. Over the past two decades, the cropland area in Yunnan has continued to decrease, with a net reduction of 7461.83 km2 and a ratio of −10.55%, causing a corresponding 0.32 × 108 t (12.12%) reduction in cropland soil loss. We also quantified the impact of different LUCC scenarios on cropland erosion, and extraordinarily high variability was found in soil loss in different basins and periods. Conversion from cropland to forest contributes the most to cropland erosion reduction, while conversion from grassland to cropland contributes 56.18% of the increase in soil erosion. Considering the current speed of cropland regulation, it is the sharp reduction in land area that leads to cropland erosion reduction rather than treatments. The choice between the Grain for Green Policy and Cropland Protecting Strategy in mountainous areas should be made carefully, with understanding and collaboration between different roles. [ABSTRACT FROM AUTHOR]