Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. (2) An effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. (3) The method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.

In recent years, the increased intensity and duration of droughts have dramatically altered the structure and function of grassland ecosystems, which have been forced to adapt to this change in climate. Combinations of global change drivers such as elevated atmospheric CO₂ concentration, warming, nitrogen (N) deposition, grazing, and land-use change have influenced the impact that droughts have on grassland C cycling. This influence, to some extent, can modify the relationship between droughts and grassland carbon (C) cycling in the multi-factor world. Unfortunately, prior reviews have been primarily anecdotal from the 1930s to the 2010s. We investigated the current state of the study on the interactive impacts of multiple factors under drought scenarios in grassland C cycling and provided scientific advice for dealing with droughts and managing grassland C cycling in a multi-factor world. Currently, adequate information is not available on the interaction between droughts and global change drivers, which would advance our understanding of grassland C cycling responses. It was determined that future experiments and models should specifically test how droughts regulate grassland C cycling under global changes. Previous multi-factor experiments of current and future global change conditions have studied various drought scenarios poorly, including changes in precipitation frequency and amplitude, timing, and interactions with other global change drivers. Multi-factor experiments have contributed to quantifying these potential changes and have provided important information on how water affects ecosystem processes under global change. There is an urgent need to establish a systematic framework that can assess ecosystem dynamic responses to droughts under current and future global change and human activity, with a focus on the combined effects of droughts, global change drivers, and the corresponding hierarchical responses of an ecosystem.

Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area.

The Surface Water and Ocean Topography (SWOT) satellite mission, jointly developed by NASA and several international collaboration agencies, aims to achieve high-resolution two-dimensional observations of global surface water. Equipped with the advanced Ka-band radar interferometer (KaRIn), it significantly enhances the ability to monitor surface water and provides a new data source for obtaining large-scale water surface elevation (WSE) data at high temporal and spatial resolution. However, the accuracy and applicability of its scientific data products for inland water bodies still require validation. This study obtained three scientific data products from the SWOT satellite between August 2023 and December 2024: the Level 2 KaRIn high-rate river single-pass vector product (L2_HR_RiverSP), the Level 2 KaRIn high-rate lake single-pass vector product (L2_HR_LakeSP), and the Level 2 KaRIn high-rate water mask pixel cloud product (L2_HR_PIXC). These were compared with in situ water level data to validate their accuracy in retrieving inland water levels across eight different regions in the middle and lower reaches of the Yangtze River (MLRYR) and to evaluate the applicability of each product. The experimental results show the following: (1) The inversion accuracy of L2_HR_RiverSP and L2_HR_LakeSP varies significantly across different regions. In some areas, the extracted WSE aligns closely with the in situ water level trend, with a coefficient of determination ($R^2$) exceeding 0.9, while in other areas, the $R^2$ is lower (less than 0.8), and the error compared to in situ water levels is larger (with Root Mean Square Error (RMSE) greater than 1.0 m). (2) This study proposes a combined denoising method based on the Interquartile Range (IQR) and Adaptive Statistical Outlier Removal (ASOR). Compared to the L2_HR_RiverSP and L2_HR_LakeSP products, the L2_HR_PIXC product, after denoising, shows significant improvements in all accuracy metrics for water level inversion, with $R^2$ greater than 0.85, Mean Absolute Error (MAE) less than 0.4 m, and RMSE less than 0.5 m. Overall, the SWOT satellite demonstrates the capability to monitor inland water bodies with high precision, especially through the L2_HR_PIXC product, which shows broader application potential and will play an important role in global water dynamics monitoring and refined water resource management research.

Soil moisture is a critical state variable in land–atmosphere interactions and the hydrological cycle. Owing to its all-weather and all-day observation capability, passive microwave remote sensing has become an important technique for regional soil moisture monitoring. However, most existing passive microwave soil moisture retrieval methods rely on fixed empirical parameters to characterize vegetation single-scattering albedo and soil surface roughness, which may not fully account for variations in surface conditions across different regions and seasons, thereby affecting retrieval accuracy. To address this issue, this study proposes a method for jointly constraining key parameters of the forward model for passive microwave soil moisture retrieval using multi-temporal brightness temperature observations. The vegetation single-scattering albedo ($\omega$) and soil surface roughness parameter ($rou$) are determined from multi-temporal brightness temperature information, and soil moisture is subsequently retrieved based on the optimized parameters. The Shandian River Basin was selected as the study area, and soil moisture retrievals were conducted using SMAP SPL3SMP brightness temperature data from 2019 to 2024. The retrieval results were evaluated using ground-based observations and compared with existing soil moisture products. The results show that: (1) The proposed parameterization method is theoretically capable of identifying the vegetation single-scattering albedo and soil surface roughness parameter across their respective parameter ranges. Based on the parameterization results in the study area, the vegetation single-scattering albedo exhibits a pronounced and relatively consistent annual pattern, with a temporal trend generally consistent with previous studies, whereas the intra-annual variation in the soil surface roughness parameter is not pronounced and cannot be reliably identified from the current results. (2) Validation against the ground-based soil moisture observation network shows that the proposed method achieves an overall RMSE, ubRMSE, MRE, and Bias of 0.0678, 0.0505, 0.3085, and −0.0453, respectively, and generally outperforms the DCA and SCA products. Compared with MCCA, the proposed method has a slightly higher ubRMSE (0.0505 vs. 0.0496) but a Bias closer to zero (−0.0453 vs. −0.0504). Consequently, its overall RMSE is lower than that of MCCA (0.0678 vs. 0.0707). These results suggest that the proposed method has the potential to reduce systematic errors while maintaining a level of random error comparable to that of existing products. (3) Spatial analysis demonstrates that the retrieved soil moisture patterns are consistent with the general spatial distribution characteristics of the study area. Compared with the MCCA, DCA, SCA-H, and SCA-V products, the proposed method exhibits stronger spatial gradients and provides clearer differentiation among regions with different moisture conditions. Overall, the proposed multi-temporal brightness temperature constraint method demonstrates good feasibility for passive microwave soil moisture retrieval and provides a new technical approach for determining key parameters in the forward model.

The difficulty in accurately determining channel Manning’s roughness has long constrained the accuracy of river flood simulation. At present, Manning’s roughness is mainly assigned using empirical approaches such as lookup tables and visual comparison, which are insufficient to represent the spatial heterogeneity of roughness in natural rivers. Remote sensing provides valuable data support for flood simulation in areas with sparse hydrological gauges. Remote-sensing observations are objective and can be acquired over large areas, but they are temporally discontinuous, whereas hydrodynamic models provide physically meaningful and temporally continuous simulations. These two information sources are therefore naturally complementary, providing a new avenue for Manning’s roughness inversion through remote sensing and hydrodynamic model coupling. To address these challenges, this study proposes a new remote sensing and hydrodynamic model coupling framework centered on roughness inversion. SAR-derived inundation extent is used as a spatial constraint, and an objective function together with particle swarm optimization (PSO) drives an iterative closed loop between the one-dimensional HEC-RAS model and the remote-sensing constraint to autonomously approach the optimal roughness parameters and improve the reliability of flood simulation. To bridge the scale mismatch between one-dimensional hydrodynamic outputs and two-dimensional SAR observations, the HEC-RAS cross-section geometry TIN structure is used to spatialize the simulated water surface, enabling direct comparison between simulated and SAR-observed inundation extents. The Le’an River in Wuyuan, Jiangxi Province, China, was selected as the test reach to evaluate the applicability and reliability of the framework under sparse-gauge conditions. The model was calibrated using the SAR-derived inundation extent, and simulated water levels were subsequently compared with rating-curve-derived reference levels. Under the SAR inundation constraint, the agreement between simulated and observed inundation extents improved, with IoU increasing from 0.763 under the initial parameter set to 0.789. Comparison with rating-curve-derived reference water levels further showed that the optimized Manning’s roughness parameters improved water-level simulation, reducing RMSE from 0.811 m to 0.594 m and increasing NSE from 0.950 to 0.973. These results demonstrate the effectiveness of the proposed method when continuous hydrological observations are unavailable. The framework provides an automated remote-sensing-constrained approach for Manning’s roughness inversion in hydrodynamic models of small and medium-sized rivers with limited in situ hydrological data.