Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R²=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1^∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R² and RMSE values of 0.42 and 0.087 m³ m⁻³), while the overall R² and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m³ m⁻³), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30^∘ S–30^∘ N) but mostly in winter over the mid-latitudes (30–60^∘ N, 30–60^∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

To quantify the ecological consequences of recent nationwide restoration efforts in China, spatially explicit information on forest biomass carbon stock changes over the past 20 years is critical. However, long-term biomass tracking at the national scale remains challenging as it requires continuous and high-resolution monitoring. Here, we characterize the changes in the above- and belowground biomass carbon (AGBC and BGBC) of forests in China between 2002 and 2021 at 1 km spatial resolution by integrating multiple types of remote sensing observations with intensive field measurements through regression and machine learning approaches. On average, 8.6 ± 0.6 and 2.2 ± 0.1 PgC were stored in above- and belowground live forests in China. Over the last 20 years, the total forest biomass carbon pool in China has increased at a rate of 114.5 ± 16.3 TgC yr⁻¹ (approximately 1.1 % yr⁻¹). The most pronounced forest biomass carbon stock gains occurred in central to southern China, including the southern Loess Plateau, Qinling mountains, southwestern karsts and southeastern forests. While the combined use of multi-source remote sensing data provides a powerful tool to assess the forest biomass carbon changes, future research is also needed to explore the drivers of the observed woody biomass trends and to evaluate the degree to which biomass gains will translate into biodiverse, healthy ecosystems that are sustainable. Annual forest above- and belowground biomass maps for China are now available at https://doi.org/10.6084/m9.figshare.21931161.v1 (Chen, 2023).

Under current global warming and accelerated population growth scenarios, cropland irrigation water consumption has become a central issue limiting the sustainability of coupled human‐natural systems. This study proposes a new estimate of recent global cropland water consumption constrained by observations and provides attributions for its recent trend. By incorporating observations, including extracted cropland leaf area index and irrigation threshold data, this study provides improved estimates of recent global cropland evapotranspiration and transpiration as well as irrigation water consumption and withdrawal. The global annual consumption and withdrawal of irrigation water are estimated to be approximately 874 and 1,867 km³ (in 2005), respectively. From 2000 to 2014, a rapid increase in cropland irrigation was detected, especially in water‐deficient areas (i.e., hyperarid, arid, and semiarid regions). Climate change, which mainly consists of rising temperature and reduced moisture conditions, is usually distinguished as the major driving factor. Human‐induced increases in crop canopy cover have also contributed to more irrigation in hyperarid and arid regions. This study also provides suggestions for water‐savings‐targeted cropland management in water‐deficient areas based on the transpiration ratio (i.e., ratio of transpiration to evapotranspiration).

Universal and equitable access to affordable safely managed drinking water (SMDW) is a significant challenge and is highlighted by the United Nations’ Sustainable Development Goals-6.1. However, SMDW coverage by 2030 is estimated to reach only 81% of the global population. Solar water evaporation (SWE) represents one potential method to ensure decentralized water purification, but its potential for addressing the global SMDW challenge remains unclear. We use a condensation-enhanced strategy and develop a physics-guided machine learning model for assessing the global potential of SWE technology to meet SMDW demand for unserved populations without external electricity input. We find that a condensation-enhanced SWE device (1 m²) can supply enough drinking water (2.5 L day⁻¹) to 95.8% of the population lacking SMDW. SWE can help fulfill universal SMDW coverage by 2030 with an annual cost of 10.4 billion U.S. dollars, saving 66.7% of the current investment and fulfilling the SDG-6.1 goal.

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.