The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m⁻² per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m⁻² per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 ± 26.31 g C m⁻² per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.

The carbon release and transport in rivers are expected to increase in a warming climate with enhanced melting. We present a continuous dataset of DOC in the river, precipitation, and groundwater, including air temperature, discharge, and precipitation in the source region of the Yangtze River (SRYR). Our study shows that the average concentrations of DOC in the three end-members are characterized as the sequence of groundwater > precipitation > river, which is related to the water volume, cycle period, and river flow speed. The seasonality of DOC in the river is observed as the obvious bimodal structure at Tuotuohe (TTH) and Zhimenda (ZMD) gauging stations. The highest concentration appears in July (2.4 mg L⁻¹ at TTH and 2.1 mg L⁻¹ at ZMD) and the secondary high value (2.2 mg L⁻¹ at TTH 1.9 mg L⁻¹ at ZMD) emerges from August to September. It is estimated that 459 and 6751 tons of DOC are transported by the river at TTH and ZMD, respectively. Although the wet deposition flux of DOC is nearly ten times higher than the river flux, riverine DOC still primarily originates from soil erosion of the basin rather than precipitation settlement. Riverine DOC fluxes are positively correlated with discharge, suggesting DOC fluxes are likely to increase in the future. Our findings highlight that permafrost degradation and glacier retreat have a great effect on DOC concentration in rivers and may become increasingly important for regional biogeochemical cycles.

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

The Carnegie-Ames-Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scale. However, the CASA is still driven by multi-source data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data, can conveniently provide real-time surface information at the regional scale, thus replacing ground observation data to drive CASA model. We attempted to improve the CASA model in this study using DEM data derived from radar RS and RS products data generated from Moderate Resolution Imaging Spectroradiometer satellite sensor. We applied it to simulate the NPP of alpine grasslands in Qinghai Lake Basin, which is located in the northeastern Qinghai-Tibetan Plateau, China. The accuracy of the RS data driven CASA, with mean absolute percent error (MAPE) of 23.32 % and root mean square error (RMSE) of 26.26 g C•m^-2•month^-1, was higher than that of the multi-source data driven CASA, with MAPE of 49.08 % and RMSE of 65.21 g C•m^-2•month^-1. The NPP simulated by RS data driven CASA in July 2020 shows an average value of 110.17 ± 26.25 g C•m^-2•month^-1, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapidly simulating grassland, farmland, forest, and other vegetation NPP to satisfy the requirements of precision agriculture, precision livestock farming, accounting carbon stocks, and other applications.

Long-term ecological monitoring is essential for sustainable management in fragile regions. This study assessed four decades (1986–2024) of ecological evolution in the Ji River Basin—a transitional loess-gully ecosystem in China's Yellow River Basin—using the Remote Sensing Ecological Index (RSEI). We integrated multi-temporal Landsat images via Google Earth Engine to construct a 40-year RSEI time series, coupling greenness (NDVI), wetness (WET), heat (LST), and dryness (NDBSI) through principal component analysis (PC1 explained >82% variance). Three evolutionary phases emerged: initial degradation (1986–1996) driven by slope cropland expansion; stabilization (1996–2006) coinciding with early Grain for Green policies; and sustained recovery (2006–2024) as high-quality zones expanded 85%. Resilience mapping delineated three functional zones: High-Resilience refugia (16.84%) in highlands with stable RSEI (CV300m) and restoration projects as primary post-2006 drivers, while urbanization constrained lowland recovery. This study demonstrates RSEI's effectiveness for monitoring transitional loess ecosystems and provides a replicable framework for sustainable basin management, contributing to SDG 15 implementation in semi-arid regions globally.

Long-term ecological monitoring is essential for sustainable management in fragile regions. This study assessed four decades (1986–2024) of ecological evolution in the Ji River Basin—a 1276.64 km² transitional loess–gully ecosystem in China’s Yellow River Basin—using the Remote Sensing Ecological Index (RSEI). We integrated multi-temporal Landsat images via Google Earth Engine to construct a 40-year RSEI time series. The index couples greenness (NDVI), wetness (WET), heat (LST), and dryness (NDBSI) through principal component analysis, with PC1 explaining > 82% of the variance. Three evolutionary phases were identified: initial degradation (1986–1996), driven by slope cropland expansion; stabilization (1996–2006), coinciding with early ‘Grain for Green’ policies; and sustained recovery (2006–2024), characterized by the expansion of high-quality zones. We developed a novel resilience zoning framework integrating local spatial consistency, terrain constraints, and functional state (mean RSEI 2016–2024), which delineated three zones: high-resilience refugia (19.37%), moderate-resilience matrix (75.54%), and low-resilience corridors (5.09%). Mid-slope positions (TPI: 1.220–1.510) within moderate-resilience zones demonstrated optimal restoration efficiency, challenging conventional uniform approaches. The findings advocate spatially differentiated strategies—investing in transitional zones, retrofitting degraded corridors, and monitoring stable refugia—to advance the implementation of Sustainable Development Goal 15 in semi-arid regions globally.