Wind erosion poses a significant challenge to agricultural sustainability in Northern China’s arid regions. This study investigated the effectiveness of alfalfa grassland versus conventional cropland in controlling wind erosion across nine study sites in three agroecological regions. Using Sentinel-2 satellite imagery and the Revised Wind Erosion Equation (RWEQ) model, we analyzed vegetation cover duration and quantified soil wind erosion from 2018 to 2020. The results showed that alfalfa grassland extended vegetation cover by 80 days annually compared to cropland, with most extension occurring in spring. Alfalfa grassland demonstrated superior erosion control, reducing soil losses by 50% (24.02 versus 50.70 t/ha/yr) and increasing soil retention threefold (1.52 versus 0.59 t/ha/yr) compared to cropland. The northwest region experienced the highest erosion rates, while management practices significantly influenced alfalfa’s soil conservation effectiveness. Multiple regression analysis revealed vegetation cover and annual precipitation as primary factors affecting wind erosion. These findings suggest integrating alfalfa into crop rotations could effectively enhance soil conservation in Northern China’s wind erosion-prone regions.

It remains challenging to map grassland above-ground biomass (AGB) over Eurasia because in-situ measurements are sparse, grassland types are highly diverse, and grasslands are highly sensitive to climate change and human activities. This study compiled extensive in-situ AGB measurements from the past 20 years and combined them with MODIS imagery, meteorological, soil, and topographic data to map grassland AGB over Eurasia for 2000-2024. We used a stacking ensemble framework to generate Eurasian grassland AGB maps at 500 m resolution for 2000–2024 and quantified pixel-wise errors and map reliability. The optimized stacking ensemble framework improved the accuracy of grassland AGB estimation and reduced estimation bias compared to single machine learning (ML) models, with RMSE reduced by 13.7% and bias reduced by 20.9%. The saturation effect under high AGB conditions was reduced. The 25-year AGB maps had a mean pixel-wise uncertainty of 18.44 g/m², with relative errors below 15% for all grassland types. The error analysis indicates that the ensemble framework has good extrapolation ability, with errors well controlled in regions with sparse in-situ measurements. The AGB maps showed an overall increasing trend in AGB across Eurasia, with an increase in 23.4% of the Eurasian grasslands and a decrease in 3.1% of the region. Semi-humid regions contributed most of the increase, but the instability of these regions in this region also increased. This study produced a long-term and reliable Eurasian grassland AGB dataset, which can be useful for future ecological and climate change studies and inform grassland management.