The dry valley is a unique geographic phenomenon in Southwest China with severe water erosion. However, little is known regarding its dominant controls and the discrepancies between dry valley subtypes, leading to the poor management of water erosion. To solve these problems, the revised universal soil loss equation (RUSLE) and Geodetector method were used in a dry temperate (DT), dry warm (DW), and dry hot (DH) valley. Results indicated that dry valleys suffer severe water erosion with a value of 64.78, 43.85, and 33.81 t·ha⁻¹·yr⁻¹. The Geodetector method is proven to be an efficient tool to quantify the dominant factor of water erosion. It was established that land use types (LUT) have the closest relationship with water erosion. The controls for water erosion could be better explained by multi-factor interactions analysis, particularly for the combination of slope and LUT in DW (q = 0.71) and DH (q = 0.66). Additionally, regions at high risk of water erosion were characterized by steep slope (>30°) and low vegetation coverage (<50%) in DT, while the opposite is shown in DH. These findings could provide insight for guiding soil erosion management and ecological restoration strategies that balance economic and environmental sustainability.

Fire severity mapping can capture heterogeneous fire severity patterns over large spatial extents. Although numerous remote sensing approaches have been established, regional-scale fire severity mapping at fine spatial scales (85%) than the accuracy of low severity classes in both cases. Adding high-resolution GF series images to the training dataset reduced the probability of low severity being under-predicted and improved the accuracy of the low severity class from 54.55% to 72.73%. RdNBR was the most important feature, and the red edge bands of Sentinel 2 images had relatively high importance. Additional studies are needed to explore the sensitivity of different spatial scales satellite images for mapping fire severity at fine spatial scales across various ecosystems.

Accurate estimation of Gross Primary Productivity (GPP) is crucial for understanding terrestrial carbon cycles and assessing ecosystem health. Light Use Efficiency (LUE) models, which are widely used for the generation of regional or global GPP products, often rely on Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). However, most existing FAPAR products with moderate to coarse spatial resolutions introduce uncertainties in GPP estimations across heterogeneous landscapes. In this work, the MODerate resolution Imaging Spectroradiometer (MODIS) FAPAR product at the 500-m resolution, along with a newly HIgh spatial resolution Global LAnd Surface Satellite (Hi-GLASS) FAPAR dataset at the 30-m resolution, was used to drive a LUE model for GPP estimations at 188 eddy covariance (EC) sites. Then, they were compared and evaluated based on the EC GPP measurements. Results showed that Hi-GLASS FAPAR provided the GPP estimates with more detailed spatial information compared to MODIS FAPAR. Moreover, the Hi-GLASS FAPAR significantly improved GPP estimations, with an overall R2 increase from 0.54 (MODIS) to 0.63 (Hi-GLASS) and an RMSE decrease from 3.04 to 2.70 gC m−2 day−1. Additionally, 75% of the selected sites exhibited enhanced R² values with Hi-GLASS FAPAR, demonstrating its application potential in GPP estimations across different vegetation types. Specifically, crop sites exhibited the most notable improvements, with an R² increase of 0.16 and an RMSE decrease of 0.70 gC m−2 day−1. These findings highlight the advantages of high-resolution FAPAR data in capturing spatial heterogeneity and improving the accuracy of GPP estimations, and underscore its potential for refined ecosystem monitoring.