Global soil erosion rate 2000-2020-0.5degree_V2

We employed the Random Forest algorithm to integrate climatic and non-climatic drivers from 2000-2020 as explanatory variables. These drivers include global meteorological factors (precipitation, temperature, potential evapotranspiration, soil moisture), leaf area index, land cover, soil physicochemical properties (particle composition, pH), and topographic features (slope, elevation). We then generated a global ER gridded dataset with a spatial resolution of 0.5°×0.5°. The 10-fold cross-validation R² and root mean square error (RMSE) values of the RF model is 0.55 and 15 t ha^-1 yr^-1, respectively.本研究采用随机森林（Random Forest）算法，整合2000-2020年的气候与非气候驱动因子作为解释变量。上述驱动因子涵盖全球气象因子（降水量、气温、潜在蒸散量、土壤湿度）、叶面积指数（Leaf Area Index, LAI）、土地覆盖、土壤理化性质（颗粒组成、pH值）以及地形特征（坡度、海拔）。随后构建了空间分辨率为0.5°×0.5°的全球ER格点数据集。该随机森林模型的10折交叉验证决定系数（R²）与均方根误差（Root Mean Square Error, RMSE）分别为0.55与15 t ha⁻¹ yr⁻¹。