中国区域30米分辨率LS因子数据集

土壤侵蚀是一个影响许多功能（生物多样性、粮食生产、碳储量、生态系统服务）的土地退化过程，引起了全球范围决策者、土地管理和政治家的关注。土壤侵蚀模型能够估计土壤流失的空间和时间模式，从而指导流域层面水土资源保护政策的制定和有效战略的实施。RUSLE模型因为很好的折中了土壤流失估计的简单适用和准确性，因此是目前最全球最广泛使用的模型。在RUSLE模型的所有输入层中，地形因子（LS因子）对土壤流失潜力的影响最为显著，用于计算LS因子的输入数据和计算方法对最终RUSLE模型计算的质量会有直接影响。中国区域30米分辨率LS因子数据集是使用开源工具和公开的高分辨率数字高程模型（30米SRTM）计算获得的，覆盖整个中国区域。在大尺度范围的数据计算过程中，基于对数据的空间分解，采用邻域依赖的计算方法，保证LS因子计算的准确性。同时采用多流算法，有助于精确估计流量累计。本数据集可用作各种尺度（地方、区域、国家）的任何土壤侵蚀评估的输入数据。

Soil erosion is a land degradation process that impacts multiple functions including biodiversity, food production, carbon storage, and ecosystem services, and has attracted widespread attention from global policymakers, land managers, and politicians. Soil erosion models can estimate the spatial and temporal patterns of soil loss, thereby guiding the formulation of soil and water conservation policies and the implementation of effective strategies at the watershed scale. The Revised Universal Soil Loss Equation (RUSLE) is currently the most widely used model globally, as it well balances the simplicity, applicability, and accuracy of soil loss estimation. Among all input layers of the RUSLE model, the topographic factor (LS factor) exerts the most significant influence on soil loss potential. The input data and calculation methods employed for LS factor calculation directly affect the quality of the final results generated by the RUSLE model. The 30-meter resolution LS factor dataset for China was computed using open-source tools and publicly available high-resolution digital elevation model (30-meter SRTM), covering the entire territory of China. During large-scale data computation, a neighborhood-dependent calculation method based on spatial data decomposition was adopted to ensure the accuracy of LS factor calculation. Meanwhile, a multiple-flow algorithm was utilized to enable accurate estimation of flow accumulation. This dataset can serve as input data for any soil erosion assessment at various scales, including local, regional, and national levels.