A 30-Meter Resolution LS-Factor Dataset for Mainland China

Soil monitoring is a land degradation process that affects many functions (biodiversity, food production, carbon stocks, ecosystem services) and has attracted the attention of policy makers, land managers and politicians worldwide. Soil monitoring models can estimate spatial and temporal patterns of soil loss, thereby guiding the formulation of water and soil resource conservation policies and the implementation of effective strategies in watershed areas. The RUSLE model is currently the most widely used model in the world because it is a good compromise between simple applicability and accuracy of soil loss estimation. Among all input layers of the RUSLE model, the terrain factor (LS factor) has a great influence on soil loss potential. Significantly, the data input and calculation methods used to calculate the LS factors have a direct impact on the quality of the final RUSLE model calculation. The 30-meter resolution LS factor dataset, covering the entire China’s regions, was calculated using open-source tools such as SAGA and GDAL, coupled with publicly available high-resolution digital elevation model data (30-meter SRTM). In the process of data calculation in a large-scale region, based on the spatial decomposition of data, a neighborhood-dependent calculation method is used to ensure the accuracy of LS factor calculation. Simultaneous use of multi-flow algorithms helps to accurately estimate flow accumulation. This dataset can be used as input data forsoil erosion assessments at various scales, from local areas to entire regions.