High-resolution mapping and driving factors of soil erodibility in southeastern Tibet

Abstract

Quantifying the spatial distribution of soil erodibility (K factor) in the Qinghai-Tibet Plateau is essential for global soil erosion management. However, many K factor maps have a coarse spatial resolution at the regional scale, and high-resolution mapping is still a challenge. Quantitative analysis of the influence of environmental factors such as soil, topography, climate, and vegetation, and human activities on the K factor is also lacking. Therefore, we mapped the high-resolution (90 m) spatial distribution of the K factor values in southeastern Tibet using a random forest model with multiple environmental variables, based on remote sensing, ground observations (114 sampling points), and gridded datasets. The K factor estimates based on soil particle size composition and organic carbon content ranged from 0.09 to 0.35, showing moderate variation. The random forest model yielded a coefficient of determination > 0.9 and provided detailed information on the spatial distribution of K factor values, especially in large unsampled areas. The predicted K factor values tended to be high in the eastern area and low in the western area. Partial least squares path modeling showed that soil physical properties such as fractal dimension and mean weight diameter of aggregates had the largest influence on the K factor (path coefficient 0.695). Climate and topography also had a considerable influence on the K factor (path coefficient −0.489 and −0.469, respectively), while the influence of vegetation and human activities was minimal. Accordingly, the random forest model is an effective tool for high-resolution spatial distribution mapping of the K factor with limited sampling data.