Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples

Soil erodibility(K factor)mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation.However,the resulting maps usually have coarse spatial resolution at a regional scale.The objectives of this study were a)to map the K factors using a set of environmental variables and random forest(RF)model,and b)to identify the important environmental variables in the predictive mapping on a regional scale.We collected 101 surface soil samples across southeast China in the summer of 2019.For each sample,we measured the particle size distribution and organic matter content,and calculated the K factors using the nomograph equation.The hyperparameters of RF were optimized through 5-fold cross validation(m_(ay)=2,n_(tree)=500,p=63),and a digital map with 250 m resolution was generated for the K factor.The lower and upper limits of a 90% prediction interval were also pro-duced for uncertainty analysis.It was found that the important environmental variables for the K factor prediction were relief,climate,land surface temperature and vegetation indexes.Since the existing K factor map has an average polygonal area of 6.8 km^(2),our approach dramatically improves the spatial resolution of the K factor to 0.0625 km^(2).The new method captures more distinct differences in spatial details,and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation.This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.