High-resolution digital mapping of soil erodibility in China

• Published in: Geoderma 2024-04, Vol.444, p.116853, Article 116853
• Main Authors: Sun, Longhui, Liu, Feng, Zhu, Xuchao, Zhang, Ganlin
• Format: Article
• Language: English
• Subjects: Environmental covariates; K factor; Machine learning; Predictive soil mapping; Soil erosion; Uncertainty
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2024.116853

•The mean K was 0.035 t ha h ha−1 MJ−1 mm−1 in China and varies with soil and water conservation zones.•The RF model has high accuracy in predicting K, with R2 = 0.49, RMSE = 0.0077 t ha h ha−1 MJ−1 mm−1.•The machine learning method represented more spatial variation detail.•Topographical and climatic variables were the primary variables affecting K. Soil erodibility (K) is the intrinsic susceptibility of a soil to water erosion. Currently, its detailed and accurate spatial distribution information especially over large areas is urgently required for national and regional soil erosion assessment and soil conservation decision making. This study combined pedotransfer function with digital soil mapping techniques to develop a high-resolution map of soil erodibility across China. The First, based on a recent national soil survey, we adopted the erosion-productivity impact calculator (EPIC) to calculate soil erodibility values at 4710 soil sampling points. Then, with the caclulated values of points, we used five techniques including polygon linking (PL), ordinary kriging (OK), Cubist, extreme gradient boosting (XGBoost), and random forests (RF) to generate spatial distribution of soil erodibility. The three latter machine learning techniques modeled the quantitative relationships between soil erodibility and a set of environmental covariates. The results showed that machine learning methods exhibited much more spatial details than the PL and OK did. Among the five techniques the RF achieved the highest accuracy with R2 of 0.49 and RMSE of 0.0077 t ha h ha−1 MJ−1 mm−1 based on 10-fold cross-validation. Spatial uncertainty analysis of the RF predictions showed that high uncerntainty occurred in northwestern China and low uncertainty in the center and southeast. We found that topographical and climatic variables are major environmental factors indirectly controlling spatial variation of soil erodibility while the soil particle composition and SOC contents directly influence the variation.