Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? - Liping Wang; Huanjun Liu; Xiang Wang; Xiaofeng Xu; Liyuan He; Chong Luo; Yong Li; Xinle Zhang; Deqiang Zang; Shufeng Zheng; Xiaodan Mei (Remote Sensing)
Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R2 > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.