Research on Spatial Distribution of Soil Texture in Southern Ningxia Based on Machine Learning

Based on historical soil data, this paper studied the spatial variability of soil texture and its relationship with environmental factors in southern Ningxia by using machine learning.

Classification and regression tree (CART), random forest (RF) and traditional statistical methods were used to explore the main environmental factors that affected the soil texture types and predict the spatial distribution of soil texture types in southern Ningxia, based on 428 soil profiles from the second soil survey in the 1980s, combined with topographic factors, soil types, and normalized vegetation index. The accuracy of the models were verified by the validating set of soil profiles and the soil samples in Haiyuan County, Ningxia.

(1) The accuracy rates of RF and CART on the soil texture type of the verification set of soil profiles were 62.36% and 55.29%, respectively; the area under the receiver operating characteristic (ROC) curve of them (area under roc curve, AUC) were 0.7515 and 0.6933, respectively; the accuracy rates of them on soil samples in Haiyuan County were 54.10% and 48.36%, respectively; the AUC of them were 0.6599 and 0.5981 respectively. (2) Soil type (ST) was the most important predictor variable, followed by elevation (Ele). The higher elevation was, the heavier the soil texture was. The effects of wind exposition index (WEI) and slope (Slo) on soil texture were lower. (3) The results predicted by two methods showed a spatial distribution trend that the soil texture was heavy in the southern area but light in the northern area of southern Ningxia.

The prediction accuracy of RF for soil texture type in southern Ningxia was higher than CART. Making full use of historical data, combined with field sampling, could meet the accuracy requirements of digital mapping. In the loess region, soil types and elevation were the environmental factors which had strong correlation with spatial variation of soil texture.