Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China

The estimation of soil organic matter (SOM) content is essential for understanding the chemical, physical, and biological functions of soil. It is also an important attribute reflecting the quality of black soil. In this study, machine learning algorithms of support vector machine (SVM), neural network (NN), decision tree (DT), random forest (RF), extreme gradient boosting machine (GBM), and generalized linear model (GLM) were used to study the accurate prediction model of SOM in Tieling County, Tieling City, Liaoning Province, China. The models were trained by using 1554 surface soil samples and 19 auxiliary variables. Recursive feature elimination was used as a feature selection method to identify effective variables. The results showed that Normalized Difference Vegetation Index (NDVI) and elevation were key auxiliary variables. Based on 10-fold cross-validation, the RF model had the highest prediction accuracy. In terms of accuracy, the coefficient of determination of RF was 0.77, and the root mean square error was 2.85. The average soil organic matter content was 20.15 g/kg. The spatial distribution of SOM shows that higher content is concentrated in the east and west, while lower content is found in the middle. The SOM content of cultivated land was lower than that of forest land.