Digital mapping of soil texture components in part of Khuzestan plain lands using machine learning models
2022
Sahraei, Nasim | Landi, Ahmad | Hojati, Saeid
This study aims to evaluate and to compare the efficiency of support vector machine (SVM) and random forest (RF) models using digital soil mapping approach to predict soil texture in part of Khuzestan province. In February 2021, before determining soil texture, 200 soil samples were taken using stratified random sampling from the surface layer )0-10 cm(. Auxiliary variables included primary and secondary derivatives of digital elevation model (DEM), remote sensing spectral indices (RS), from which the appropriate category was selected using principal component analysis (PCA). Based on PCA method, nine topographic variables from DEM and eight vegetation indices and spectra from RS were selected to predict soils texture components (sand, silt, and clay). The efficiency of the models was evaluated using the coefficient of determination (R2) and the root mean squared of the error (RMSE). The results indicated that the random forest model had higher accuracy and less error than the support vector machine model (SVM), so that values of R2 in this model were 0.80 for sand, 0.81 for silt, and 0.78 for clay, and the RMSE in the prediction of these particles were 6.02, 5.89 and 6.02, respectively. While the R2 and RMSE in the support vector machine model for prediction of sand, silt and clay were (0.39, 13.70), (0.45, 10.70), and (0.46, 9.32), respectively. Also, the results of this evaluation showed that salinity index, brightness index, and channel network in addition of the 6-band Landsat 8 satellite or the far infrared band were the most important environmental variables predicting clay, silt, and sand particles. In conclusion, we suggest using Random Forest model as a useful and reliable method in preparing digital maps of soil texture in the study area.
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