Soil salinity digital mapping using random forest model in saline lands of Eyvanekey plain

This study aimed to investigate the spatial changes of soil salinity using RF model in a part of Eyvanekey Plain (Semnan Province 2018). Grid sampling with 100 m intervals (106 samples) was taken from 105 ha of soils developed on marl and gravely alluviums. The land uses were pistachio plantations with furrow irrigation and abandoned land. The maximum EC was (173.2 and 34 dS/m) in the abandoned and furrow irrigation pistachio plantations respectively. The main factors of salinization were saline marls, saline irrigation water, and high PET. The R2 for the salinity prediction map by RF model was 0.49, and the most important covariates were normalized difference salinity index (NDSI), topographic wetness index (TWI), Channel Network Base Level (CNBL), normalized difference vegetation index (NDVI), and modified soil vegetation index (SAVI). Spectral ratio indices derived from Landsat 8 contributed the most to the soil salinity prediction. Out of 5 main auxiliary variables, 3 variables are related to spectral ratio indices and the reason was the presence of salt on the soil in the studied area. Using NDVI with other salinity and moisture indices improved the salinity prediction model. Examining the results of covariates correlation and the implementation of recursive feature elimination showed that many covariates increase model complexity and prediction error. Recursive feature elimination helped to simplify the model by identifying the most important covariates. The salinity prediction map by random forest was consistent with the field observations and clearly defined the critical saline area.