Monitoring Soil Salinity in the Harran Plain: A Comparative Analysis of Machine Learning Algorithms Using Two Different Scenarios with Sentinel-2 Data

Abstract This study aimed to predict soil salinity in the Harran Plain using remote sensing and machine learning methods. The performance of five machine learning algorithms (Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Gradient Boost Machine (GBM), Classification and Regression Trees (CART) was compared under two different data scenarios: one using all parameters and another using only Sentinel-2 spectral indices. The results showed that the Random Forest (RF) algorithm achieved the highest performance in both scenarios. In the scenario using all parameters, the RF model achieved values of R2 = 0.87, RMSE = 0.154 dS/m, and MAE = 0.112 dS/m, while in the scenario using only Sentinel-2 indices, it achieved R2 = 0.83, RMSE = 0.175 dS/m, and MAE = 0.128 dS/m. SHAP analyses supported the feasibility of the model predictions and confirmed the consistency of RF. These findings demonstrate that Sentinel-2-based models provide a low-cost and effective alternative to traditional methods for large-scale areas.