Soil classification in the Sudan Savanna using sentinel products and topographic information with machine learning models

Abstract

Abstract Accurate soil information is crucial for sustainable agricultural planning and land management, particularly in data-scarce regions, such as the Sudan Savanna, the largest sorghum-producing area in Africa. A recent study reported that soils in this region corresponded well with the topography, having formed primarily through erosion–deposition processes, resulting in systematic variation in soil types along the landscape. Therefore, this study compared the performances of three machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), for soil classification based on multisource remote sensing and topographic data. Ground-truth data with four different soil types, Lixisols, Petric Plinthosols, Pisoplinthic Petric Plinthosols, and Gleysols, were used to train and validate the models using 19 remote sensing-derived covariates including Sentinel-1 SAR, Sentinel-2 bands, spectral indices, and Topographic Wetness Index. Machine learning classification was analyzed under different scenarios of remote sensing feature combination. Results showed that the XGBoost with the selected feature combination achieved the highest performance with an overall accuracy of 78.9%, followed by RF (72.3%) and SVM (65.2%). Among the selected features, topographic parameters appeared the most important and provided complementary information for accurate soil classification. This study demonstrates the effectiveness of integrating optical, radar, and topographic information for soil mapping and provides a valuable management tool to support agricultural and environmental strategies in the Sudan Savanna.