Land use/cover classification using machine learning algorithms and their impacts on land surface temperature and soil moisture in the Alawuha Watershed, Ethiopia

Abstract

Land use/cover (LULC) mapping is vital for natural resource management and environmental monitoring in rapidly developing regions such as Ethiopia's Northern Highlands. This study pioneers the integration of Sentinel-1 Synthetic Aperture Radar and Sentinel-2 Level 2A MultiSpectral Instrument data via Google Earth Engine to achieve high-accuracy LULC classification in the Alawuha Watershed, evaluating Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machines (SVM). It also examined spatiotemporal variations in land surface temperature (LST) and the soil moisture index (SMI) across LULC types using Landsat 8. The Radial Basis Function (RBF) SVM outperformed RF and CART, achieving an average overall accuracy (OA) of 89.6 % and an F1 score of 89.5 % across 2019 and 2024, compared to 88.2 % OA and 88.1 % F1 for RF, and 83.8 % OA and 83.3 % F1 for CART. Spatiotemporal analysis revealed urban expansion, increased forest cover, and stable farmland, with farmland consistently dominant in the watershed. LST decreased significantly from 2014 to 2025, with built-up areas showing the highest values at 41.4 °C (2019) and 38.1 °C (2024) and forests the lowest at 30.4 °C (2019) and 27.8 °C (2024). SMI increased significantly (2014–2025), with forests recording the highest values at 0.59 (2019) and 0.66 (2024), and built-up and bare lands the lowest. These findings highlight LULC's role in regulating microclimates and water balance, offering key insights for sustainable land-use planning and environmental management.