Study Region: The Angreb Watershed in northwestern Ethiopia, with diverse topography, bimodal rainfall, and mixed land use, represents semi-arid agroecosystems. Study Focus: This study integrates Landsat 8 spectral and thermal data with IoT-enabled soil moisture sensors to evaluate surface moisture variability. By deriving the Soil Moisture Index (SMI) from NDVI and Land Surface Temperature (LST), spatial gradients of soil moisture were mapped across rainfed, irrigated, and natural vegetation zones. Ground validation using 20 IoT sensors ensured reproducibility, strengthening the framework’s applicability for precision irrigation and climate-resilient smart farming systems. New Hydrological Insights: High-resolution SMI maps revealed strong spatial heterogeneity, with vegetated areas showing cooler temperatures and higher moisture, while degraded zones exhibited thermal intensification and dryness. A robust inverse correlation between LST and SMI (r = –0.84) confirmed the reliability of the vegetation–temperature framework. Validation with IoT sensors demonstrated high accuracy (Pearson r = 0.86, RMSE ±3.1 %, MAE ±2.4 %), underscoring the strength of combining satellite analytics with real-time ground observations. This integrated approach advances operational drought monitoring, supports precision irrigation scheduling, and enhances resilience in semi-arid farming landscapes. The findings highlight the transformative potential of satellite-IoT synergy for sustainable water resource management and agricultural planning.
Yanqun et al. (2026) studied this question.