Highlights What are the main findings? Integration of SMAP, Sentinel-2, and WaPOR data through LSTM modeling enabled accurate daily soil moisture prediction across fragmented smallholder landscapes in semiarid northern Ghana. Stone bunds presented consistent soil moisture enhancement across multiple years, terrain types, and seasons, with benefits most pronounced on steeper slopes and in areas with lower topographic wetness. What is the implication of the main findings? The modeling framework provides a transferable approach for monitoring soil-water dynamics in data-sparse dryland regions where traditional monitoring infrastructure is absent. Model-enhanced satellite observations of soil moisture enable quantification of conservation practice effectiveness, supporting evidence-driven scaling of nature-based solutions for climate adaptation in vulnerable agricultural systems.Highlights What are the main findings? Integration of SMAP, Sentinel-2, and WaPOR data through LSTM modeling enabled accurate daily soil moisture prediction across fragmented smallholder landscapes in semiarid northern Ghana. Stone bunds presented consistent soil moisture enhancement across multiple years, terrain types, and seasons, with benefits most pronounced on steeper slopes and in areas with lower topographic wetness. What is the implication of the main findings? The modeling framework provides a transferable approach for monitoring soil-water dynamics in data-sparse dryland regions where traditional monitoring infrastructure is absent. Model-enhanced satellite observations of soil moisture enable quantification of conservation practice effectiveness, supporting evidence-driven scaling of nature-based solutions for climate adaptation in vulnerable agricultural systems.Abstract In semiarid, fragmented landscapes where data scarcity challenges effective land management, accurate soil moisture monitoring is critical. This study presents a high-resolution analysis that integrates remote sensing, in situ data, and machine learning to predict soil moisture and evaluate the impact of land conservation practices. A Long Short-Term Memory (LSTM) model combined with Random Forest gap-filling achieved strong predictive performance (R2 = 0.84; RMSE = 0.103 cm3 cm-3), outperforming SMAP satellite estimates by approximately 30% across key accuracy metrics. The model was applied to 222 field sites in northern Ghana to quantify the effects of stone bunds on soil moisture retention. The results revealed that fields with stone bunds maintained 4-6% higher moisture than non-bunded fields, particularly on steep slopes and in areas with low to moderate topographic wetness. These findings demonstrate the capability of combining remote sensing and deep learning for fine-scale soil-moisture prediction and provide quantitative evidence of how nature-based solutions enhance water retention and climate resilience in dryland agricultural systems.