Integrating Field Data, Remote Sensing, and Machine Learning for Enhanced Soil Moisture Prediction in Semi-Arid West Africa

Soil moisture plays a pivotal role in driving hydrological, ecological, and agricultural processes. Yet, its accurate estimation remains a significant challenge, particularly in data-scarce and semi-arid regions of West Africa. This study presents a comprehensive approach that integrates field measurements, high-resolution remote sensing data, and advanced machine learning techniques to enhance soil moisture prediction in small-scale agricultural systems. By combining innovative downscaling methods with deep learning models, the proposed framework effectively captures both the spatial heterogeneity of soil moisture and its complex temporal dynamics, addressing a critical gap in existing methodologies. The predictive framework demonstrated outstanding performance, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.854, reducing root mean square error (RMSE) by 33%, and exhibiting negligible bias when compared to conventional approaches. These metrics highlight its capability to provide more accurate and reliable predictions, even in the context of limited ground-based observations. Moreover, the study underscores the significant impact of soil conservation practices, such as stone bunds, on enhancing soil moisture retention. The analysis revealed that these interventions are particularly effective on steep slopes and in areas with lower moisture accumulation potential, offering valuable insights for sustainable land and water resource management. By bridging the gap between coarse-resolution satellite observations and the fine-scale data needs of localized agricultural systems, this study delivers a scalable and adaptable solution for soil moisture monitoring. The integration of cutting-edge technologies with on-the-ground insights not only enhances predictive accuracy but also provides a robust framework for improving agricultural resilience and water management in semi-arid environments. These findings emphasize the transformative potential of leveraging modern tools and multidisciplinary approaches to address pressing challenges in soil moisture estimation and agricultural sustainability, paving the way for more informed decision-making in vulnerable regions.