Remote Sensing of Soil Moisture Dynamics: Applications in Agriculture, Hydrology and Climate Studies
Dev Kumar, Kumar Ashish, Samiksha G. Ahire, Shobha Malviya, Priyanka Rani, Rajnish Yadav, Mohit Kashyap, Khude R. G, Sonam Kumari, Surendra Kumar Gautam
International Journal of Plant & Soil Science · pp. 730–750 · Published 18 Aug 2025
10.9734/ijpss/2025/v37i85673Abstract
Soil moisture is a critical state variable within the Earth's system, fundamentally governing the exchange of water and energy between the land surface and the atmosphere. Its dynamics directly influence agricultural productivity, hydrological processes, and climate patterns across multiple scales. This comprehensive review examines the evolution, principles, and applications of remote sensing technologies for monitoring soil moisture. We provide a detailed analysis of the primary remote sensing methodologies, including optical, thermal infrared, and microwave techniques, elucidating their physical bases, operational characteristics, and respective advantages and limitations. The significant contributions of key satellite missions, such as the Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Sentinel-1 missions, are evaluated in the context of advancing global soil moisture monitoring capabilities. The paper explores the transformative impact of remotely sensed soil moisture data across three principal domains. In agriculture, these data facilitate precision irrigation, enhance crop yield forecasting, and support robust drought monitoring and early warning systems. In hydrology, the applications extend to improved flood forecasting, quantitative assessment of groundwater recharge, and more effective watershed management. For climate studies, soil moisture data are crucial for understanding land-atmosphere interactions, validating and improving climate models, and conducting long-term environmental monitoring. Despite significant technological progress, persistent challenges remain, particularly the trade-off between spatial and temporal resolution and the confounding effects of vegetation and surface roughness. The integration of advanced machine learning algorithms with multi-sensor data fusion techniques is highlighted as a promising pathway to overcome these limitations. Future directions, including next-generation satellite missions, the deployment of CubeSat constellations, and the synergy with Internet of Things (IoT) systems, are expected to further revolutionize the field, providing unprecedented insights into terrestrial water dynamics and supporting global efforts toward sustainable resource management.
Cited by 3
Giulliana Karine Gabriel Cunha, K. D. Deusdará Leal, J. M. da Silva · Revista Brasileira de Geografia Física · 2026
Sravani Pathivada, Susmitha Pallipe, V. S. Sahithi · 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) · 2026
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