A systematic review on soil moisture estimation using remote sensing data for agricultural applications

Soil moisture plays a central role in agricultural sustainability and water-resource management under climate change and increasing water scarcity. Remote-sensing technologies have transformed soil-moisture estimation by enabling large-scale, high-resolution, and continuous monitoring. Following the PRISMA framework, this systematic review analyzes 64 studies published between 2016 and 2024, selected from 379 screened articles, focusing on agricultural applications. Remote-sensing data span optical, thermal, and microwave observations from satellites and unmanned aerial vehicles (UAVs), with estimation approaches classified as empirical, semi-empirical, physical, or learning-based. Satellite observations dominate the literature (73% of studies), while UAVs are increasingly used for high-resolution, site-specific assessments. Multi-sensor fusion, combining optical, thermal, and microwave data, is a growing strategy to overcome the limitations of individual sensors. Active SAR systems provide weather-independent measurements with high spatial resolution, whereas optical and thermal sensors offer valuable spectral indices but are limited by cloud cover and shallow penetration depth. Learning-based methods are the most frequent approach (54% of studies), using machine and deep learning to model complex relationships between soil moisture and remote-sensing variables. Principal challenges include vegetation interference, surface roughness, and limited in-situ calibration data. Mitigation strategies involve longer-wavelength SAR (L- and P-bands), multi-sensor fusion, downscaling, and integration of auxiliary datasets (soil texture, elevation, meteorology). By synthesizing recent advances and emerging trends, this review provides practical guidance for accurate, scalable, and operational soil-moisture monitoring in precision agriculture and environmental management. • Satellite data dominate soil moisture remote sensing analysis (73% of studies). • Learning-based and multi-sensor fusion methods improve estimation accuracy. • Challenges include vegetation, sensing depth, spatial resolution, and calibration. • Future directions: multi-sensor integration, ML, and new microwave sensors.