What question did this study set out to answer?

The study aims to improve the estimation of topsoil moisture in agricultural soils using SAR data.

February 21, 2026Remote Sensing0 citationsOpen Access

Estimation of Topsoil Moisture on Bare Agricultural Soils at the Intra-Plot Spatial Scale Using a Statistical Algorithm and X- and C-Bands SAR Satellite Data

Key Points

The study aims to improve the estimation of topsoil moisture in agricultural soils using SAR data.
Used TerraSAR-X and Radarsat-2 SAR imagery for data collection.
Developed a random forest regression model with radar backscatter and soil characteristics as inputs.
Conducted in situ measurements of TSM and other soil properties across 29 agricultural plots.
Applied modeling approach at intra-plot scale with varying circular buffer sizes.
Estimation performance increased with larger buffer size, peaking at 30 m with an R2 > 0.8.
Both C-band and X-band data were reliable, slightly improving when multi-angle data was used.
Inclusion of soil texture and roughness parameters enhanced the model's accuracy.
Intra-plot variability of TSM exceeded inter-plot differences, emphasizing the need for high resolution.

Abstract

Accurate estimation of topsoil moisture (TSM) is essential for optimizing agricultural practices, particularly in the context of precision farming. This study evaluates the use of high-resolution synthetic aperture radar (SAR) imagery from TerraSAR-X (X-band) and Radarsat-2 (C-band) for estimating TSM over bare agricultural soils, at both plot and intra-plot spatial scales. The experiment was conducted over a 420 km2 area in southwest France, comprising 29 agricultural plots with varying topography, soil texture, and land management practices. Extensive in situ measurements of TSM, soil texture, and surface roughness were collected over multiple dates. A random forest regression model was developed to estimate soil moisture, using radar backscatter coefficients, incidence angles, soil texture components (clay, silt, sand), and roughness parameters (Hrms, correlation length) as input features. The modeling approach was applied at multiple spatial scales by extracting satellite signals within circular buffers of varying radius (5 to 30 m), as well as at the plot scale. Results indicate that estimation performance improves with increasing buffer size, with the best results achieved at the 30 m intra-plot scale (R2 > 0.8, RMSE < 4 m3·m−3), outperforming plot-scale estimates. Both C-band and X-band data provided reliable results, with a slight advantage when combining data from multiple incidence angles. The inclusion of surface roughness and soil texture significantly improved model accuracy, underlining the importance of accounting for local soil properties in radar-based moisture retrieval. The intra-plot variability of TSM was found to be substantial, often exceeding inter-plot differences, highlighting the necessity for high spatial resolution in moisture monitoring. This study demonstrates the value of combining ground observations with multi-frequency SAR data and machine learning for high-resolution soil moisture mapping. The approach supports more precise water management strategies and contributes to sustainable agricultural development through informed decision-making.

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