What question did this study set out to answer?

The research aims to improve remote sensing techniques for measuring soil moisture in bare Chernozems on varied terrains.

April 3, 2026Open Access

Remote Sensing of Soil Moisture in Bare Chernozems on Flat and Sloping Terrains

ZDZlatomir DimitrovSpace Research and Technology Institute AAAtanas AtanasovTechnical University of Varna DGDessislava GanevaBulgarian Academy of Sciences

Puntos clave

The research aims to improve remote sensing techniques for measuring soil moisture in bare Chernozems on varied terrains.
Utilized Sentinel-1 C-band SAR data for soil moisture retrieval
Calibrated TDR volumetric water content against gravimetric measurements
Evaluated SSM predictions against ground-based measurements
Compared performance of Random Forest and Gaussian Process Regression models
Normalized incidence angles to enhance SAR data accuracy
Achieved nRMSE of 7.27% and R2 of 0.86 during model training
Gaussian Process Regression provided better accuracy on independent validation dataset
Joint utilization of Sentinel-1 and Sentinel-2 data resulted in improved SSM determination
Identified optical indices as crucial for modeling soil moisture variations over time

Resumen

The aim of the current study was to select and test the appropriate model and input parameters for remote sensing retrieval of surface soil moisture (SSM) in the case of bare Chernozems on flat and sloping terrains in northern Bulgaria under different tillage systems. Normalized synthetic aperture radar (SAR) measurements from Sentinel-1 C-band dual-pol products (Gamma-Nought in VV, ratio) were utilized in two ways to delineate SSM from environmental factors that bias determination. The accuracy of the obtained SSM prediction was evaluated against ground-based volumetric water content (VWC) measured in the 0–3.8 cm soil layer at multiple points using a TDR meter. The TDR VWC data were preliminarily calibrated against gravimetric measurements in the 0–5 cm soil layer. The obtained data for soil water retention curves in all studied variants were used to determine the range of soil moisture variation. The measured ground-based data for surface roughness generally correlate with the co-pol Gamma-Nought in VV. The data modeled with the surface soil moisture script in Sentinel Hub (SSM-SH) was calibrated using the ground-based data. Incidence angle normalization of Sentinel-1 products improved the relationship between SAR observables and SSM, when expressed as the ratio of soil moisture to total porosity (rVWC). The modeling indicated the highest importance of the optical indices, together with the temporal differences of radar descriptors sensitive to variations in soil moisture over time. Although the applied Random Forest Regression (RFR) model achieved higher accuracy during training (nRMSE of 7.27%, R2 of 0.86), the Gaussian Process Regression (GPR) model provided better generalization performance on the independent validation dataset. The results proved the advantages of the joint utilization of temporal Sentinel-1 SAR measurements with Sentinel-2 optical acquisitions to determine SSM in different bare soil conditions for achieving high accuracy.

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