Abstract

Monitoring the surface soil moisture (SSM) in agricultural areas at plot scale helps in many applications such as irrigation planning and crop management. Over the last decade, SAR (Synthetic Aperture Radar) data have shown great potential in estimating SSM over agriculture areas. Today, Sentinel-1 (S1) and Sentinel-2 (S2) satellites present a good opportunity for operational SSM estimates in agricultural areas because they provide free and open access data at high spatial resolution (10 m x 10 m) and high revisit time (6 days over Europe). The aim of this paper is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) using the neural network technique.

Highlights

  • Soil moisture plays a key role in different hydrological processes

  • To estimate soil moisture and roughness from the C-band polarimetric radar data, Baghdadi et al [1] first generated a database of backscattering coefficients for a wide range of bare soil conditions using the Integral Equation Model Integral Equation model (IEM) [2]

  • The higher accuracy of estimated surface soil moisture (SSM) moisture is probably due to the well-calibrated IEM combined with the well parameterized Water Cloud Model (WCM) and the use of high spatial resolution (10 m × 10 m) land cover maps derived from S2 images to eliminate SAR scattering from forest and urban areas

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Summary

INTRODUCTION

Soil moisture plays a key role in different hydrological processes (floods, runoff, evapotranspiration, infiltration, soil erosion, and imbalances in the water and carbon cycles). The trained neural networks were validated using a real database and show an accuracy on the soil moisture estimates of approximately 7 vol.% with the use of a priori information. El Hajj et al [3] validated the proposed approach for the operational mapping of soil moisture over a study site located in Occitanie, South France They showed that the soil moisture in agricultural areas could be estimated with an accuracy of approximately 5 vol.%. The higher accuracy of estimated SSM moisture is probably due to the well-calibrated IEM combined with the well parameterized WCM and the use of high spatial resolution (10 m × 10 m) land cover maps derived from S2 images to eliminate SAR scattering from forest and urban areas.

S1-SSM ALGORITHM
COMPARISON BETWEEN S1-SSM AND
CONCLUSION

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