Soil Moisture Retrieval in the Presence of Vegetation Using Dual-polarisation Data

When retrieving the surface soil moisture over agricultural fields using Synthetic Aperture Radar (SAR), vegetation absorbs and scatters the signal, which then hinders the analysis of the underlying soil [1,2]. One method to circumvent this is by decomposing the radar signal into three components: surface, dihedral, and volume scattering [3,4]. Recent advancements have extended these models into a tensor framework and incorporated spatial information to then invert the geophysical parameters of the models and retrieve soil moisture for a wider range of crop scenarios  [5]. The soil moisture is retrieved through numerical optimization of the models' geophysical parameters. 

In this work, we compare the information loss when retrieving soil moisture using the tensor-based decomposition between full-polarisation and dual-polarisation inversion. We assess the ambiguity of parameter retrieval for different combinations of dual-polarisation channels and conclude on which set-up of dual-polarisations with VV, VH, and/or HH provides the most constrained and thus most optimal soil moisture retrieval with the tensor decomposition technique. 

This work is implemented using the full-pol airborne F-SAR data from DLR and soil moisture retrieval from the inversion is compared with ground measurements taken during the AgriROSE-L campaign around Munich, Germany, in 2025. 

[1] I. Hajnsek, E. Pottier and S. R. Cloude, "Inversion of surface parameters from polarimetric SAR," in IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 4, pp. 727-744, April 2003, doi: 10.1109/TGRS.2003.810702.

[2] Dipankar Mandal, Vineet Kumar, Debanshu Ratha, Subhadip Dey, Avik Bhattacharya, Juan M. Lopez-Sanchez, Heather McNairn, Yalamanchili S. Rao, Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data, Remote Sensing of Environment,  https://doi.org/10.1016/j.rse.2020.111954.

[3] Freeman, Anthony, and Stephen L. Durden. "A three-component scattering model for polarimetric SAR data." IEEE transactions on geoscience and remote sensing 36.3 (2002): 963-973.

[4] Yamaguchi, Yoshio, et al. "Four-component scattering model for polarimetric SAR image decomposition." IEEE Transactions on geoscience and remote sensing 43.8 (2005): 1699-1706

[5] Basargin, N., Alonso-González, A., & Hajnsek, I. “Model-based tensor decompositions for soil moisture estimation.” Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR (2024)