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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)