Very-high-resolution soil moisture estimation from remote-sensed SAR data has been under investigation in the literature. At this spatial scale, hypotheses that can be fulfilled at lower spatial scales are in fact not supported. For example, when analyzing agricultural areas at the field scale, it is essential to account for the seasonal evolution of vegetation and variations in soil roughness due to agricultural practices.
Similarly to what was previously conducted with Sentinel-1 C-band data [1], this work investigates the retrieval of superficial soil moisture from SAOCOM L-band data at the field scale. The L-band data are representative of a thicker layer of soil (20 cm) with respect to the C-band (6 cm) and are characterized by a spatial resolution of around 10 m. In situ soil moisture data on the study area are available, where the vegetation types are derived from the EUCROPMAP for the year 2022, and changes in roughness conditions are taken into account viaanomaly detection [2].

Acknowledgements
This research was performed under the framework of the GRAW project, funded by the Italian Space Agency (ASI), agreement no. 2023-1-HB.0, as part of the ASI’s program “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE).

References
[1] Graldi, G.; Zardi, D.; Vitti, A. Retrieving Soil Moisture at the Field Scale from Sentinel-1 Data over a Semi-Arid Mediterranean Agricultural Area. Remote Sens. 2023, 15, 2997. https://doi.org/10.3390/rs15122997
[2] Zhu, L. et al. (2019). ‘Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery’. In: Remote Sensing of Environment 225, pp. 93–106. DOI: 10.1016/j.rse.2019.02.027.