Physics-Based ML and Polarimetric SAR for Soil Moisture Retrieval
(2023) 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023) — Location: Pasadena, CA, USA (16.July.2023)
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- Abstract
- Soil moisture represents a significant guiding factor for agricultural activities, especially for smart irrigation and crop yield estimation. In this context, SAR data is one of the most valuable sources of information for accurate and continuative estimation of soil moisture in agricultural areas. However, SAR-derived soil moisture retrievals are affected by several factors, including the vegetation cover, which is responsible for additional signal attenuation and scattering mechanisms. Concerning the algorithms for soil moisture estimation, Machine Learning (ML) has proved to be a valuable instrument for finding relations between SAR data and the soil dielectric properties. For this purpose, this study aims to synergically adopt electromagnetic data modelling and a ML algorithm to estimate the scattering contributions associated with the ground and demonstrate that they are more sensitive to soil moisture than the total received signal. To apply such approach to real SAR data, airborne acquisitions at L-band will be considered.
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Citations
Papale, L. G., Del Frate, F., Guerriero, L., Schiavon, G., & Bouchat, J. (2023). Physics-Based ML and Polarimetric SAR for Soil Moisture Retrieval. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 1930-1933. https://doi.org/10.1109/igarss52108.2023.10283164 (Original work published 2023)