EGU22-10703
https://doi.org/10.5194/egusphere-egu22-10703
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Matlab´s Supervised Machine-Learning Tools to Retrieve Surface Soil Moisture from Sentinel-1 SAR Data Over the Valencia Anchor Station (Spain)

Pierre Ferreira do Prado1,2, Iolanda Cristina Silveira Duarte1, and Ernesto Lopez-Baeza2,3
Pierre Ferreira do Prado et al.
  • 1Federal University of São Carlos, Center of Human Sciences and Biology, Applied Microbiology Laboratory, Sorocaba - São Paulo, Brazil (ppradogm@gmail.com) and (iolanda@ufscar.br)
  • 2Environmental Remote Sensing Group (Climatology from Satellites), Earth Physics & Thermodynamics Department, Faculty of Physics, University of Valencia, Valencia, Spain (piefedo@alumni.uv.es) and (Ernesto.Lopez@uv.es)
  • 3Albavalor S.L.U., University of Valencia Science Park, Paterna - Valencia, Spain (elopezbaeza@albavalor.es)

The urgency on detailing surface soil moisture content worldwide, especially in agricultural soils, is well established. The efforts of the European Space Agency (ESA), regarding the Sentinel-1 mission, facilitated a synthetic aperture radar (SAR) sensor that, in conjunction with machine-learning-based methods, can be useful  and fruitful responding to this technological demand. This paper aims at  exploring the possibility of the Valencia Anchor Station, near the city of Valencia, Eastern Spain, to provide 1 km x 1 km soil moisture products using its ground-based reference meassurements. The results suggest that, among several options, an artificial neural network using the Levenberg-Maquardt learning algorithm, based on soil moisture recovery from Sentinel-1 SAR radar data should be preferred for this site. Among other options, the so called fine-tree regression also presented relevant results. All of this allows us to gain insights into the complexity of the relation SAR´s backscatter – surface soil moisture relation for this site, also aiming at the potential extension of this knowledge to other sites where Sentinel-1 data is available, for example, in framework of the Joint Research Center "Ground-Based Observations for Validation (GBOV) of Copernicus Global Land Products" Project.

How to cite: Ferreira do Prado, P., Silveira Duarte, I. C., and Lopez-Baeza, E.: Using Matlab´s Supervised Machine-Learning Tools to Retrieve Surface Soil Moisture from Sentinel-1 SAR Data Over the Valencia Anchor Station (Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10703, https://doi.org/10.5194/egusphere-egu22-10703, 2022.