Soil moisture retrieval algorithm using Sentinel-1 images

Nicolas Baghdadi (1) , Mohammad El Hajj (1) , Mehrez Zribi (2)

1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 CESBIO - Centre d'études spatiales de la biosphère

The aim of this communication is to present an operational approach for mapping soil moisture at high spatial resolution (plot scale) in agriculture areas by coupling S1 and S2 images. The proposed approach is based on the inversion of the Water Cloud Model (WCM) combined with the modified Integral Equation Model (IEM). Neural networks were developed and validated using synthetic SAR C-band database. The results showed that the soil moisture could be estimated in agricultural areas with an accuracy of approximately 5 vol.%.

Mots clés: Sentinel-1&2; SAR; C-band; Neural networks; Index Terms -Soil moisture

Domaines: Environnement et Société

Nicolas Baghdadi, Mohammad El Hajj, Mehrez Zribi. Soil moisture retrieval algorithm using Sentinel-1 images. International Scientific and Practical Conference “Problems of Desertification: Dynamics, Assessment, Solutions", Dec 2019, Samarkand, Uzbekistan. ⟨hal-02631815⟩