Soil moisture retrieval over irrigated grassland using X-band SAR data

Mohammad El Hajj (1) , N. Baghdadi (1) , Mehrez Zribi (2) , Gilles Belaud (3) , B. Cheviron (4) , Dominique Courault (5) , F. Charron (3)

1 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
2 CESBIO - Centre d'études spatiales de la biosphère
3 Montpellier SupAgro - Centre international d'études supérieures en sciences agronomiques
4 UMR G-EAU - Gestion de l'Eau, Acteurs, Usages
5 EMMAH - Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes

The aim of this study was to develop an inversion approach to estimate surface soil moisture from X-band SAR data over irrigated grassland areas. This approach simulates a coupling scenario between Synthetic Aperture Radar (SAR) and optical images through the Water Cloud Model (WCM). A time series of SAR (TerraSAR-X and COSMO-SkyMed) and optical (SPOT 4/5 and LANDSAT 7/8) images were acquired over an irrigated grassland region in southeastern France. An inversion technique based on multi-layer perceptron neural networks (NNs) was used to invert the Water Cloud Model (WCM) for soil moisture estimation. Three inversion configurations based on SAR and optical images were defined: (1) HH polarization, (2) HV polarization, and (3) both HH and HV polarizations, all with one vegetation descriptor derived from optical data. The investigated vegetation descriptors were the Normalized Difference Vegetation Index "NDVI", Leaf Area Index "LAI", Fraction of Absorbed Photosynthetically Active Radiation "FAPAR", and the Fractional vegetation COVER "FCOVER". These vegetation descriptors were derived from optical images. For the three inversion configurations, the NNs were trained and validated using a noisy synthetic dataset generated by the WCM for a wide range of soil moisture and vegetation descriptor values. The trained NNs were then validated from a real dataset composed oTERRASAR-X COSMO-SKYMED X-band SAR backscattering coefficients and vegetation descriptor derived from optical images. The use of X-band SAR measurements in HH polarization (in addition to one vegetation descriptor derived from optical images) yields more precise results on soil moisture (Mv) estimates. In the case of NDVI derived from optical images as the vegetation descriptor, the Root Mean Square Error on Mv estimates was 3.6 Vol.% for NDVI values between 0.45 and 0.75, and 6.1 Vol.% for NDVI between 0.75 and 0.90. Similar results were obtained regardless of the other vegetation descriptor used.

Mots clés: SYNTHETIC APERTURE RADAR; PRAIRIES; SOIL WATER CONTENT

Mots clés: TELEDETECTION; PRAIRIE; HUMIDITE DU SOL; RADAR A SYNTHESE D'OUVERTURE

Domaines: Sciences de l'environnement

Mohammad El Hajj, N. Baghdadi, Mehrez Zribi, Gilles Belaud, B. Cheviron, et al.. Soil moisture retrieval over irrigated grassland using X-band SAR data. Remote Sensing of Environment, 2016, 176, pp.202-218. ⟨10.1016/j.rse.2016.01.027⟩. ⟨hal-01336862⟩