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Open AccessArticle

Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements

Irstea (Institut National de Recherche en Sciences et Technologies pour l’Environnement et l’Agriculture), UMR-TETIS (Unité Mixte de Recherche, Territoires, Environnement, Télédétection et Information Spatiale), 500 rue François Breton, F-34093 Montpellier CEDEX 5, France
CESBIO (Centre d’Etudes Spatiales de la BIOsphère), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
CNR-IFAC (National Research Council, Institute of Applied Physics), via Madonna del Piano 10, Sesto Fiorentino, 50019 Firenze, Italy
Laboratory of Hydrology and Water Management, Ghent University, B-9000 Ghent, Belgium
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
CNR-ISSIA (Consiglio Nazionale delle Ricerche, Istituto di Studi sui Sistemi Intelligenti per l’Automazione), via Amendola 122/D, 70126 Bari, Italy
Author to whom correspondence should be addressed.
Academic Editors: Alexander Löw and Jian Peng
Water 2017, 9(1), 38;
Received: 15 November 2016 / Revised: 28 December 2016 / Accepted: 3 January 2017 / Published: 11 January 2017
(This article belongs to the Special Issue Remote Sensing of Soil Moisture)
The aim of this paper is to evaluate the most used radar backscattering models (Integral Equation Model “IEM”, Oh, Dubois, and Advanced Integral Equation Model “AIEM”) using a wide dataset of SAR (Synthetic Aperture Radar) data and experimental soil measurements. These forward models reproduce the radar backscattering coefficients ( σ 0 ) from soil surface characteristics (dielectric constant, roughness) and SAR sensor parameters (radar wavelength, incidence angle, polarization). The analysis dataset is composed of AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR, ERS, RADARSAT, ASAR and TerraSAR-X data and in situ measurements (soil moisture and surface roughness). Results show that Oh model version developed in 1992 gives the best fitting of the backscattering coefficients in HH and VV polarizations with RMSE values of 2.6 dB and 2.4 dB, respectively. Simulations performed with the Dubois model show a poor correlation between real data and model simulations in HH polarization (RMSE = 4.0 dB) and better correlation with real data in VV polarization (RMSE = 2.9 dB). The IEM and the AIEM simulate the backscattering coefficient with high RMSE when using a Gaussian correlation function. However, better simulations are performed with IEM and AIEM by using an exponential correlation function (slightly better fitting with AIEM than IEM). Good agreement was found between the radar data and the simulations using the calibrated version of the IEM modified by Baghdadi (IEM_B) with bias less than 1.0 dB and RMSE less than 2.0 dB. These results confirm that, up to date, the IEM modified by Baghdadi (IEM_B) is the most adequate to estimate soil moisture and roughness from SAR data. View Full-Text
Keywords: Oh; Dubois; IEM; AIEM; SAR images; soil moisture; surface roughness Oh; Dubois; IEM; AIEM; SAR images; soil moisture; surface roughness
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Choker, M.; Baghdadi, N.; Zribi, M.; El Hajj, M.; Paloscia, S.; Verhoest, N.E.C.; Lievens, H.; Mattia, F. Evaluation of the Oh, Dubois and IEM Backscatter Models Using a Large Dataset of SAR Data and Experimental Soil Measurements. Water 2017, 9, 38.

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