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

Mohammad Choker,Mehrez Zribi,Hans Lievens,Francesco Mattia,Niko Verhoest,Nicolas Baghdadi,Mohammad El Hajj,Simonetta Paloscia

doi:10.3390/w9010038

Abstract

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.

Highlights

In the context of sustainable development, soil and water resources management is a key issue from the environmental point of view, and from a socioeconomic perspective [1]
The analysis of the error of the Dubois model according to the validity domain was studied by separating two intervals of kHrms, soil moisture and incidence angle (θ)
The analysis of the error of the Dubois model according to the validity domain was studied by about 0.5 dB with mv-values lower than 20 vol % (RMSE = 4.6 and 2.8 dB at HH and VV, respectively) range of surface roughness, soil moisture and incidence angle (Table 2)

Summary

Introduction

In the context of sustainable development, soil and water resources management is a key issue from the environmental point of view, and from a socioeconomic perspective [1]. Water 2017, 9, 38 characteristics (SSC), such as moisture (mv), roughness, texture, and slaking crusts are some key variables used to understand and model natural hazards, such as erosion, drought, runoff, and floods [2]. Soil moisture and roughness are important variables in land surface hydrology as they control the amount of water that infiltrates into the soil and replenishes the water table [3]. The estimation of soil moisture and roughness was performed by inverting the measured SAR backscatter through SAR backscattering models (both empirical and physical). Empirical models need to be calibrated using site specific in situ measurements and SAR observation at each time are used over a different study area. The most commonly empirical models are the models of Oh [8,9,10,11] and Dubois [12]; while, the most popular physical models are Integral equation model (IEM) [13], IEM calibrated by Baghdadi, called in this paper “IEM_B” [14,15,16,17,18,19], and Advanced Integral Equation Model (AIEM) [20]

Objectives

Results

Conclusion