Calibration of the Integral Equation Model for SAR data in C‐band and HH and VV polarizations

Baghdadi, Nicolas; Holah, Noha; Zribi, Mehrez

doi:10.1080/01431160500212278

Cited by 143 publications

(173 citation statements)

References 15 publications

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“…Such a technique was initially introduced by Su et al (1997), who used backscatter and soil moisture observations of a first acquisition date to estimate a roughness parameter, referred to as the effective roughness parameter, which could further replace in situ measurements of roughness for the retrieval of soil moisture from subsequent SAR acquisitions. However, recent studies (Baghdadi et al, 2004(Baghdadi et al, , 2006 have shown that effective roughness parameters of the same site may diverge significantly when derived from different SAR acquisitions with specific sensor configurations. As a result, the use of temporally constant effective roughness parameters for soil moisture retrieval from subsequent acquisitions may not be justified.…”

Section: Introductionmentioning

confidence: 99%

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of fieldmeasured roughness. This paper analyses effective roughness parameters derived from C-and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at Cand L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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Section: Introductionmentioning

confidence: 99%

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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“…In this new version of the IEM, the measured correlation length is replaced by a fitting parameter "Lopt", whatever the range of Hrms. The use of Lopt has been tested under various SAR configurations, in the X and C bands, and at both HH and VV polarizations [33][34][35][36]. The results show firstly that the implementation of Lopt leads to an improved agreement between the IEM simulations and the SAR data.…”

Section: Baghdadi Calibrated Iem Versionmentioning

confidence: 94%

“…In the following paragraph, the semi-empirical calibration of the IEM model developed by Baghdadi et al [33][34][35][36] is applied, in order to minimize the mismatch between the IEM simulations and the observed backscattered signal. In this new version of the IEM, the measured correlation length is replaced by a fitting parameter "Lopt", whatever the range of Hrms.…”

Section: Baghdadi Calibrated Iem Versionmentioning

confidence: 99%

“…A statistical model was thus developed to estimate effective roughness parameters from radar observations. Baghdadi et al [33][34][35][36] define a new empirical correlation length, Lopt, calculated as a function of the rms surface height and the characteristics of the remote sensing radar (frequency, incidence angle and polarization), allowing an improved fit to be achieved between IEM simulations and radar observations. Despite these contributions, the influence of roughness is still poorly modelled in currently known inversion techniques, and most of these studies were made in the C band.…”

Section: Introductionmentioning

confidence: 99%

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Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Gorrab

Zribi

Baghdadi³

et al. 2015

Remote Sensing

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Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

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“…Moreover, the inconsistencies researchers are observed in several studies between SAR backscattering coefficients and those simulated results by the models, interpretation the inversion results was inaccurate [7]- [10]. The empirical and semi-empirical models are working based on the experimental data and most frequent was tested over wet and humid area by C band radar data such as RADARSAT-1 and 2 and ENVISAT and recently x-band data such as TerraSAR data over bare soil [10]- [12]. The radar scattering model, integral equation method (IEM) model of has been most commonly used validated at fine scales in a laboratory setting (Licheri et al, 2001; Macelloni et al, 2000) using uniform media but has not been shown to consistently predict radar backscatter at broad scales [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

Gharechelou¹,

Tateishi²,

Sumantyo³

2015

ARS

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The purpose of this paper is to simulate the backscattered signal by experimental data and field working then, comparing with the backscattered signal from actual L-band SAR data over arid to semi-arid environments. The experimental data included the laboratory-measured dielectric constant of soil samples and the roughness parameter. A backscattering model used to simulate the backscattering coefficient in sparse vegetation land cover. The backscattering coefficient (σ˚) simulated using the AIEM (advanced integral equation model) based on the experimental data. The roughness data were considered by the field observation, chain method measuring and photogrammetry simulation technique by stereo image of ground real photography. The simulated backscattering coefficients were compared with the real extracted backscattering coefficient (σ˚) from the ALOS PALSAR single and dual polarization mode data. The most problem in backscattering simulation was the vegetation water content. Therefore, the water-cloud model using the water index result of optical data applied on the simulated backscatter model for enhancement the backscattering heterogeneity from vegetation water contents due to the mix pixel of vegetation in spars vegetation. At the results the AIEM model overestimated the backscattering simulation, it might be cause of high sensitivity of this model to roughness. The ALOS PALSAR HV polarization mode is more sensitive than the HH mode to vegetation water content. The water-cloud model could improve the result and the correlation function of the samples was increased but, the difficulties were the input the A and B parameters to model.

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Calibration of the Integral Equation Model for SAR data in C‐band and HH and VV polarizations

Cited by 143 publications

References 15 publications

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Comparison of Simulated Backscattering Signal and ALOS PALSAR Backscattering over Arid Environment Using Experimental Measurement

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