Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements

Baghdadi, Nicolas; Gherboudj, Imen; Zribi, Mehrez; Sahebi, Mahmod Reza; King, Christine; Bonn, F.

doi:10.1080/01431160310001654392

Cited by 152 publications

(170 citation statements)

References 14 publications

Supporting

Mentioning

159

Contrasting

Order By: Relevance

“…Many approximate solutions (and improvements) to the original version of the IEM have been developed [44,57,[68][69][70][71] and used in numerous studies with varying results [72,73]. Baghdadi et al [74] proposed a semi-empirical calibration of the IEM by replacing the required correlation length (l) with a calibration parameter derived from SAR data and field measurements and found the calibrated IEM to agree well with SAR backscatter measurements, as did Sahebi et al [75]. This calibrated IEM was later modified by Baghdadi and Zribi [76] to include C-band HH and VV polarisation and found by Alvarez-Mosos et al [77] to also show good agreement with the measured SAR backscattering coefficients.…”

Section: Soil Moisture Retrieval Using Theoretical Scattering Modelsmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

View full text Add to dashboard Cite

Abstract:The importance of land surface-atmosphere interactions, principally the effects of soil moisture, on hydrological, meteorological, and ecological processes has gained widespread recognition over recent decades. Its high spatial and temporal variability however, makes soil moisture a difficult parameter to measure and monitor effectively using traditional methods. Microwave remote sensing technology has demonstrated the potential to map and monitor relative soil moisture changes over large areas at regular intervals in time and also the opportunity of measuring, through inverse modelling, absolute soil moisture values. This ability has been demonstrated under a variety of topographic and land cover conditions using both active and passive microwave instruments. The purpose of this paper is to review the current status of soil moisture determination from active microwave remote sensing systems and to highlight the key areas of research that will have to be addressed to achieve routine use of the proposed retrieval approaches.

show abstract

Section: Soil Moisture Retrieval Using Theoretical Scattering Modelsmentioning

confidence: 99%

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

2009

View full text Add to dashboard Cite

show abstract

“…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: 72%

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

“…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]. Some of the more successful studies include those by Bindlish and Barros; 2000, [14], Baghdadi, et al, 2004Baghdadi, et al, , 2011. The advanced integral equation model (AIEM) proposed by Fung et al, [15].…”

Section: Introductionmentioning

confidence: 99%

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

Gharechelou¹,

Tateishi²,

Sumantyo³

2015

ARS

View full text Add to dashboard Cite

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.

show abstract

Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements

Cited by 152 publications

References 14 publications

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques

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

Contact Info

Product

Resources

About