A modified IEM model for: scattering from soil surfaces with application to soil moisture sensing

Fung, A.K.; Dawson, M.S.; Chen, K.S.; Hsu, A.Y.; Engman, Edwin T.; O'Neill, P.; Wang, J.

doi:10.1109/igarss.1996.516642

Cited by 24 publications

(9 citation statements)

References 12 publications

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“…Based on a theoretical analysis, Fung et al [194] reported that angular SAR measurements could be used to determine roughness parameters for IEM, and furthermore that this approach was preferable to direct ground measurements as this accounts for scale, heterogeneity and resolution.…”

Section: Alternative Approaches To the Roughness Problemmentioning

confidence: 99%

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Verhoest

Lievens

Wagner

et al. 2008

Sensors

288

234

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Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.

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Section: Alternative Approaches To the Roughness Problemmentioning

confidence: 99%

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Verhoest

Lievens

Wagner

et al. 2008

Sensors

288

234

View full text Add to dashboard Cite

show abstract

“…Since the IEM is valid only for single scattering terms attributable to surface scattering, the model is generally only used to invert soil moisture from bare soil surfaces [44,65] where second order scattering is not considered, although in a later effort, Fung et al [66] improved the model to take into account multiple scattering terms. However, due to the complexity of the model, the original version is rarely used and often replaced by approximate solutions [67].…”

Section: Soil Moisture Retrieval Using Theoretical Scattering Modelsmentioning

confidence: 99%

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

2009

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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.

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“…A previous ground based experiment (Chanzy et al, 1998;Baghdadi et al, 2006;Rahman et al, 2007) and theoretical study (Sahebi et al, 2001(Sahebi et al, , 2002Fung et al, 1996) demonstrated that the multi-angular configuration is the best for estimation of bare soil surface parameters. Therefore, the multi-angular configuration was used for the inversion of backscattering models to estimate roughness and soil moisture from RADARSAT-1 data acquired at two different incidence angles.…”

Section: Methodsmentioning

confidence: 99%

“…Pasquariello et al (1997) and Baghdadi et al (2002) also determined that soil moisture estimation could be improved by using IEM inversion with multi-angular SAR imagery configuration. Based on a theoretical analysis, Fung et al (1996) reported that the multi-angular approach could be used to determine roughness parameters for IEM model and is also preferred over direct ground measurements in certain cases. Zribi and Dechambre (2002) defined the Z-index, which is a ratio of RMS height and correlation length, from backscattering coefficient differences generated by the IEM model with two different incidence angles.…”

Section: Introductionmentioning

confidence: 99%

An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

Sahebi

Angles

2010

Hydrol. Earth Syst. Sci.

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Abstract. The radar signal recorded by earth observation (EO) satellites is sensitive to soil moisture and surface roughness, which both influence the onset of runoff.This paper focuses on inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35 • and 47 • (in mode S3 and S7). This inversion was performed with three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM), and Modified Dubois Model (MDM), which were compared to obtain the best configuration. Mean absolute errors of 1.23, 1.12, and 2.08 cm for roughness expressed in rms height and for dielectric constant, mean absolute errors of 2.46 -equal to 3.88 (m 3 m −3 ) in volumetric soil moisture, -4.95 -equal to 8.72 (m 3 m −3 ) in volumetric soil moisture -and 3.31 -equal to 6.03 (m 3 m −3 ) in volumetric soil moisture -were obtained for the MDM, GOM, and OM simulation, respectively. These results indicate that the MDM provided the most accurate data with minimum errors. Therefore, the latter inversion algorithm was applied to images, and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.

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A modified IEM model for: scattering from soil surfaces with application to soil moisture sensing

Cited by 24 publications

References 12 publications

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

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

An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1

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