Measuring Surface Roughness Height to Parameterize Radar Backscatter Models for Retrieval of Surface Soil Moisture

Bryant, Ray B.; Moran, M. Susan; Thoma, David P.; Collins, Chandra D. Holifield; Skirvin, S. M.; Rahman, M.; Slocum, K.; Starks, Patrick J.; Bosch, David D.; Dugo, M. P. González

doi:10.1109/lgrs.2006.887146

Cited by 85 publications

(81 citation statements)

References 12 publications

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“…The ground data for each parcel are issued by some point measurements, and the mean is presented as rms height and dielectric constant of the parcel. This result was also reported by Lehrsch et al (1988) and Bryant et al (2007). Bryant et al (2007) stated that measurement accuracy is the limiting factor in the accuracy of the soil moisture predictions for many cases.…”

Section: Resultssupporting

confidence: 83%

“…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%

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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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Section: Resultssupporting

confidence: 83%

Section: Methodsmentioning

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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“…Surface roughness and incident angle are often tuned or adjusted for, but semi-empirical equations, such as the Dubois model (see [17]), may limit the inclusion of additional variables that may lead to more accurate and robust prediction. Bryant et al, (2007) have previously demonstrated how roughness effects on radar backscatter are very complex depending on the configuration of the sensor, and the relationship between root-mean-square-height (h RM S ) and surface correlation length (CL) (i.e., the maximum extent of spatial correlation in surface roughness function in SAR horizontal look-direction), and that the degree of error in soil-moisture measurements can vary tremendously (e.g., < 1% to 82%), depending on whether CL is derived from h RM S or whether it is measured in the field [19]. Generally, in experimental studies, there is no relationship between these two independent parameters, however, recent studies have offered empirical, semi-empirical and theoretical approaches for deriving CL directly from a measurement of h RM S and to parameterize radar scattering models like the Integral Equation Model (IEM) for surface roughness requiring only the measurement of h RM S [19][20][21].…”

Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

“…Bryant et al, (2007) have previously demonstrated how roughness effects on radar backscatter are very complex depending on the configuration of the sensor, and the relationship between root-mean-square-height (h RM S ) and surface correlation length (CL) (i.e., the maximum extent of spatial correlation in surface roughness function in SAR horizontal look-direction), and that the degree of error in soil-moisture measurements can vary tremendously (e.g., < 1% to 82%), depending on whether CL is derived from h RM S or whether it is measured in the field [19]. Generally, in experimental studies, there is no relationship between these two independent parameters, however, recent studies have offered empirical, semi-empirical and theoretical approaches for deriving CL directly from a measurement of h RM S and to parameterize radar scattering models like the Integral Equation Model (IEM) for surface roughness requiring only the measurement of h RM S [19][20][21]. Rahman et al, (2008) demonstrate regional-scale mapping of surface roughness and soil moisture (using a multi-angle approach and the Integral Equation Model (IEM) retrieval algorithm for sparsely vegetated landscapes), eliminating the need for field measurements [22].…”

Section: Broad Range Of Model Assumptions and Predictive Accuracymentioning

confidence: 99%

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

Hosseini¹,

Newlands

Dean

et al. 2015

Remote Sensing

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We present a flexible, integrated statistical-based modeling approach to improve the robustness of soil moisture data predictions. We apply this approach in exploring the consequence of different choices of leading predictors and covariates. Competing models, predictors, covariates and changing spatial correlation are often ignored in empirical analyses and validation studies. An optimal choice of model and predictors may, however, provide a more consistent and reliable explanation of the high environmental variability and stochasticity of soil moisture observational data. We integrate active polarimetric satellite remote-sensing data (RADARSAT-2, C-band) with ground-based in-situ data across an agricultural monitoring site in Canada. We apply a grouped step-wise algorithm to iteratively select best-performing predictors of soil moisture. Integrated modeling approaches may better account for observed uncertainty and be tuned to different applications that vary in scale and scope, while also providing greater insights into spatial scaling (upscaling and downscaling) of soil moisture variability from the field-to regional scale. We discuss several methodological extensions and data requirements to enable further statistical modeling and validation for improved agricultural decision-support.Remote Sens. 2015, 7 2753

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“…Several studies have attributed this to an inadequate processing of roughness measurements on the one hand (e.g. Bryant et al, 2007;Lievens et al, 2009), or to a failure of the backscatter models in describing the complexity of surface roughness on the other hand (e.g. Mattia et al, 2003;Wagner et al, 2007).…”

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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Measuring Surface Roughness Height to Parameterize Radar Backscatter Models for Retrieval of Surface Soil Moisture

Cited by 85 publications

References 12 publications

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

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

Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data

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

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