A derivation of roughness correlation length for parameterizing radar backscatter models

Rahman, M.; Moran, M. Susan; Thoma, David P.; Bryant, Ray B.; Sano, Edson Eyji; Collins, Chandra D. Holifield; Skirvin, S. M.; Kershner, C. M.; Orr, Barron J.

doi:10.1080/01431160601075533

Cited by 49 publications

(36 citation statements)

References 21 publications

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“…Álvarez-Mozos et al (2008) applied the technique developed by Baghdadi et al (2006) to RADARSAT-1 data over Navarre (Spain), which led to promising soil moisture retrieval results. Alternatively, Rahman et al (2007) propagated backscatter observations from dry soils (eliminating the effects of soil moisture content) and measurements of RMS height through the IEM for the retrieval of effective correlation length. The use of the latter evidenced a large improvement over the use of field measurements.…”

Section: The Effective Roughness Approachmentioning

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: The Effective Roughness Approachmentioning

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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“…The RMS height (s) represents the vertical surface roughness (Oh and Hong 2007) and is defined as the standard deviation of the corresponding mean height of the soil surface, generally measured in centimetres (Rahman et al 2007). For the rough surface profile, s is defined in terms of the mean surface height and second moment of the surface profile as (Ulaby et al 1986) …”

Section: Surface Roughness Parameters S and Lmentioning

confidence: 99%

“…r(l) ¼ 1/e. Thus, the correlation length is defined as the length from a point on the ground to a short distance for which the heights of a rough surface are correlated with each other (Rahman et al 2007). This provides a reference for estimating the statistical independence of two points on the surface, i.e.…”

Section: Surface Roughness Parameters S and Lmentioning

confidence: 99%

“…The RMS height is the standard deviation of the surface height points from the reference surface (Ulaby et al 1986) and hence represents the vertical roughness Hong 2007, Baghdadi et al 2008). However, it is noteworthy that direct measurement of these parameters over larger field areas is expensive and sometimes impractical (Rahman et al 2007). Thus, as an alternative, the roughness parameters can be retrieved with the help of satellite images, especially SAR images.…”

Section: Introductionmentioning

confidence: 99%

“…A fundamental element to understand the nature of global change is the ability to model the two-way interaction between land and atmosphere (Dubois et al 1995). Again, surface roughness is a key parameter of radar backscatter models to retrieve soil information from radar images (Singh 2005, Oh and Hong 2007, Rahman et al 2007). Therefore, it is possible to retrieve surface roughness parameters with the SAR images.…”

Section: Introductionmentioning

confidence: 99%

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The potential application of fractal approach for surface roughness retrieval: A study for simulated surfaces

Pant

Singh

Srivastava

2010

Geomatics, Natural Hazards and Risk

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Natural surfaces can be modelled with fractals because fractals properly account for scale invariance and self similarity of these surfaces. The well-known measure, i.e. fractal dimension (D), is the property used to describe the roughness of fractal surfaces. Retrieving the Earth's surface roughness with satellite images, particularly synthetic aperture radar (SAR) images, is an interesting and challenging task. Consequently, many researchers are using electromagnetic models, various inversion techniques, semi-empirical models to retrieve the roughness parameters, i.e. RMS surface height (s) and autocorrelation length (l). Most of the models require some a priori information or some given values to solve the equations and retrieve l and s. Uncertainty still exists to retrieve these parameters with minimum or no a priori information. Therefore, in this paper, the fractal dimension approach has been applied to correlate l and s with fractal properties for development of a surface parameter retrieval algorithm. For this purpose, 1500 synthetic surfaces for known l and s have been generated, and their fractal dimension has been computed. D has also been computed after introducing Gaussian and speckle noise to the generated surfaces. The analysis among D, l and s shows the potentiality of relationship among these parameters and is helpful in developing a relationship among them by which l and s can be retrieved. The values of l and s are retrieved with the help of a look-up table for the synthetic surfaces which can be extended for retrieval of roughness parameters from the SAR images.

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Near‐Ground Channel Modeling with Applications in Wireless Sensor Networks and Autonomous Driving

Zekavat¹,

Buehrer²

2018

Handbook of Position Location

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A derivation of roughness correlation length for parameterizing radar backscatter models

Cited by 49 publications

References 21 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

The potential application of fractal approach for surface roughness retrieval: A study for simulated surfaces

Near‐Ground Channel Modeling with Applications in Wireless Sensor Networks and Autonomous Driving

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