An Improved Iem Model for Bistatic Scattering From Rough Surfaces

Fung, A.K.; Liu, W.Y.; Chen, K.S.; Tsay, Mu‐King

doi:10.1163/156939302x01119

Cited by 89 publications

(63 citation statements)

References 4 publications

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“…In summary, physical-based models provide an explanation of the interactions between microwave electromagnetic radiation and the Earth's surface. They allow for a simulation of diverse experimental scenarios in terms of sensor configurations (wavelength, polarization, incidence angle) and soil parameters (surface roughness, soil dielectric constant, vegetation cover) [16,17]. Oh, Dubois, and integral equation models (IEMs) are among the most widely used models in inversion procedures [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel-and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R 2 ), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

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Section: Introductionmentioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

et al. 2018

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“…where R is replaced with σ 4π cos θ and σ the bistatic scattering coefficient matrix of the bottom surface, based on the IEM rough surface model, given in Equations (7) to (10). The positive and negative superscripts in L describe the attenuations of upward and downward intensities, respectively.…”

Section: Surface-volume Scatteringmentioning

confidence: 99%

“…Currently, many versions of IEM model for single and multiple surface scattering are available [2,10,21,22]. In this study, we use the original IEM with both single and multiple scattering to model the top and bottom surfaces.…”

Section: Introductionmentioning

confidence: 99%

Backscattering Analysis for Snow Remote Sensing Model With Higher Order of Surface-Volume Scattering

Syahali¹,

Ewe²

2016

PIER M

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Abstract-The study of earth terrain in Antarctica is important as this region has a direct impact on global environment and weather condition. There have been many research works in developing remote sensing technologies, as it can be used as an earth observation technique to monitor the polar region [11,15]. In previous studies, remote sensing forward model has been developed to study and understand scattering mechanisms and sensitivity of physical parameters of snow and sea ice. This paper is an extended work from previous studies [16][17][18][19], where an improved theoretical model to study polar region was developed. Multiple-surface scattering, based on an existing integral equation model (IEM) that calculates surface scattering and additional second-order surface-volume scattering, were added in the model from prior research works [7] for improvement in the backscattering calculation. We present herein the application of this model on a snow layer above ground which is modeled as a volume of ice particles that are closely packed and bounded by irregular boundaries above a homogenous half space. The effect of including multiple surface scattering and additional surface-volume scattering up to second order in the backscattering coefficient calculation of snow layer is studied for co-polarized and cross-polarized returns. Comparisons with satellite data are also done for validation. Results show improvement in the total backscattering coefficient for cross-polarized return in the studied range, suggesting that multiple-surface scattering and surface-volume scattering up to second order are important scattering mechanisms in the snow layer and should not be ignored in polar research.

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“…The integral equation model, or IEM [18], is commonly used to predict backscatter from surface parameters in intertidal environments (root mean square height, correlation length and dielectric constant) and SAR configuration (polarization of microwave signal, wavelength and angle of incidence) [14,[38][39][40]. In this study, we used an extended version of the IEM that takes into account the phase effect in Green's function; as a result, this version provides much more accuracy in bistatic scattering and also includes multiple scattering [41]; an elaborate description of the model can be found in Fung and Chen [42]. For this study, we used a spectrum with an exponential autocorrelation function.…”

Section: Shellfish Backscatter Modelling and Mappingmentioning

confidence: 99%

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Nieuwhof

Herman

Dankers³

et al. 2015

Remote Sensing

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On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X-(TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh's model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X-and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56-0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29-0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although OPEN ACCESSRemote Sens. 2015, 7 3711 spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency.

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An Improved Iem Model for Bistatic Scattering From Rough Surfaces

Cited by 89 publications

References 4 publications

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale

Backscattering Analysis for Snow Remote Sensing Model With Higher Order of Surface-Volume Scattering

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

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