Note on the multiple scattering in an IEM model

Chen, K.S.; Wu, Tzong‐Dar; Tsay, Mu‐King; Fung, A.K.

doi:10.1109/36.823917

Cited by 106 publications

(54 citation statements)

References 7 publications

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“…Whereas the complete version describes the backscattering process from a bare soil surface without any limitation on frequency and roughness, the approximate solutions are only valid for low to medium frequencies and surfaces with low roughness. 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].…”

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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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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“…The approaches range from simple empirical regression models, to comprising semi-empirical models and complex electromagnetic scattering models [43]. Among these, the integral equation model (IEM) proposed by [44] and its further developments known as advance IEM (AIEM) and extended IEM (I 2 EM) have become a popular means to estimate surface soil moisture over bare soil [43,[45][46][47][48]. Especially when empirical calibrations of the surface roughness are applied [49] the method yields fairly robust results.…”

Section: Airborne and Ground-based Sar Derived Soil Moisturementioning

confidence: 99%

In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR

et al. 2017

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At radar frequencies below 2 GHz, the mismatch between the 5 to 15 cm sensing depth of classical time domain reflectometry (TDR) probe soil moisture measurements and the radar penetration depth can easily lead to unreliable in situ data. Accurate quantitative measurements of soil water contents at various depths by classical methods are cumbersome and usually highly invasive. We propose an improved method for the estimation of vertical soil moisture profiles from multi-offset ground penetrating radar (GPR) data. A semi-automated data acquisition technique allows for very fast and robust measurements in the field. Advanced common mid-point (CMP) processing is applied to obtain quantitative estimates of the permittivity and depth of the reflecting soil layers. The method is validated against TDR measurements using data acquired in different environments. Depth and soil moisture contents of the reflecting layers were estimated with root mean square errors (RMSE) on the order of 5 cm and 1.9 Vol.-%, respectively. Application of the proposed technique for the validation of synthetic aperture radar (SAR) soil moisture estimates is demonstrated based on a case study using airborne L-band data and ground-based P-band data. For the L-band case we found good agreement between the near-surface GPR estimates and extended integral equation model (I 2 EM) based SAR retrievals, comparable to those obtained by TDR. At the P-band, the GPR based method significantly outperformed the TDR method when using soil moisture estimates at depths below 30 cm.

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“…In (Fung, 1994), the z z  terms and the term with the  are dropped in the equations (5.1.15) and (5.1.16) in order to simplify the calculation. However, in an improved version of the IEM (see (Chen et al, 2000)) these terms are kept in the analysis. In addition, as was the case for the Kirchhoff coefficients, f qp , the dependence through the slope terms is removed by integrating by parts and discarding the edge terms.…”

Section: On the Formulation Of The Iemmentioning

confidence: 99%