Evaluation of multi-frequency bare soil microwave reflectivity models

Montpetit, Benoît; Royer, A.; Wigneron, Jean‐Pierre; Chanzy, André; Mialon, Arnaud

doi:10.1016/j.rse.2015.02.015

Cited by 47 publications

(26 citation statements)

References 42 publications

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“…Hence, the maps presented in this study are valid for low values of the parameter, which is an assumption often made at C-band [18]. Moreover, a recent study [27] has confirmed the fact that a low value of the parameter can be used to model the effects of roughness over a large frequency range. Note also that the use of large values of make the decoupling between the roughness and vegetation effects more difficult (lower values were obtained in the R 2 values of the relationship between * and NDVI over all types of biomes).…”

Section: Discussionsupporting

confidence: 77%

“…Wang et al [57] have found that the frequency dependence of is strong and relatively small values for were obtained, ranging from 0 to 0.12 at L-band and from 0 to 0.3 at C-band. More recently, Montpetit et al [27] have found that the value Q = 0.075 can be used over a large frequency range (1.4-90 GHz). In this study, we considered the value of is equal to zero, but a sensitivity analysis is made for Q varying from 0 to 0.3.…”

Section: Methodsmentioning

confidence: 99%

“…As noted in Section 3, Montpetit et al [27] have found that the value 0.075 can be used over a large frequency range and Wang et al [57] have found values of ranging from 0 to 0.3 at C-band. In this sensitivity study, in addition to the case 0, we set the values of the parameter to 0.…”

Section: Sensitivity Analysismentioning

confidence: 96%

“…Montpetit et al [27], recently showed that the latter modeling approach was very effective in modeling the signatures of rough soils at AMSR-E frequencies. Several studies estimated the values of the model parameters ( , , and ) from microwave observations over bare fields [21,22] and computed relationships to link these parameters to the physical parameters measured in the fields (mainly and ) [23,24].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Global-Scale Evaluation of Roughness Effects on C-Band AMSR-E Observations

Wang

Wigneron²,

Jiang

et al. 2015

Remote Sensing

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Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF's (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval process. In the first step, we retrieved a parameter (referred to as the * parameter) accounting for the combined effects of roughness and vegetation. Then, global MODIS NDVI

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Section: Discussionsupporting

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global-Scale Evaluation of Roughness Effects on C-Band AMSR-E Observations

Wang

Wigneron²,

Jiang

et al. 2015

Remote Sensing

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“…Chen and Weng developed a semi-empirical model mainly relied on the ground-based reflectivity measurements [5], which analytically accounted for both roughness attenuation and cross-polarization mixing effects in the frequency range of 1-100 GHz. A number of studies have also evaluated and compared these previous semi-empirical models [7,25]. Most of these published models were developed either from limited ground-based measurement conditions (e.g., from single view angle or limited soil roughness range) or with discrete model formulation (e.g., using different sets of model parameters at different frequencies).…”

Section: Introductionmentioning

confidence: 99%

A Parameterized Microwave Emissivity Model for Bare Soil Surfaces

Xie

Shi

et al. 2017

Remote Sensing

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Due to the difficulty in accurately interpreting surface emissivity spectra, problems remain in the application of passive microwave satellite observations over land surfaces. This study develops a parameterized soil surface emissivity model to quantify the microwave emissivity accurately and rapidly for Gaussian-correlated rough surfaces. We first analyze the sensitivity of surface emissivity to parameters using the advanced integral equation model (AIEM) simulated data. On the basis of the analysis and previous empirical models, two function factors that consider the polarization dependence of surface reflectivity are developed in the parameterized soil surface emissivity model. These factors also comprehensively account for the effects of surface roughness, soil moisture, and incident angle. A comparison with the AIEM simulated data indicates that the absolute error of effective reflectivity estimated by the parameterized soil surface emissivity model is small with a magnitude of 10 −2 . Validation through experimental measurements suggests that a good agreement could be obtained. The parameterized soil surface emissivity model is applied to simulate satellite measurements of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). Compared with the commonly-used microwave land emissivity model developed by Weng et al. (2001), the simulation results using the parameterized soil surface emissivity model yield a lower root-mean-square error (RMSE) and the overall errors are reduced, particularly for horizontal polarization. The newly-developed parameterized soil surface emissivity model should be useful in improving our understanding and modeling the measurements of passive microwave radiometers.

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