Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

Zhao, Tianjie; Shi, Jiancheng; Mialon, Arnaud; Kerr, Yann H.

doi:10.1016/j.pce.2015.04.001

Cited by 34 publications

(14 citation statements)

References 48 publications

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“…On the one hand, the passive microwave observation is more sensitive to SM while the active microwave observation is more sensitive to roughness (Ma et al, 2015). On the other hand, it is commonly recognized that L-band is more suitable than X-band for SM estimation 290 over vegetated soil (Rosenqvist et al, 2007;Chen et al, 2010;Panciera et al, 2014;Zhao et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

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Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

Wang

2017

Preprint

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The L-band radiometer has demonstrated its strong ability of estimating soil moisture (SM) over vegetated surface. 15However, the present SM products derived from satellite radiometers are hardly directly used in heterogeneous oasis. This may be attributed to the coarse spatial resolution of the satellite-based observations and the defects of the point-estimation algorithms which cannot quantify the uncertainty in SM inversion. This paper presents a SM estimation based on the combination of Bayesian probabilistic inversion with high resolution airborne radiometer observations. The overall objective is to quantify the uncertainty in SM estimation and to provide a desirable SM 20 3) The Bayesian PI improves the simulation performance of forward model via maximum likelihood estimation. 4) The 3P and 2P strategies result in different SM inversion uncertainties. Overall, the present Bayesian PI combining multi-channel L-band observations has obtained a desirable SM estimation and uncertainty quantification, which may offer an insight into the future SM inversion based on passive microwave remote sensing. 30

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Section: Discussionmentioning

confidence: 99%

“…Soil moisture (SM) is a key variable in the process of water and energy balance at the land surface (Zhao et al, 2015;Kim et al, 2015). It usually presents strong spatial and temporal variation, resulting in difficulties of its analysis from in situ measurements, especially over large areas (Chaouch et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

Wang

2017

Preprint

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“…Compared with other previous theoretical models, the AIEM has proved to be accurate in simulating the surface emission signals over a wider range of surface conditions and incident angles [13,19,24]. In the AIEM, the effective surface reflectivity R e p comprises two components: the coherent and non-coherent components.…”

Section: Aiemmentioning

confidence: 99%

“…The correlation length l is defined as the horizontal distance ξ over which the surface profile is autocorrelated with a value larger than 1/e [24]. Assuming that a one-dimensional surface profile contains N points with surface height z i , the autocorrelation function (ACF) can be expressed as follows:…”

Section: Aiemmentioning

confidence: 99%

“…Based on the Q/H model, other semi-empirical models have also been developed by modifying the roughness parameters, such as the Hp model [5,[21][22][23]. On the basis of the physical modeling of the AIEM, Zhao et al [24] developed a parameterized exponentially-correlated surface emissivity model, which is mainly used for retrieving L-band passive microwave soil moisture data. Shi et al [13] developed the Qp model on the basis of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor configurations, wherein the polarization dependence from the effect of the surface roughness was considered.…”

Section: Introductionmentioning

confidence: 99%

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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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Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau

et al. 2017

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Two soil moisture and temperature monitoring networks were established in the Tibetan Plateau (TP) during recent years. One is located in a semihumid area (Naqu) of central TP and consists of 56 soil moisture and temperature measurement (SMTM) stations, the other is located in a semiarid area (Pali) of southern TP and consists of 21 SMTM stations. In this study, the station data are used to evaluate soil moisture retrievals from three microwave satellites, i.e., the Soil Moisture Active Passive (SMAP) of NASA, the Soil Moisture and Ocean Salinity (SMOS) of European Space Agency, and the Advanced Microwave Scanning Radiometer 2 (AMSR2) of Japan Aerospace Exploration Agency. It is found that the SMAP retrievals tend to underestimate soil moisture in the two TP networks, mainly due to the negative biases in the effective soil temperature that is derived from a climate model. However, the SMAP product well captures the amplitude and temporal variation of the soil moisture. The SMOS product performs well in Naqu network with acceptable error metrics but fails to capture the temporal variation of soil moisture in Pali network. The AMSR2 products evidently exaggerate the temporal variation of soil moisture in Naqu network but dampen it in Pali network, suggesting its retrieval algorithm needs further improvements for the TP.

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Parametric exponentially correlated surface emission model for L-band passive microwave soil moisture retrieval

Cited by 34 publications

References 48 publications

Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

Soil Moisture Estimation Based on Probabilistic Inversion over Heterogeneous Vegetated Fields Using Airborne PLMR Brightness Temperature

A Parameterized Microwave Emissivity Model for Bare Soil Surfaces

Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau

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