A new parameterization of the effective temperature for L band radiometry

Holmes, Thomas; Rosnay, Patricia de; Jeu, Richard de; Wigneron, Jean‐Pierre; Kerr, Yann H.; Calvet, Jean‐Christophe; Escorihuela, María José; Saleh, Khaldoun; Lemaitre, F.

doi:10.1029/2006gl025724

Cited by 72 publications

(42 citation statements)

References 10 publications

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“…The essence of T e f f calculation is a series of weighting values which reflect the soil temperature gradient (for example, the Choudhury's scheme) [29] and further the impact of soil moisture, for example, Wigneron's [34] and Holmes' scheme [31]. Keeping this in mind, we can simplify the soil temperature gradient by normalizing it as follows:…”

Section: Characteristic Expression Of T E F Fmentioning

confidence: 99%

“…The accuracy of this inference depends on whether the linear assumption is satisfied which is basically the case if more layers are observed (e.g., via the use of Lv's multilayer T e f f scheme). [29,31,34] ∆x i soil thickness at ith layer m…”

Section: Microwave Radiative Transfer Modelmentioning

confidence: 99%

“…The weighting function is supposed to reflect the impact of soil moisture on the soil effective temperature. However, there is no variable of depth contained in Choudhury's [29], Wigneron's [30] or Holmes' [31] T e f f schemes. As indicated by the integral scheme, the weighting function would be more representative if it considers the influence of both soil moisture and soil temperature.…”

Section: Introductionmentioning

confidence: 99%

“…In general, all current two-layer T e f f schemes use a weighting function for the soil temperature between upper layer and deeper layer. Such weighting function can be a constant [29], a fitting function [30,31], or an exponential function [15,16]. The weighting function is supposed to reflect the impact of soil moisture on the soil effective temperature.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Zeng

Wen

et al. 2018

Remote Sensing

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Abstract:Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the longer L-band wavelength of these satellites, the emitting behavior of the land becomes very complex due to the unknown deeper penetration depth. This complexity leads to more uncertainty in calibration and validation of satellite soil moisture product and their applications. In the framework of zeroth-order incoherent microwave radiative transfer model, the soil effective temperature is the only component that contains depth information and thus provides the necessary link to quantify the penetration depth. By means of the multi-layer soil effective temperature (Lv's T e f f ) scheme, we have determined the relationship between the penetration depth and soil effective temperature and verified it against field observations at the Maqu Network. The key findings are that the penetration depth can be estimated according to Lv's T e f f scheme with the assumption of linear soil temperature gradient along the optical depth; and conversely, the soil temperature at the penetration depth should be equal to the soil effective temperature with the same linear assumption. The accuracy of this inference depends on to what extent the assumption of linear soil temperature gradient is satisfied. The result of this study is expected to advance understanding of the soil moisture products retrieved by SMOS and SMAP and improve the techniques in data assimilation and climate research.

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Section: Characteristic Expression Of T E F Fmentioning

confidence: 99%

Section: Microwave Radiative Transfer Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Zeng

Wen

et al. 2018

Remote Sensing

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“…This adjustable parameter can be calculated using the imaginary (Ei) and real (Er) soil dielectric constant variables [30]:…”

Section: Soil Effective Temperaturementioning

confidence: 99%

Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation

et al. 2015

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Abstract:The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model. This study evaluated the applicability of this model to the Heihe River Basin in Northern China for specific underlying surfaces by simulating brightness temperature (BT) with the L-MEB model. To analyze the influence of a ground sampling strategy on the simulations, two resampling methods based on ground observations were compared. In the first method, the simulated BT of each point observation was initially acquired. The simulations were then resampled at a 1 km resolution. The other method was based on gridded data with a resolution of 1 km averaged from point observations, such as soil moisture, soil temperature, and soil texture. The simulated BTs at a 1 km resolution were then obtained using the L-MEB model. Because of the large variability in soil moisture, the resampling method based on gridded data was used in the simulation. The simulated BTs based on the calibrated parameters were validated using airborne L-band data from the Polarimetric L-band Multibeam Radiometer (PLMR) acquired during the HiWATER project. The root mean square errors (RMSEs) between the simulated results and the PLMR data were 6 to 7 K for V-polarization and 3 to 5 K for H-polarization at different angles. These results OPEN ACCESSRemote Sens. 2015, 7 10879 demonstrate that the model effectively represents agricultural land surfaces, and this study will serve as a reference for applying the L-MEB model in arid regions and for selecting a ground sampling strategy.

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Potential of bias correction for downscaling passive microwave and soil moisture data

2015

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Passive microwave satellites such as Soil Moisture and Ocean Salinity or Soil Moisture Active Passive observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disaggregate soil moisture to a scale more appropriate for hydrological applications. Temporal stability of soil moisture and TB was demonstrated at the Little Washita and Little River Experimental Watersheds using in situ observations and the Community Microwave Emissions Model. Decomposition of the mean square difference (MSD) between the watershed average soil moisture and TB showed that bias was a major contributor to differences between watershed average and local-scale soil moisture and TB, particularly at sites with high MSD. The mean RMSD between watershed average and local soil moisture was 0.04 m 3 m À3 and 0.06 m 3 m À3 at Little River and Little Washita, respectively. Following a simple bias correction the RMSD was reduced to 0.03 m 3 m À3 at both sites. Considering multiple incidence angles at both horizontal and vertical polarization, bias correction of watershed average TB V reduced the RMSD by approximately 75% and 45% and TB H RMSD by 68% and 36% for Little River and Little Washita, respectively, at all incidence angles. Therefore, at subsatellite grid scale, bias correction can be considered a viable technique for downscaling passive microwave observations and soil moisture retrievals.

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A new parameterization of the effective temperature for L band radiometry

Cited by 72 publications

References 10 publications

Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Estimation of Penetration Depth from Soil Effective Temperature in Microwave Radiometry

Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation

Potential of bias correction for downscaling passive microwave and soil moisture data

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