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Philippe Richaume - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Arnaud Mialon, Philippe Richaume, Jeanpierre Wigneron, Delphine Leroux, Simone Bircher, Ahmad Albitar, Thierry Pellarin, Y.h. KerrAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The Retrieval Algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS Algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS Retrieval Algorithm converges to realistic soil moisture Retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.
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smos level 2 Retrieval Algorithm over forests description and generation of global maps
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013Co-Authors: Rachid Rahmoune, P Ferrazzoli, Y Kerr, Philippe RichaumeAbstract:This paper shows global maps of optical depth and soil moisture over land, obtained using the last prototype of SMOS Level 2 Retrieval Algorithm, which will be implemented in V600 version of Level 2 product made available by European Space Agency (ESA). The focus is on forested areas, where the approach adopted to develop the Algorithm can be subdivided into different steps. First a theoretical model, which was previously developed and tested using ground based and airborne measurements, generated parametric outputs. By fitting this output data set, the albedo and the optical depth of a simple first order radiative transfer model were estimated. Then, this simplified forest model was included in the general ESA Level 2 Retrieval Algorithm over land, described in the Algorithm Theroretical Baseline Document (ATBD). The paper describes the details of this procedure and shows some Retrieval results. First, the prototype Algorithm was run with three free parameters: Soil moisture, optical depth, and albedo. The retrieved albedo resulted to be close to the initial estimate (0.08) for Boreal forests, while it was lower for Tropical forests. Running again the Algorithm with the albedo fixed, a global map of optical depth was generated. The spatial features of the map follow the global information about forest biomass and forest height available in the literature. Finally it was found that, on average, the influence of seasonal effects on optical depth is moderate.
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the smos soil moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Yann Kerr, P Waldteufel, Philippe Richaume, Jeanpierre Wigneron, P Ferrazzoli, Ali Mahmoodi, Ahmad Al Bitar, F Cabot, C Gruhier, S JugleaAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 Algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a Retrieval Algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS Algorithm theoretical basis documents to be used to produce the operational Algorithm. The principle of the SM Retrieval Algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The Algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the Algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.
Y.h. Kerr - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Arnaud Mialon, Philippe Richaume, Jeanpierre Wigneron, Delphine Leroux, Simone Bircher, Ahmad Albitar, Thierry Pellarin, Y.h. KerrAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The Retrieval Algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS Algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS Retrieval Algorithm converges to realistic soil moisture Retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.
Jeanpierre Wigneron - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Dobson and Mironov Dielectric Models in the SMOS Soil Moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Arnaud Mialon, Philippe Richaume, Jeanpierre Wigneron, Delphine Leroux, Simone Bircher, Ahmad Albitar, Thierry Pellarin, Y.h. KerrAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission provides global surface soil moisture over the continental land surfaces. The Retrieval Algorithm is based on the comparison between the observations of the L-band (1.4 GHz) brightness temperatures (TB) and the simulated TB data using the L-band Microwave Emission of the Biosphere (L-MEB) model. The L-MEB model includes a dielectric model for the computation of the soil dielectric constant. Since the beginning of the mission, the Dobson model has been used in the operational SMOS Algorithm. Recently, a new model of the soil dielectric constant has been developed by Mironov et al. and is now considered. This paper is the first evaluation of these two models based on the actual SMOS observations. First, both Dobson and Mironov models were modified to ensure that the SMOS Retrieval Algorithm converges to realistic soil moisture Retrievals (symmetrization for negative soil moisture values was applied). Second, soil moisture was retrieved over several sites using both Dobson and Mironov models to compute the soil dielectric constant and were compared with in situ measurements. At a global scale, the use of the Mironov model leads to higher retrieved soil moisture than when using the Dobson model (0.033 m3/m3 on average). However, the comparisons of the two model output with in situ measurements over various test sites do not demonstrate a superior performance of one model over the other.
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the smos soil moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Yann Kerr, P Waldteufel, Philippe Richaume, Jeanpierre Wigneron, P Ferrazzoli, Ali Mahmoodi, Ahmad Al Bitar, F Cabot, C Gruhier, S JugleaAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 Algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a Retrieval Algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS Algorithm theoretical basis documents to be used to produce the operational Algorithm. The principle of the SM Retrieval Algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The Algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the Algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.
J. P. Burrows - One of the best experts on this subject based on the ideXlab platform.
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The semianalytical cloud Retrieval Algorithm for SCIAMACHY I. The validation
Atmospheric Chemistry and Physics, 2006Co-Authors: A. A. Kokhanovsky, V. V. Rozanov, T. Nauss, C. Reudenbach, J. S. Daniel, H. L. Miller, J. P. BurrowsAbstract:A recently developed cloud Retrieval Algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud Retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer (MODIS) data. The results of the cloud top height Retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and Retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.
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The semianalytical cloud Retrieval Algorithm for SCIAMACHY ? I. The validation
Atmospheric Chemistry and Physics Discussions, 2005Co-Authors: A. A. Kokhanovsky, V. V. Rozanov, T. Nauss, C. Reudenbach, J. S. Daniel, H. L. Miller, J. P. BurrowsAbstract:A recently developed cloud Retrieval Algorithm for the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) is briefly presented and validated using independent and well tested cloud Retrieval techniques based on the look-up-table approach for MODeration resolutIon Spectrometer data. The results of the cloud top height Retrievals using measurements in the oxygen A-band by an airborne crossed Czerny-Turner spectrograph and the Global Ozone Monitoring Experiment (GOME) instrument are compared with those obtained from airborne dual photography and Retrievals using data from Along Track Scanning Radiometer (ATSR-2), respectively.
S Juglea - One of the best experts on this subject based on the ideXlab platform.
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the smos soil moisture Retrieval Algorithm
IEEE Transactions on Geoscience and Remote Sensing, 2012Co-Authors: Yann Kerr, P Waldteufel, Philippe Richaume, Jeanpierre Wigneron, P Ferrazzoli, Ali Mahmoodi, Ahmad Al Bitar, F Cabot, C Gruhier, S JugleaAbstract:The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 Algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a Retrieval Algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS Algorithm theoretical basis documents to be used to produce the operational Algorithm. The principle of the SM Retrieval Algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The Algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the Algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.
