Spatial distribution and possible sources of SMOS errors at the global scale
SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the t...
Published in: | Remote Sensing of Environment |
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Main Authors: | , , , |
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2013
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Online Access: | https://hal.ird.fr/ird-00828769 https://hal.ird.fr/ird-00828769/document https://hal.ird.fr/ird-00828769/file/2013RSE_spatial_distribution.pdf https://doi.org/10.1016/j.rse.2013.02.017 |
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ftccsdartic:oai:HAL:ird-00828769v1 |
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Open Polar |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
Triple collocation SMOS Error structure Soil moisture Multiple linear regression Analysis of variance [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems |
spellingShingle |
Triple collocation SMOS Error structure Soil moisture Multiple linear regression Analysis of variance [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems Leroux, Delphine Kerr, Yann H. Richaume, Philippe Fieuzal, Rémy Spatial distribution and possible sources of SMOS errors at the global scale |
topic_facet |
Triple collocation SMOS Error structure Soil moisture Multiple linear regression Analysis of variance [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems |
description |
SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)-LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia. |
author2 |
Centre d'études spatiales de la biosphère (CESBIO) Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS) Telespazio Services par satellites |
format |
Article in Journal/Newspaper |
author |
Leroux, Delphine Kerr, Yann H. Richaume, Philippe Fieuzal, Rémy |
author_facet |
Leroux, Delphine Kerr, Yann H. Richaume, Philippe Fieuzal, Rémy |
author_sort |
Leroux, Delphine |
title |
Spatial distribution and possible sources of SMOS errors at the global scale |
title_short |
Spatial distribution and possible sources of SMOS errors at the global scale |
title_full |
Spatial distribution and possible sources of SMOS errors at the global scale |
title_fullStr |
Spatial distribution and possible sources of SMOS errors at the global scale |
title_full_unstemmed |
Spatial distribution and possible sources of SMOS errors at the global scale |
title_sort |
spatial distribution and possible sources of smos errors at the global scale |
publisher |
HAL CCSD |
publishDate |
2013 |
url |
https://hal.ird.fr/ird-00828769 https://hal.ird.fr/ird-00828769/document https://hal.ird.fr/ird-00828769/file/2013RSE_spatial_distribution.pdf https://doi.org/10.1016/j.rse.2013.02.017 |
genre |
National Snow and Ice Data Center |
genre_facet |
National Snow and Ice Data Center |
op_source |
ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment https://hal.ird.fr/ird-00828769 Remote Sensing of Environment, Elsevier, 2013, 133, pp.240-250. ⟨10.1016/j.rse.2013.02.017⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2013.02.017 ird-00828769 https://hal.ird.fr/ird-00828769 https://hal.ird.fr/ird-00828769/document https://hal.ird.fr/ird-00828769/file/2013RSE_spatial_distribution.pdf doi:10.1016/j.rse.2013.02.017 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1016/j.rse.2013.02.017 |
container_title |
Remote Sensing of Environment |
container_volume |
133 |
container_start_page |
240 |
op_container_end_page |
250 |
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1766071714676998144 |
spelling |
ftccsdartic:oai:HAL:ird-00828769v1 2023-05-15T17:14:21+02:00 Spatial distribution and possible sources of SMOS errors at the global scale Leroux, Delphine Kerr, Yann H. Richaume, Philippe Fieuzal, Rémy Centre d'études spatiales de la biosphère (CESBIO) Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3) Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS) Telespazio Services par satellites 2013 https://hal.ird.fr/ird-00828769 https://hal.ird.fr/ird-00828769/document https://hal.ird.fr/ird-00828769/file/2013RSE_spatial_distribution.pdf https://doi.org/10.1016/j.rse.2013.02.017 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2013.02.017 ird-00828769 https://hal.ird.fr/ird-00828769 https://hal.ird.fr/ird-00828769/document https://hal.ird.fr/ird-00828769/file/2013RSE_spatial_distribution.pdf doi:10.1016/j.rse.2013.02.017 info:eu-repo/semantics/OpenAccess ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment https://hal.ird.fr/ird-00828769 Remote Sensing of Environment, Elsevier, 2013, 133, pp.240-250. ⟨10.1016/j.rse.2013.02.017⟩ Triple collocation SMOS Error structure Soil moisture Multiple linear regression Analysis of variance [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems info:eu-repo/semantics/article Journal articles 2013 ftccsdartic https://doi.org/10.1016/j.rse.2013.02.017 2021-10-24T15:00:36Z SMOS (Soil Moisture and Ocean Salinity) data have now been available for over two years and, as part of the validation process, comparing this new dataset to already existing global datasets of soil moisture is possible. In this study, SMOS soil moisture product was evaluated globally by using the triple collocation method. This statistical method is based on the comparison of three datasets and produces global error maps by statistically inter-comparing their variations. Only the variable part of the errors are considered here, the bias errors are not treated by triple collocation. This method was applied to the following datasets: SMOS Level 2 product, two soil moisture products derived from AMSR-E (Advanced Microwave Scanning Radiometer)-LPRM (Land Parameter Retrieval Model) and NSIDC (National Snow and Ice Data Center), ASCAT (Advanced Scatterometer) and ECMWF (European Center for Medium range Weather Forecasting). The resulting errors are not absolute since they depend on the choice of the datasets. However this study showed that the spatial structure of the SMOS was independent of the combination and pointed out the same areas where SMOS performed well and where it did not. This global SMOS error map was then linked to other global parameters such as soil texture, RFI (Radio Frequency Interference) occurrence probabilities and land cover in order to identify their influences in the SMOS error. Globally the presence of forest in the field of view of the radiometer seemed to have the greatest influence on SMOS error (56.8%) whereas RFI represented 1.7% according to the analysis of variance from a multiple linear regression model. These percentages were not identical for all the continents and some discrepancies in the proportion of the influence were highlighted: soil texture was the main influence over Europe whereas RFI had the largest influence over Asia. Article in Journal/Newspaper National Snow and Ice Data Center Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Remote Sensing of Environment 133 240 250 |