The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E
This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the interna...
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
2017
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Online Access: | https://hal.science/hal-01595237 https://doi.org/10.1016/j.rse.2016.11.026 |
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ftutoulouse3hal:oai:HAL:hal-01595237v1 |
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openpolar |
institution |
Open Polar |
collection |
Université Toulouse III - Paul Sabatier: HAL-UPS |
op_collection_id |
ftutoulouse3hal |
language |
English |
topic |
remote sensing radiometer soil moisture and ocean salinity télédétection radiomètre acquisition de données capteur smos ndvi réseau de mesures humidité du sol [SDV]Life Sciences [q-bio] |
spellingShingle |
remote sensing radiometer soil moisture and ocean salinity télédétection radiomètre acquisition de données capteur smos ndvi réseau de mesures humidité du sol [SDV]Life Sciences [q-bio] van Der Schalie, Robin de Jeu, Richard A. M. Kerr, Yann H. Wigneron, Jean-Pierre Rodriguez‐fernandez, Nemesio Al Yaari, Amen Parinussa, Robert Mathijs Mecklenburg, Susanne Drusch, Matthias The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
topic_facet |
remote sensing radiometer soil moisture and ocean salinity télédétection radiomètre acquisition de données capteur smos ndvi réseau de mesures humidité du sol [SDV]Life Sciences [q-bio] |
description |
This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a ... |
author2 |
Faculty of Earth and Life Sciences Vrije Universiteit Amsterdam Amsterdam (VU) Space Technology Center Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Interactions Sol Plante Atmosphère (UMR ISPA) Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro) Agence Spatiale Européenne = European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) |
format |
Article in Journal/Newspaper |
author |
van Der Schalie, Robin de Jeu, Richard A. M. Kerr, Yann H. Wigneron, Jean-Pierre Rodriguez‐fernandez, Nemesio Al Yaari, Amen Parinussa, Robert Mathijs Mecklenburg, Susanne Drusch, Matthias |
author_facet |
van Der Schalie, Robin de Jeu, Richard A. M. Kerr, Yann H. Wigneron, Jean-Pierre Rodriguez‐fernandez, Nemesio Al Yaari, Amen Parinussa, Robert Mathijs Mecklenburg, Susanne Drusch, Matthias |
author_sort |
van Der Schalie, Robin |
title |
The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
title_short |
The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
title_full |
The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
title_fullStr |
The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
title_full_unstemmed |
The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E |
title_sort |
merging of radiative transfer based surface soil moisture data from smos and amsr-e |
publisher |
HAL CCSD |
publishDate |
2017 |
url |
https://hal.science/hal-01595237 https://doi.org/10.1016/j.rse.2016.11.026 |
genre |
albedo |
genre_facet |
albedo |
op_source |
ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment https://hal.science/hal-01595237 Remote Sensing of Environment, 2017, 189, pp.180-193. ⟨10.1016/j.rse.2016.11.026⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2016.11.026 hal-01595237 https://hal.science/hal-01595237 doi:10.1016/j.rse.2016.11.026 PRODINRA: 383169 WOS: 000393005400014 |
op_rights |
http://creativecommons.org/licenses/by-sa/ |
op_doi |
https://doi.org/10.1016/j.rse.2016.11.026 |
container_title |
Remote Sensing of Environment |
container_volume |
189 |
container_start_page |
180 |
op_container_end_page |
193 |
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1810486675713818624 |
spelling |
ftutoulouse3hal:oai:HAL:hal-01595237v1 2024-09-15T17:36:01+00:00 The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E van Der Schalie, Robin de Jeu, Richard A. M. Kerr, Yann H. Wigneron, Jean-Pierre Rodriguez‐fernandez, Nemesio Al Yaari, Amen Parinussa, Robert Mathijs Mecklenburg, Susanne Drusch, Matthias Faculty of Earth and Life Sciences Vrije Universiteit Amsterdam Amsterdam (VU) Space Technology Center Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Interactions Sol Plante Atmosphère (UMR ISPA) Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro) Agence Spatiale Européenne = European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) 2017 https://hal.science/hal-01595237 https://doi.org/10.1016/j.rse.2016.11.026 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2016.11.026 hal-01595237 https://hal.science/hal-01595237 doi:10.1016/j.rse.2016.11.026 PRODINRA: 383169 WOS: 000393005400014 http://creativecommons.org/licenses/by-sa/ ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment https://hal.science/hal-01595237 Remote Sensing of Environment, 2017, 189, pp.180-193. ⟨10.1016/j.rse.2016.11.026⟩ remote sensing radiometer soil moisture and ocean salinity télédétection radiomètre acquisition de données capteur smos ndvi réseau de mesures humidité du sol [SDV]Life Sciences [q-bio] info:eu-repo/semantics/article Journal articles 2017 ftutoulouse3hal https://doi.org/10.1016/j.rse.2016.11.026 2024-06-25T00:22:00Z This paper evaluates a methodology to integrate surface soil moisture retrievals from SMOS and AMSR-E into a single, consistent dataset retrieved by the Land Parameter Retrieval Model (LPRM). In a first step, the SMOS LPRM soil moisture retrievals were used as the baseline for optimizing the internal parameterization (i.e. surface roughness and single scattering albedo) of the AMSR-E LPRM retrievals. Secondly, to overcome the uniqueness of these datasets a linear scaling approach was applied resulting in a consistent soil moisture dataset. The new parameter set from the first step is similar for the two (low) frequencies of AMSR-E (i.e. C- and X-band) further improving their inter-comparability for both soil moisture and vegetation optical depth. Soil moisture retrievals from these AMSR-E frequencies were globally merged based on the availability of brightness temperatures that are free from RFI contamination (resulting in AMSR-E LPRMN). This new product was evaluated against both the SMOS LPRM product in the overlapping period (July 2010 to October 2011), as well as the standard, publicly available AMSR-E LPRM dataset (AMSR-E LPRMV3) for an almost 9 year period (January 2003 to October 2011). For the overlapping period, the AMSR-E and SMOS LPRM products show high temporal correlation coefficients (0.60 < R < 0.90) and low root mean square errors (rmse < 0.04 m3 m− 3) for NDVI values up to 0.60. Their agreement tends to drop over the well-known challenging areas such as the arctic region and tropical rainforest. A detailed evaluation over in situ sites from 5 in situ networks worldwide showed that AMSR-E LPRMN often outperforms SMOS LPRM in sparsely vegetated areas, with generally higher correlation coefficients in areas with NDVI < 0.3, and in general a lower unbiased rmse (ubrmse). In line with theoretical expectations, SMOS LPRM outperforms the AMSR-E LPRM product over the more densely vegetated areas. The newly developed AMSR-E LPRMN product was also compared against AMSR-E LPRMV3, revealing a ... Article in Journal/Newspaper albedo Université Toulouse III - Paul Sabatier: HAL-UPS Remote Sensing of Environment 189 180 193 |