Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations
| hal.structure.identifier | Interactions Sol Plante Atmosphère [UMR ISPA] | |
| dc.contributor.author | AL-YAARI, A. | |
| hal.structure.identifier | Interactions Sol Plante Atmosphère [UMR ISPA] | |
| dc.contributor.author | WIGNERON, J.P. | |
| hal.structure.identifier | Centre d'études spatiales de la biosphère [CESBIO] | |
| dc.contributor.author | KERR, Yann H. | |
| hal.structure.identifier | University of Amsterdam [Amsterdam] = Universiteit van Amsterdam [UvA] | |
| dc.contributor.author | DE JEU, R. | |
| hal.structure.identifier | Centre d'études spatiales de la biosphère [CESBIO] | |
| dc.contributor.author | RODRIGUEZ‐FERNANDEZ, Nemesio | |
| hal.structure.identifier | University of Amsterdam [Amsterdam] = Universiteit van Amsterdam [UvA] | |
| dc.contributor.author | VAN DER SCHALIE, R. | |
| hal.structure.identifier | Interactions Sol Plante Atmosphère [UMR ISPA] | |
| dc.contributor.author | AHMAD, Al Bitar | |
| hal.structure.identifier | Centre d'études spatiales de la biosphère [CESBIO] | |
| dc.contributor.author | MIALON, Arnaud | |
| hal.structure.identifier | Centre d'études spatiales de la biosphère [CESBIO] | |
| dc.contributor.author | RICHAUME, Philippe | |
| hal.structure.identifier | Faculty of Earth and Life Sciences [Amsterdam] [FALW] | |
| dc.contributor.author | DOLMAN, A. | |
| hal.structure.identifier | Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols [METIS] | |
| hal.structure.identifier | Institut Pierre-Simon-Laplace [IPSL] | |
| dc.contributor.author | DUCHARNE, Agnès | |
| dc.date.accessioned | 2024-04-08T12:12:03Z | |
| dc.date.available | 2024-04-08T12:12:03Z | |
| dc.date.issued | 2016 | |
| dc.identifier.issn | 0034-4257 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/196731 | |
| dc.description.abstractEn | Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R = 0.60 and mean global RMSE = 0.057 m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055 m3/m3 for AMSR-reg and 0.084 m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.subject | SMOS | |
| dc.subject | soil moisture | |
| dc.subject.en | AMSR-E | |
| dc.subject.en | statistical regression | |
| dc.title.en | Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.rse.2015.11.022 | |
| dc.subject.hal | Planète et Univers [physics]/Sciences de la Terre/Hydrologie | |
| dc.subject.hal | Planète et Univers [physics]/Interfaces continentales, environnement | |
| bordeaux.journal | Remote Sensing of Environment | |
| bordeaux.page | 453–464 | |
| bordeaux.volume | 180 | |
| bordeaux.hal.laboratories | Interactions Soil Plant Atmosphere (ISPA) - UMR 1391 | * |
| bordeaux.institution | Bordeaux Sciences Agro | |
| bordeaux.institution | INRAE | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01312307 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01312307v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2016&rft.volume=180&rft.spage=453%E2%80%93464&rft.epage=453%E2%80%93464&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=AL-YAARI,%20A.&WIGNERON,%20J.P.&KERR,%20Yann%20H.&DE%20JEU,%20R.&RODRIGUEZ%E2%80%90FERNANDEZ,%20Nemesio&rft.genre=article |
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