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A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm

hal.structure.identifierNanjing University [NJU]
dc.contributor.authorHU, Lu
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorZHAO, Tianjie
hal.structure.identifierNanjing University [NJU]
dc.contributor.authorJU, Weimin
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorPENG, Zhiqing
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorSHI, Jiancheng
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRODRÍGUEZ-FERNÁNDEZ, Nemesio
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorJ.-P., Wigneron
hal.structure.identifierUSDA-ARS : Agricultural Research Service
dc.contributor.authorCOSH, Michael
hal.structure.identifierTsinghua University [Beijing] [THU]
dc.contributor.authorYANG, Kun
hal.structure.identifierTsinghua University [Beijing] [THU]
dc.contributor.authorLU, Hui
hal.structure.identifierChinese Academy of Sciences [Beijing] [CAS]
dc.contributor.authorYAO, Panpan
dc.date.accessioned2024-04-08T11:43:44Z
dc.date.available2024-04-08T11:43:44Z
dc.date.issued2023-04-23
dc.identifier.issn0034-4257
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195120
dc.description.abstractEnSoil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions.
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.subject.enSoil moisture
dc.subject.enVegetation optical depth
dc.subject.enMCCA
dc.subject.enAMSR-E
dc.subject.enAMSR2
dc.subject.enFrequency and polarization dependence
dc.title.enA twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm
dc.typeArticle de revue
dc.identifier.doi10.1016/j.rse.2023.113595
dc.subject.halSciences de l'environnement
bordeaux.journalRemote Sensing of Environment
bordeaux.page113595
bordeaux.volume292
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04118657
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04118657v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing%20of%20Environment&rft.date=2023-04-23&rft.volume=292&rft.spage=113595&rft.epage=113595&rft.eissn=0034-4257&rft.issn=0034-4257&rft.au=HU,%20Lu&ZHAO,%20Tianjie&JU,%20Weimin&PENG,%20Zhiqing&SHI,%20Jiancheng&rft.genre=article


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