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dc.contributor.authorYao, Panpan
dc.contributor.authorLu, Hui
dc.contributor.authorShi, Jiancheng
dc.contributor.authorZhao, Tianjie
dc.contributor.authorYang, Kun
dc.contributor.authorCosh, Michael H
dc.contributor.authorGianotti, Daniel J Short
dc.contributor.authorEntekhabi, Dara
dc.date.accessioned2021-10-13T18:25:59Z
dc.date.available2021-10-13T18:25:59Z
dc.date.issued2021-05
dc.date.submitted2020-10
dc.identifier.issn2052-4463
dc.identifier.urihttps://hdl.handle.net/1721.1/132957
dc.description.abstractLong term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36km resolution (2002–2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m3 /m3 . NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observationdriven dataset spans nearly two decades at present, and is extendable through the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.en_US
dc.language.isoen
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1038/S41597-021-00925-8en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceScientific Dataen_US
dc.titleA long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)en_US
dc.typeArticleen_US
dc.identifier.citationYao, P., Lu, H., Shi, J. et al. A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019). Sci Data 8, 143 (2021).en_US
dc.contributor.departmentParsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology)
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.relation.journalScientific Dataen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-10-13T17:27:35Z
dspace.orderedauthorsYao, P; Lu, H; Shi, J; Zhao, T; Yang, K; Cosh, MH; Gianotti, DJS; Entekhabi, Den_US
dspace.date.submission2021-10-13T17:27:41Z
mit.journal.volume8en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work Neededen_US


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