| dc.contributor.author | Yao, Panpan | |
| dc.contributor.author | Lu, Hui | |
| dc.contributor.author | Shi, Jiancheng | |
| dc.contributor.author | Zhao, Tianjie | |
| dc.contributor.author | Yang, Kun | |
| dc.contributor.author | Cosh, Michael H | |
| dc.contributor.author | Gianotti, Daniel J Short | |
| dc.contributor.author | Entekhabi, Dara | |
| dc.date.accessioned | 2021-10-13T18:25:59Z | |
| dc.date.available | 2021-10-13T18:25:59Z | |
| dc.date.issued | 2021-05 | |
| dc.date.submitted | 2020-10 | |
| dc.identifier.issn | 2052-4463 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/132957 | |
| dc.description.abstract | Long 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.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | en_US |
| dc.relation.isversionof | 10.1038/S41597-021-00925-8 | en_US |
| dc.rights | Creative Commons Attribution 4.0 International license | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Scientific Data | en_US |
| dc.title | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Yao, 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.department | Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology) | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering | |
| dc.relation.journal | Scientific Data | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2021-10-13T17:27:35Z | |
| dspace.orderedauthors | Yao, P; Lu, H; Shi, J; Zhao, T; Yang, K; Cosh, MH; Gianotti, DJS; Entekhabi, D | en_US |
| dspace.date.submission | 2021-10-13T17:27:41Z | |
| mit.journal.volume | 8 | en_US |
| mit.journal.issue | 1 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work Needed | en_US |