MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)

Author(s)
Yao, Panpan; Lu, Hui; Shi, Jiancheng; Zhao, Tianjie; Yang, Kun; Cosh, Michael H; Gianotti, Daniel J Short; Entekhabi, Dara; ... Show more Show less
Thumbnail
DownloadPublished version (12.02Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
Metadata
Show full item record
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.
Date issued
2021-05
URI
https://hdl.handle.net/1721.1/132957
Department
Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology); Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Journal
Scientific Data
Publisher
Springer Science and Business Media LLC
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).
Version: Final published version
ISSN
2052-4463

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.