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
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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
2021-05Department
Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology); Massachusetts Institute of Technology. Department of Civil and Environmental EngineeringJournal
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