A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019)
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 Advance...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/132957 |
| _version_ | 1811082192627957760 |
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| author | Yao, Panpan Lu, Hui Shi, Jiancheng Zhao, Tianjie Yang, Kun Cosh, Michael H Gianotti, Daniel J Short Entekhabi, Dara |
| author2 | Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology) |
| author_facet | Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology) Yao, Panpan Lu, Hui Shi, Jiancheng Zhao, Tianjie Yang, Kun Cosh, Michael H Gianotti, Daniel J Short Entekhabi, Dara |
| author_sort | Yao, Panpan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:59:03Z |
| format | Article |
| id | mit-1721.1/132957 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:59:03Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1329572024-06-05T20:42:10Z A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) Yao, Panpan Lu, Hui Shi, Jiancheng Zhao, Tianjie Yang, Kun Cosh, Michael H Gianotti, Daniel J Short Entekhabi, Dara Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology) Massachusetts Institute of Technology. Department of Civil and Environmental Engineering 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. 2021-10-13T18:25:59Z 2021-10-13T18:25:59Z 2021-05 2020-10 2021-10-13T17:27:35Z Article http://purl.org/eprint/type/JournalArticle 2052-4463 https://hdl.handle.net/1721.1/132957 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 10.1038/S41597-021-00925-8 Scientific Data Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Scientific Data |
| spellingShingle | Yao, Panpan Lu, Hui Shi, Jiancheng Zhao, Tianjie Yang, Kun Cosh, Michael H Gianotti, Daniel J Short Entekhabi, Dara A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title_full | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title_fullStr | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title_full_unstemmed | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title_short | A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2019) |
| title_sort | long term global daily soil moisture dataset derived from amsr e and amsr2 2002 2019 |
| url | https://hdl.handle.net/1721.1/132957 |
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