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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 Advanced...

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Autores principales: Yao, Panpan, Lu, Hui, Shi, Jiancheng, Zhao, Tianjie, Yang, Kun, Cosh, Michael H., Gianotti, Daniel J. Short, Entekhabi, Dara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160186/
https://www.ncbi.nlm.nih.gov/pubmed/34045448
http://dx.doi.org/10.1038/s41597-021-00925-8
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author Yao, Panpan
Lu, Hui
Shi, Jiancheng
Zhao, Tianjie
Yang, Kun
Cosh, Michael H.
Gianotti, Daniel J. Short
Entekhabi, Dara
author_facet Yao, Panpan
Lu, Hui
Shi, Jiancheng
Zhao, Tianjie
Yang, Kun
Cosh, Michael H.
Gianotti, Daniel J. Short
Entekhabi, Dara
author_sort Yao, Panpan
collection PubMed
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 36 km resolution (2002–2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m(3)/m(3). NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven 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.
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spelling pubmed-81601862021-06-10 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 Sci Data Data Descriptor 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 36 km resolution (2002–2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m(3)/m(3). NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven 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. Nature Publishing Group UK 2021-05-27 /pmc/articles/PMC8160186/ /pubmed/34045448 http://dx.doi.org/10.1038/s41597-021-00925-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article.
spellingShingle Data Descriptor
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)
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160186/
https://www.ncbi.nlm.nih.gov/pubmed/34045448
http://dx.doi.org/10.1038/s41597-021-00925-8
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