A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015

Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sens...

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Autores principales: Cui, Yaokui, Zeng, Chao, Zhou, Jie, Xie, Hongjie, Wan, Wei, Hu, Ling, Xiong, Wentao, Chen, Xi, Fan, Wenjie, Hong, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Data Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823363/
https://www.ncbi.nlm.nih.gov/pubmed/31672975
http://dx.doi.org/10.1038/s41597-019-0228-x
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author Cui, Yaokui
Zeng, Chao
Zhou, Jie
Xie, Hongjie
Wan, Wei
Hu, Ling
Xiong, Wentao
Chen, Xi
Fan, Wenjie
Hong, Yang
author_facet Cui, Yaokui
Zeng, Chao
Zhou, Jie
Xie, Hongjie
Wan, Wei
Hu, Ling
Xiong, Wentao
Chen, Xi
Fan, Wenjie
Hong, Yang
author_sort Cui, Yaokui
collection PubMed
description Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.
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spelling pubmed-68233632019-11-07 A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015 Cui, Yaokui Zeng, Chao Zhou, Jie Xie, Hongjie Wan, Wei Hu, Ling Xiong, Wentao Chen, Xi Fan, Wenjie Hong, Yang Sci Data Data Descriptor Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002–2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP. Nature Publishing Group UK 2019-10-31 /pmc/articles/PMC6823363/ /pubmed/31672975 http://dx.doi.org/10.1038/s41597-019-0228-x Text en © The Author(s) 2019 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Cui, Yaokui
Zeng, Chao
Zhou, Jie
Xie, Hongjie
Wan, Wei
Hu, Ling
Xiong, Wentao
Chen, Xi
Fan, Wenjie
Hong, Yang
A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title_full A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title_fullStr A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title_full_unstemmed A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title_short A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015
title_sort spatio-temporal continuous soil moisture dataset over the tibet plateau from 2002 to 2015
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6823363/
https://www.ncbi.nlm.nih.gov/pubmed/31672975
http://dx.doi.org/10.1038/s41597-019-0228-x
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