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A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015
Lake surface water temperature (LSWT) is of vital importance for hydrological and meteorological studies. The LSWT ground measurements in the Tibetan Plateau (TP) were quite scarce because of its harsh environment. Thermal infrared remote sensing is a reliable way to calculate historical LSWT. In th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497724/ https://www.ncbi.nlm.nih.gov/pubmed/31048686 http://dx.doi.org/10.1038/s41597-019-0040-7 |
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author | Liu, Baojian Wan, Wei Xie, Hongjie Li, Huan Zhu, Siyu Zhang, Guoqing Wen, Lijuan Hong, Yang |
author_facet | Liu, Baojian Wan, Wei Xie, Hongjie Li, Huan Zhu, Siyu Zhang, Guoqing Wen, Lijuan Hong, Yang |
author_sort | Liu, Baojian |
collection | PubMed |
description | Lake surface water temperature (LSWT) is of vital importance for hydrological and meteorological studies. The LSWT ground measurements in the Tibetan Plateau (TP) were quite scarce because of its harsh environment. Thermal infrared remote sensing is a reliable way to calculate historical LSWT. In this study, we present the first and longest 35-year (1981–2015) daytime lake-averaged LSWT data of 97 large lakes (>80 km(2) each) in the TP using the 4-km Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data. The LSWT dataset, taking advantage of observations from NOAA’s afternoon satellites, includes three time scales, i.e., daily, 8-day-averaged, and monthly-averaged. The AVHRR-derived LSWT has a similar accuracy (RMSE = 1.7 °C) to that from other data products such as MODIS (RMSE = 1.7 °C) and ARC-Lake (RMSE = 2.0 °C). An inter-comparison of different sensors indicates that for studies such as those considering long-term climate change, the relative bias of different AVHRR sensors cannot be ignored. The proposed dataset should be, to some extent, a valuable asset for better understanding the hydrologic/climatic property and its changes over the TP. |
format | Online Article Text |
id | pubmed-6497724 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64977242019-05-03 A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 Liu, Baojian Wan, Wei Xie, Hongjie Li, Huan Zhu, Siyu Zhang, Guoqing Wen, Lijuan Hong, Yang Sci Data Data Descriptor Lake surface water temperature (LSWT) is of vital importance for hydrological and meteorological studies. The LSWT ground measurements in the Tibetan Plateau (TP) were quite scarce because of its harsh environment. Thermal infrared remote sensing is a reliable way to calculate historical LSWT. In this study, we present the first and longest 35-year (1981–2015) daytime lake-averaged LSWT data of 97 large lakes (>80 km(2) each) in the TP using the 4-km Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data. The LSWT dataset, taking advantage of observations from NOAA’s afternoon satellites, includes three time scales, i.e., daily, 8-day-averaged, and monthly-averaged. The AVHRR-derived LSWT has a similar accuracy (RMSE = 1.7 °C) to that from other data products such as MODIS (RMSE = 1.7 °C) and ARC-Lake (RMSE = 2.0 °C). An inter-comparison of different sensors indicates that for studies such as those considering long-term climate change, the relative bias of different AVHRR sensors cannot be ignored. The proposed dataset should be, to some extent, a valuable asset for better understanding the hydrologic/climatic property and its changes over the TP. Nature Publishing Group UK 2019-05-02 /pmc/articles/PMC6497724/ /pubmed/31048686 http://dx.doi.org/10.1038/s41597-019-0040-7 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 Liu, Baojian Wan, Wei Xie, Hongjie Li, Huan Zhu, Siyu Zhang, Guoqing Wen, Lijuan Hong, Yang A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title | A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title_full | A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title_fullStr | A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title_full_unstemmed | A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title_short | A long-term dataset of lake surface water temperature over the Tibetan Plateau derived from AVHRR 1981–2015 |
title_sort | long-term dataset of lake surface water temperature over the tibetan plateau derived from avhrr 1981–2015 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497724/ https://www.ncbi.nlm.nih.gov/pubmed/31048686 http://dx.doi.org/10.1038/s41597-019-0040-7 |
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