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Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake
Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surf...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977524/ https://www.ncbi.nlm.nih.gov/pubmed/27502177 http://dx.doi.org/10.1038/srep31251 |
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author | Pareeth, Sajid Salmaso, Nico Adrian, Rita Neteler, Markus |
author_facet | Pareeth, Sajid Salmaso, Nico Adrian, Rita Neteler, Markus |
author_sort | Pareeth, Sajid |
collection | PubMed |
description | Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr(−1) (*P < 0.05), and of 0.036 °C yr(−1) (***P < 0.001) during summer. |
format | Online Article Text |
id | pubmed-4977524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-49775242016-08-22 Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake Pareeth, Sajid Salmaso, Nico Adrian, Rita Neteler, Markus Sci Rep Article Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr(−1) (*P < 0.05), and of 0.036 °C yr(−1) (***P < 0.001) during summer. Nature Publishing Group 2016-08-09 /pmc/articles/PMC4977524/ /pubmed/27502177 http://dx.doi.org/10.1038/srep31251 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Pareeth, Sajid Salmaso, Nico Adrian, Rita Neteler, Markus Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title | Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title_full | Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title_fullStr | Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title_full_unstemmed | Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title_short | Homogenised daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake |
title_sort | homogenised daily lake surface water temperature data generated from multiple satellite sensors: a long-term case study of a large sub-alpine lake |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4977524/ https://www.ncbi.nlm.nih.gov/pubmed/27502177 http://dx.doi.org/10.1038/srep31251 |
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