Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes

Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate chan...

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Main Authors: Gian Lieberherr, Stefan Wunderle
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/7/990
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spelling doaj-26e45e6e7f5f459ab48180e549ee90892020-11-24T22:38:21ZengMDPI AGRemote Sensing2072-42922018-06-0110799010.3390/rs10070990rs10070990Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European LakesGian Lieberherr0Stefan Wunderle1Institute of Geography, University of Bern, Hallerstrasse 12, CH-3012 Bern, SwitzerlandInstitute of Geography, University of Bern, Hallerstrasse 12, CH-3012 Bern, SwitzerlandLake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake’s response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016).http://www.mdpi.com/2072-4292/10/7/990Advanced Very High Resolution Radiometer (AVHRR)Lake Surface Water Temperature (LSWT)long-term data recordtemperature trendintra-annual variability
collection DOAJ
language English
format Article
sources DOAJ
author Gian Lieberherr
Stefan Wunderle
spellingShingle Gian Lieberherr
Stefan Wunderle
Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
Remote Sensing
Advanced Very High Resolution Radiometer (AVHRR)
Lake Surface Water Temperature (LSWT)
long-term data record
temperature trend
intra-annual variability
author_facet Gian Lieberherr
Stefan Wunderle
author_sort Gian Lieberherr
title Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
title_short Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
title_full Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
title_fullStr Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
title_full_unstemmed Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes
title_sort lake surface water temperature derived from 35 years of avhrr sensor data for european lakes
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-06-01
description Lake surface water temperature (LSWT) is an important parameter with which to assess aquatic ecosystems and to study the lake’s response to climate change. The AVHRR archive of the University of Bern offers great potential to derive consistent LSWT data suited for the study of climate change and lake dynamics. To derive such a dataset, challenges such as orbit drift correction, non-water pixel detection, and homogenization had to be solved. The result is a dataset covering over 3.5 decades of spatial LSWT data for 26 European lakes. The validation against in-situ temperature data at 19 locations showed an uncertainty between ±0.8 K and ±2.0 K (standard deviation), depending on locations of the lakes. The long-term robustness of the dataset was confirmed by comparing in-situ and satellite derived temperature trends, which showed no significant difference. The final trend analysis showed significant LSWT warming trends at all locations (0.2 K/decade to 0.8 K/decade). A gradient of increasing trends from south-west to north-east of Europe was revealed. The strong intra-annual variability of trends indicates that single seasonal trends do not well represent the response of a lake to climate change, e.g., autumn trends are dominant in the north of Europe, whereas winter trends are dominant in the south. Intra-lake variability of trends indicates that trends at single in-situ stations do not necessarily represent the lake’s response. The LSWT dataset generated for this study gives some new and interesting insights into the response of European lakes to climate change during the last 36 years (1981–2016).
topic Advanced Very High Resolution Radiometer (AVHRR)
Lake Surface Water Temperature (LSWT)
long-term data record
temperature trend
intra-annual variability
url http://www.mdpi.com/2072-4292/10/7/990
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