Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach
Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes i...
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description | Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000–2018 across all the investigated lakes.
Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement.
All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.
[Display omitted]
•Landsat-retrieved LSWTs inverted using simplified Planck's law showed good accuracy.•Landsat and MODIS retrieved LSWT showed an accuracy better than +2.5 °C.•Landsat LSWT dataset was used to validate rescaled MODIS LSWTs.•Sardinian lakes are warming with an annual trend of +0.010 °C/yr during 2000–2018.•The LSWT trends can be explained by the Italian climatology for the same period. |
doi_str_mv | 10.1016/j.scitotenv.2019.135567 |
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Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement.
All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.
[Display omitted]
•Landsat-retrieved LSWTs inverted using simplified Planck's law showed good accuracy.•Landsat and MODIS retrieved LSWT showed an accuracy better than +2.5 °C.•Landsat LSWT dataset was used to validate rescaled MODIS LSWTs.•Sardinian lakes are warming with an annual trend of +0.010 °C/yr during 2000–2018.•The LSWT trends can be explained by the Italian climatology for the same period.</description><identifier>ISSN: 0048-9697</identifier><identifier>EISSN: 1879-1026</identifier><identifier>DOI: 10.1016/j.scitotenv.2019.135567</identifier><identifier>PMID: 31780156</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Climate change ; In situ long-term dataset ; LSWT ; Mann-Kendall test ; Mediterranean Sea ; Nash-Sutcliffe efficiency ; Sen's slope ; Time series</subject><ispartof>The Science of the total environment, 2020-03, Vol.707, p.135567-135567, Article 135567</ispartof><rights>2018 Elsevier B.V.</rights><rights>Copyright © 2018 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-4b05716adc1cd7c43ddc69ef54f16c3a5f50898b0befbdc87072e5989d265b103</citedby><cites>FETCH-LOGICAL-c371t-4b05716adc1cd7c43ddc69ef54f16c3a5f50898b0befbdc87072e5989d265b103</cites><orcidid>0000-0001-9199-0094 ; 0000-0001-6382-4208 ; 0000-0003-3927-9494</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.scitotenv.2019.135567$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31780156$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Virdis, Salvatore G.P.</creatorcontrib><creatorcontrib>Soodcharoen, Nooch</creatorcontrib><creatorcontrib>Lugliè, Antonella</creatorcontrib><creatorcontrib>Padedda, Bachisio M.</creatorcontrib><title>Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach</title><title>The Science of the total environment</title><addtitle>Sci Total Environ</addtitle><description>Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000–2018 across all the investigated lakes.
Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement.
All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.
[Display omitted]
•Landsat-retrieved LSWTs inverted using simplified Planck's law showed good accuracy.•Landsat and MODIS retrieved LSWT showed an accuracy better than +2.5 °C.•Landsat LSWT dataset was used to validate rescaled MODIS LSWTs.•Sardinian lakes are warming with an annual trend of +0.010 °C/yr during 2000–2018.•The LSWT trends can be explained by the Italian climatology for the same period.</description><subject>Climate change</subject><subject>In situ long-term dataset</subject><subject>LSWT</subject><subject>Mann-Kendall test</subject><subject>Mediterranean Sea</subject><subject>Nash-Sutcliffe efficiency</subject><subject>Sen's slope</subject><subject>Time series</subject><issn>0048-9697</issn><issn>1879-1026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFUU2P0zAQtRCILQt_AebIgRQ7aWKH22q1wEqLuMDZcuxJ1yWxi-0U7W_kTzGlZa_4Ynn05n34MfZG8LXgonu_W2frSywYDuuai34tmrbt5BO2Ekr2leB195StON-oqu96ecFe5LzjdKQSz9lFI6Tiou1W7PdNLn42xccAcYRsCk6TL1g5TP6ADibzA-EXjRPkJY3GIhSc95hMWRJm8AHKPSEwEyTAF3S0nZIJaMIHuA0FtwT1YQvzMhVf5bgki-_OL2KI0_JXnVxsMT2ACQ4MTDFsKyKaYfQ4OXCmmIwFlnykMkCmETJ5JAtmv0_R2PuX7Nlopoyvzvcl-_7x5tv15-ru66fb66u7yjZSlGoz8FaKzjgrrJN20zhnux7HdjOKzjamHVuuejXwAcfBWSW5rLHtVe_qrh0Eby7Z2xMvyf5cKLiefbb0bxQ6LlnXTc0bVatNTVB5gtoUc0446n2ioOlBC66PTeqdfmxSH5vUpyZp8_VZZBlmdI97_6ojwNUJgBT14DEdiTBYKiChLdpF_1-RP1-Iupg</recordid><startdate>20200310</startdate><enddate>20200310</enddate><creator>Virdis, Salvatore G.P.</creator><creator>Soodcharoen, Nooch</creator><creator>Lugliè, Antonella</creator><creator>Padedda, Bachisio M.</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9199-0094</orcidid><orcidid>https://orcid.org/0000-0001-6382-4208</orcidid><orcidid>https://orcid.org/0000-0003-3927-9494</orcidid></search><sort><creationdate>20200310</creationdate><title>Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach</title><author>Virdis, Salvatore G.P. ; Soodcharoen, Nooch ; Lugliè, Antonella ; Padedda, Bachisio M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-4b05716adc1cd7c43ddc69ef54f16c3a5f50898b0befbdc87072e5989d265b103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Climate change</topic><topic>In situ long-term dataset</topic><topic>LSWT</topic><topic>Mann-Kendall test</topic><topic>Mediterranean Sea</topic><topic>Nash-Sutcliffe efficiency</topic><topic>Sen's slope</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Virdis, Salvatore G.P.</creatorcontrib><creatorcontrib>Soodcharoen, Nooch</creatorcontrib><creatorcontrib>Lugliè, Antonella</creatorcontrib><creatorcontrib>Padedda, Bachisio M.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The Science of the total environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Virdis, Salvatore G.P.</au><au>Soodcharoen, Nooch</au><au>Lugliè, Antonella</au><au>Padedda, Bachisio M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach</atitle><jtitle>The Science of the total environment</jtitle><addtitle>Sci Total Environ</addtitle><date>2020-03-10</date><risdate>2020</risdate><volume>707</volume><spage>135567</spage><epage>135567</epage><pages>135567-135567</pages><artnum>135567</artnum><issn>0048-9697</issn><eissn>1879-1026</eissn><abstract>Lake surface water temperature (LSWT) is a key parameter to help study the environmental and ecological impacts of climate change. In this work, we measured the LSWT of 1 natural and 23 artificial lakes located on the island of Sardinia in the western Mediterranean, which is a region where changes in climate are projected to have significant impacts. By integrating multi-source and multi-resolution datasets of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat and long-term in situ temperature observations, we detected, measured, and analysed the LSWT trends during the period of 2000–2018 across all the investigated lakes.
Methodologically, we demonstrated that a simplified approached based on Planck's equation for Landsat thermal infrared (TIR) data could be a valid alternative to radiative transfer equation retrieval methods for the retrieval of LSWT without loss of accuracy. Moreover, we demonstrated that rescaled and independently validated MOD112A-derived LSWT showed good accuracy, efficiently filled the spatial and temporal gaps in long-term in situ LSWT, and could be used for long-term LSWT trend detection and measurement.
All 24 lakes showed an annual warming trend of +0.010 °C/y, warming winter trend of +0.013 °C/y, and cooling summer trend of −0.038 °C/y during the period of 2000–2018. This study demonstrated that the measured trend rates could be explained by and were strongly correlated with the climatology of Italy for the 2000–2018 period. Finally, we demonstrated the key role and the importance of the availability of long-term in situ temperature datasets. The approach used in this study is up-scalable to other medium to low-resolution TIR sensors as well as to other long-term monitoring sites, such as LTER-Italy, LTER-Europe, or ILTER sites.
[Display omitted]
•Landsat-retrieved LSWTs inverted using simplified Planck's law showed good accuracy.•Landsat and MODIS retrieved LSWT showed an accuracy better than +2.5 °C.•Landsat LSWT dataset was used to validate rescaled MODIS LSWTs.•Sardinian lakes are warming with an annual trend of +0.010 °C/yr during 2000–2018.•The LSWT trends can be explained by the Italian climatology for the same period.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>31780156</pmid><doi>10.1016/j.scitotenv.2019.135567</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9199-0094</orcidid><orcidid>https://orcid.org/0000-0001-6382-4208</orcidid><orcidid>https://orcid.org/0000-0003-3927-9494</orcidid></addata></record> |
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subjects | Climate change In situ long-term dataset LSWT Mann-Kendall test Mediterranean Sea Nash-Sutcliffe efficiency Sen's slope Time series |
title | Estimation of satellite-derived lake water surface temperatures in the western Mediterranean: Integrating multi-source, multi-resolution imagery and a long-term field dataset using a time series approach |
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