A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed....

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Autores principales:	María Pedreros-Guarda, Rodrigo Abarca-del-Río, Karen Escalona, Ignacio García, Óscar Parra
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	water surface temperature Google Earth Engine lakes remote sensing Landsat Science Q
Acceso en línea:	https://doaj.org/article/bcac689f1dcb4611be4b2c86cd292f55
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spelling	oai:doaj.org-article:bcac689f1dcb4611be4b2c86cd292f552021-11-25T18:54:07ZA Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile10.3390/rs132245442072-4292https://doaj.org/article/bcac689f1dcb4611be4b2c86cd292f552021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4544https://doaj.org/toc/2072-4292Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C) and temporal (7–25 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.María Pedreros-GuardaRodrigo Abarca-del-RíoKaren EscalonaIgnacio GarcíaÓscar ParraMDPI AGarticlewater surface temperatureGoogle Earth Enginelakesremote sensingLandsatScienceQENRemote Sensing, Vol 13, Iss 4544, p 4544 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	water surface temperature Google Earth Engine lakes remote sensing Landsat Science Q
spellingShingle	water surface temperature Google Earth Engine lakes remote sensing Landsat Science Q María Pedreros-Guarda Rodrigo Abarca-del-Río Karen Escalona Ignacio García Óscar Parra A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
description	Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C) and temporal (7–25 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.
format	article
author	María Pedreros-Guarda Rodrigo Abarca-del-Río Karen Escalona Ignacio García Óscar Parra
author_facet	María Pedreros-Guarda Rodrigo Abarca-del-Río Karen Escalona Ignacio García Óscar Parra
author_sort	María Pedreros-Guarda
title	A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
title_short	A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
title_full	A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
title_fullStr	A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
title_full_unstemmed	A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile
title_sort	google earth engine application to retrieve long-term surface temperature for small lakes. case: san pedro lagoons, chile
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bcac689f1dcb4611be4b2c86cd292f55
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A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

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