A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predic...

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Autores principales:	Lilian-Maite Lezama Valdes, Marwan Katurji, Hanna Meyer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning Science Q
Acceso en línea:	https://doaj.org/article/9598b374fd564188aa71ef7766ce2044
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spelling	oai:doaj.org-article:9598b374fd564188aa71ef7766ce20442021-11-25T18:55:17ZA Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data10.3390/rs132246732072-4292https://doaj.org/article/9598b374fd564188aa71ef7766ce20442021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4673https://doaj.org/toc/2072-4292To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R<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 0.78 and a RMSE of 3.32 <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. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution.Lilian-Maite Lezama ValdesMarwan KaturjiHanna MeyerMDPI AGarticledownscalingLand Surface TemperatureAntarcticaMcMurdo Dry ValleysMODISmachine learningScienceQENRemote Sensing, Vol 13, Iss 4673, p 4673 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning Science Q
spellingShingle	downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning Science Q Lilian-Maite Lezama Valdes Marwan Katurji Hanna Meyer A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
description	To monitor environmental and biological processes, Land Surface Temperature (LST) is a central variable, which is highly variable in space and time. This particularly applies to the Antarctic Dry Valleys, which host an ecosystem highly adapted to the extreme conditions in this cold desert. To predict possible climate induced changes on the Dry Valley ecosystem, high spatial and temporal resolution environmental variables are needed. Thus we enhanced the spatial resolution of the MODIS satellite LST product that is sensed sub-daily at a 1 km spatial resolution to a 30 m spatial resolution. We employed machine learning models that are trained using Landsat 8 thermal infrared data from 2013 to 2019 as a reference to predict LST at 30 m resolution. For the downscaling procedure, terrain derived variables and information on the soil type as well as the solar insolation were used as potential predictors in addition to MODIS LST. The trained model can be applied to all available MODIS scenes from 1999 onward to develop a 30 m resolution LST product of the Antarctic Dry Valleys. A spatio-temporal validation revealed an R<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 0.78 and a RMSE of 3.32 <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. The downscaled LST will provide a valuable surface climate data set for various research applications, such as species distribution modeling, climate model evaluation, and the basis for the development of further relevant environmental information such as the surface moisture distribution.
format	article
author	Lilian-Maite Lezama Valdes Marwan Katurji Hanna Meyer
author_facet	Lilian-Maite Lezama Valdes Marwan Katurji Hanna Meyer
author_sort	Lilian-Maite Lezama Valdes
title	A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
title_short	A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
title_full	A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
title_fullStr	A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
title_full_unstemmed	A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data
title_sort	machine learning based downscaling approach to produce high spatio-temporal resolution land surface temperature of the antarctic dry valleys from modis data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9598b374fd564188aa71ef7766ce2044
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A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data

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