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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ftmuenster:oai:wwu.de:d36882de-9cc3-4fb9-9498-bb206e0bb83f 2024-09-15T17:40:29+00:00 A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data Lezama Valdes, L. (Lilian-Maite) Katurji, M. (Marwan) Meyer, H. (Hanna) Universitäts- und Landesbibliothek Münster 2023-02-13 application/pdf https://nbn-resolving.org/urn:nbn:de:hbz:6-61029596236 https://doi.org/10.17879/21039606144 https://miami.uni-muenster.de/Record/d36882de-9cc3-4fb9-9498-bb206e0bb83f https://repositorium.uni-muenster.de/transfer/miami/d36882de-9cc3-4fb9-9498-bb206e0bb83f eng eng info:eu-repo/semantics/reference/doi/10.3390/rs13224673 info:eu-repo/semantics/reference/url/https://github.com/MLezamaValdes/downscaleLST.MDV info:eu-repo/semantics/reference/doi/10.17605/OSF.IO/5MH6X https://nbn-resolving.org/urn:nbn:de:hbz:6-61029596236 urn:nbn:de:hbz:6-61029596236 doi:10.17879/21039606144 https://miami.uni-muenster.de/Record/d36882de-9cc3-4fb9-9498-bb206e0bb83f https://repositorium.uni-muenster.de/transfer/miami/d36882de-9cc3-4fb9-9498-bb206e0bb83f CC BY 4.0 info:eu-repo/semantics/openAccess downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning ddc:550 info:eu-repo/classification/ddc/550 Earth sciences article doc-type:article info:eu-repo/semantics/article Text info:eu-repo/semantics/publishedVersion publishedVersion 2023 ftmuenster https://doi.org/10.17879/2103960614410.3390/rs1322467310.17605/OSF.IO/5MH6X 2024-08-20T23:56:41Z 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 R2 of 0.78 and a RMSE of 3.32 ∘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. Article in Journal/Newspaper Antarc* Antarctic Antarctica McMurdo Dry Valleys Münster University (WWU): miami |
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Open Polar |
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Münster University (WWU): miami |
op_collection_id |
ftmuenster |
language |
English |
topic |
downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning ddc:550 info:eu-repo/classification/ddc/550 Earth sciences |
spellingShingle |
downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning ddc:550 info:eu-repo/classification/ddc/550 Earth sciences Lezama Valdes, L. (Lilian-Maite) Katurji, M. (Marwan) Meyer, H. (Hanna) A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data |
topic_facet |
downscaling Land Surface Temperature Antarctica McMurdo Dry Valleys MODIS machine learning ddc:550 info:eu-repo/classification/ddc/550 Earth sciences |
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 R2 of 0.78 and a RMSE of 3.32 ∘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. |
author2 |
Universitäts- und Landesbibliothek Münster |
format |
Article in Journal/Newspaper |
author |
Lezama Valdes, L. (Lilian-Maite) Katurji, M. (Marwan) Meyer, H. (Hanna) |
author_facet |
Lezama Valdes, L. (Lilian-Maite) Katurji, M. (Marwan) Meyer, H. (Hanna) |
author_sort |
Lezama Valdes, L. (Lilian-Maite) |
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 |
publishDate |
2023 |
url |
https://nbn-resolving.org/urn:nbn:de:hbz:6-61029596236 https://doi.org/10.17879/21039606144 https://miami.uni-muenster.de/Record/d36882de-9cc3-4fb9-9498-bb206e0bb83f https://repositorium.uni-muenster.de/transfer/miami/d36882de-9cc3-4fb9-9498-bb206e0bb83f |
genre |
Antarc* Antarctic Antarctica McMurdo Dry Valleys |
genre_facet |
Antarc* Antarctic Antarctica McMurdo Dry Valleys |
op_relation |
info:eu-repo/semantics/reference/doi/10.3390/rs13224673 info:eu-repo/semantics/reference/url/https://github.com/MLezamaValdes/downscaleLST.MDV info:eu-repo/semantics/reference/doi/10.17605/OSF.IO/5MH6X https://nbn-resolving.org/urn:nbn:de:hbz:6-61029596236 urn:nbn:de:hbz:6-61029596236 doi:10.17879/21039606144 https://miami.uni-muenster.de/Record/d36882de-9cc3-4fb9-9498-bb206e0bb83f https://repositorium.uni-muenster.de/transfer/miami/d36882de-9cc3-4fb9-9498-bb206e0bb83f |
op_rights |
CC BY 4.0 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.17879/2103960614410.3390/rs1322467310.17605/OSF.IO/5MH6X |
_version_ |
1810486535907180544 |