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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Online Access: | https://hdl.handle.net/10092/103291 https://doi.org/10.3390/rs13224673 |
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ftunivcanter:oai:ir.canterbury.ac.nz:10092/103291 2023-05-15T13:55:49+02:00 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-M Meyer H Katurji, Marwan 2021-11-22T07:50:29Z application/pdf https://hdl.handle.net/10092/103291 https://doi.org/10.3390/rs13224673 en eng MDPI AG Lezama Valdes L-M, Katurji M, Meyer H A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sensing. 13(22). 4673-4673. 2072-4292 https://hdl.handle.net/10092/103291 http://doi.org/10.3390/rs13224673 All rights reserved unless otherwise stated http://hdl.handle.net/10092/17651 downscaling land surface temperature Antarctica McMurdo dry valleys MODIS machine learning Fields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370108 - Meteorology Fields of Research::37 - Earth sciences::3702 - Climate change science::370201 - Climate change processes Fields of Research::37 - Earth sciences::3709 - Physical geography and environmental geoscience Journal Article 2021 ftunivcanter https://doi.org/10.3390/rs13224673 2022-09-08T13:30:44Z 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 University of Canterbury, Christchurch: UC Research Repository Antarctic McMurdo Dry Valleys The Antarctic Remote Sensing 13 22 4673 |
institution |
Open Polar |
collection |
University of Canterbury, Christchurch: UC Research Repository |
op_collection_id |
ftunivcanter |
language |
English |
topic |
downscaling land surface temperature Antarctica McMurdo dry valleys MODIS machine learning Fields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370108 - Meteorology Fields of Research::37 - Earth sciences::3702 - Climate change science::370201 - Climate change processes Fields of Research::37 - Earth sciences::3709 - Physical geography and environmental geoscience |
spellingShingle |
downscaling land surface temperature Antarctica McMurdo dry valleys MODIS machine learning Fields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370108 - Meteorology Fields of Research::37 - Earth sciences::3702 - Climate change science::370201 - Climate change processes Fields of Research::37 - Earth sciences::3709 - Physical geography and environmental geoscience Lezama Valdes L-M Meyer H Katurji, Marwan 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 Fields of Research::37 - Earth sciences::3701 - Atmospheric sciences::370108 - Meteorology Fields of Research::37 - Earth sciences::3702 - Climate change science::370201 - Climate change processes Fields of Research::37 - Earth sciences::3709 - Physical geography and environmental geoscience |
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. |
format |
Article in Journal/Newspaper |
author |
Lezama Valdes L-M Meyer H Katurji, Marwan |
author_facet |
Lezama Valdes L-M Meyer H Katurji, Marwan |
author_sort |
Lezama Valdes L-M |
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://hdl.handle.net/10092/103291 https://doi.org/10.3390/rs13224673 |
geographic |
Antarctic McMurdo Dry Valleys The Antarctic |
geographic_facet |
Antarctic McMurdo Dry Valleys The Antarctic |
genre |
Antarc* Antarctic Antarctica McMurdo Dry Valleys |
genre_facet |
Antarc* Antarctic Antarctica McMurdo Dry Valleys |
op_relation |
Lezama Valdes L-M, Katurji M, Meyer H A Machine Learning Based Downscaling Approach to Produce High Spatio-Temporal Resolution Land Surface Temperature of the Antarctic Dry Valleys from MODIS Data. Remote Sensing. 13(22). 4673-4673. 2072-4292 https://hdl.handle.net/10092/103291 http://doi.org/10.3390/rs13224673 |
op_rights |
All rights reserved unless otherwise stated http://hdl.handle.net/10092/17651 |
op_doi |
https://doi.org/10.3390/rs13224673 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
22 |
container_start_page |
4673 |
_version_ |
1766262687981895680 |