Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches
High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods p...
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ftmdpi:oai:mdpi.com:/2072-4292/14/22/5775/ 2023-08-20T04:06:53+02:00 Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches Jiahang Che Minghu Ding Qinglin Zhang Yetang Wang Weijun Sun Yuzhe Wang Lei Wang Baojuan Huai agris 2022-11-16 application/pdf https://doi.org/10.3390/rs14225775 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs14225775 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 22; Pages: 5775 temperature MODIS machine learning methods GrIS Text 2022 ftmdpi https://doi.org/10.3390/rs14225775 2023-08-01T07:21:43Z High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects. Text Greenland Ice Sheet MDPI Open Access Publishing Greenland Remote Sensing 14 22 5775 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
temperature MODIS machine learning methods GrIS |
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temperature MODIS machine learning methods GrIS Jiahang Che Minghu Ding Qinglin Zhang Yetang Wang Weijun Sun Yuzhe Wang Lei Wang Baojuan Huai Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
topic_facet |
temperature MODIS machine learning methods GrIS |
description |
High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects. |
format |
Text |
author |
Jiahang Che Minghu Ding Qinglin Zhang Yetang Wang Weijun Sun Yuzhe Wang Lei Wang Baojuan Huai |
author_facet |
Jiahang Che Minghu Ding Qinglin Zhang Yetang Wang Weijun Sun Yuzhe Wang Lei Wang Baojuan Huai |
author_sort |
Jiahang Che |
title |
Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
title_short |
Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
title_full |
Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
title_fullStr |
Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
title_full_unstemmed |
Reconstruction of Near-Surface Air Temperature over the Greenland Ice Sheet Based on MODIS Data and Machine Learning Approaches |
title_sort |
reconstruction of near-surface air temperature over the greenland ice sheet based on modis data and machine learning approaches |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14225775 |
op_coverage |
agris |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Remote Sensing; Volume 14; Issue 22; Pages: 5775 |
op_relation |
https://dx.doi.org/10.3390/rs14225775 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14225775 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
22 |
container_start_page |
5775 |
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1774718244395941888 |