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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ftdoajarticles:oai:doaj.org/article:999b5e46e71c495c873ed6e62ad6faaf 2023-05-15T16:28:08+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 2022-11-01T00:00:00Z https://doi.org/10.3390/rs14225775 https://doaj.org/article/999b5e46e71c495c873ed6e62ad6faaf EN eng MDPI AG https://www.mdpi.com/2072-4292/14/22/5775 https://doaj.org/toc/2072-4292 doi:10.3390/rs14225775 2072-4292 https://doaj.org/article/999b5e46e71c495c873ed6e62ad6faaf Remote Sensing, Vol 14, Iss 5775, p 5775 (2022) temperature MODIS machine learning methods GrIS Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14225775 2022-12-30T19:41:14Z 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. Article in Journal/Newspaper Greenland Ice Sheet Directory of Open Access Journals: DOAJ Articles Greenland Remote Sensing 14 22 5775 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
temperature MODIS machine learning methods GrIS Science Q |
spellingShingle |
temperature MODIS machine learning methods GrIS Science Q 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 Science Q |
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 |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14225775 https://doaj.org/article/999b5e46e71c495c873ed6e62ad6faaf |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Remote Sensing, Vol 14, Iss 5775, p 5775 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/22/5775 https://doaj.org/toc/2072-4292 doi:10.3390/rs14225775 2072-4292 https://doaj.org/article/999b5e46e71c495c873ed6e62ad6faaf |
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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1766017762080063488 |