Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
The turbulent heat flux (THF) over leads is an important parameter for climate change monitoring in the Arctic region. THF over leads is often calculated from satellite-derived ice surface temperature (IST) products, in which mixed pixels containing both ice and open water along lead boundaries redu...
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ftdoajarticles:oai:doaj.org/article:41d6d1a3618c4211838d9f6aaa21dc12 2023-05-15T15:06:21+02:00 Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning Z. Yin X. Li Y. Ge C. Shang Y. Du F. Ling 2021-06-01T00:00:00Z https://doi.org/10.5194/tc-15-2835-2021 https://doaj.org/article/41d6d1a3618c4211838d9f6aaa21dc12 EN eng Copernicus Publications https://tc.copernicus.org/articles/15/2835/2021/tc-15-2835-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-2835-2021 1994-0416 1994-0424 https://doaj.org/article/41d6d1a3618c4211838d9f6aaa21dc12 The Cryosphere, Vol 15, Pp 2835-2856 (2021) Environmental sciences GE1-350 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.5194/tc-15-2835-2021 2022-12-31T08:56:42Z The turbulent heat flux (THF) over leads is an important parameter for climate change monitoring in the Arctic region. THF over leads is often calculated from satellite-derived ice surface temperature (IST) products, in which mixed pixels containing both ice and open water along lead boundaries reduce the accuracy of calculated THF. To address this problem, this paper proposes a deep residual convolutional neural network (CNN)-based framework to estimate THF over leads at the subpixel scale (DeepSTHF) based on remotely sensed images. The proposed DeepSTHF provides an IST image and the corresponding lead map with a finer spatial resolution than the input IST image so that the subpixel-scale THF can be estimated from them. The proposed approach is verified using simulated and real Moderate Resolution Imaging Spectroradiometer images and compared with the conventional cubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine-spatial-resolution IST images and lead maps, thereby providing more accurate and reliable THF over leads. Article in Journal/Newspaper Arctic Climate change The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic The Cryosphere 15 6 2835 2856 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 Z. Yin X. Li Y. Ge C. Shang Y. Du F. Ling Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
The turbulent heat flux (THF) over leads is an important parameter for climate change monitoring in the Arctic region. THF over leads is often calculated from satellite-derived ice surface temperature (IST) products, in which mixed pixels containing both ice and open water along lead boundaries reduce the accuracy of calculated THF. To address this problem, this paper proposes a deep residual convolutional neural network (CNN)-based framework to estimate THF over leads at the subpixel scale (DeepSTHF) based on remotely sensed images. The proposed DeepSTHF provides an IST image and the corresponding lead map with a finer spatial resolution than the input IST image so that the subpixel-scale THF can be estimated from them. The proposed approach is verified using simulated and real Moderate Resolution Imaging Spectroradiometer images and compared with the conventional cubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine-spatial-resolution IST images and lead maps, thereby providing more accurate and reliable THF over leads. |
format |
Article in Journal/Newspaper |
author |
Z. Yin X. Li Y. Ge C. Shang Y. Du F. Ling |
author_facet |
Z. Yin X. Li Y. Ge C. Shang Y. Du F. Ling |
author_sort |
Z. Yin |
title |
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
title_short |
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
title_full |
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
title_fullStr |
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
title_full_unstemmed |
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning |
title_sort |
estimating subpixel turbulent heat flux over leads from modis thermal infrared imagery with deep learning |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-2835-2021 https://doaj.org/article/41d6d1a3618c4211838d9f6aaa21dc12 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change The Cryosphere |
genre_facet |
Arctic Climate change The Cryosphere |
op_source |
The Cryosphere, Vol 15, Pp 2835-2856 (2021) |
op_relation |
https://tc.copernicus.org/articles/15/2835/2021/tc-15-2835-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-2835-2021 1994-0416 1994-0424 https://doaj.org/article/41d6d1a3618c4211838d9f6aaa21dc12 |
op_doi |
https://doi.org/10.5194/tc-15-2835-2021 |
container_title |
The Cryosphere |
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15 |
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6 |
container_start_page |
2835 |
op_container_end_page |
2856 |
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1766337981906419712 |