globalchange  > 气候变化与战略
DOI: 10.5194/tc-15-2835-2021
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
Author: Yin Z.; Li X.; Ge Y.; Shang C.; Li X.; Du Y.; Ling F.
Source Publication: Cryosphere
ISSN: 19940416
Publishing Year: 2021
Volume: 15, Issue:6
pages begin: 2835
pages end: 2856
Language: 英语
English Abstract: 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. © Author(s) 2021.
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Document Type: 期刊论文
Appears in Collections:气候变化与战略

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Affiliation: Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei, 230601, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

Recommended Citation:
Yin Z.,Li X.,Ge Y.,et al. Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning[J]. Cryosphere,2021-01-01,15(6)
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