气候变化领域集成服务门户(CCPortal): Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

CCPortal

DOI	10.5194/tc-15-2835-2021
	Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
	Yin Z.; Li X.; Ge Y.; Shang C.; Li X.; Du Y.; Ling F.
发表日期	2021
ISSN	19940416
起始页码	2835
结束页码	2856
卷号	15 期号:6
英文摘要	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.
语种	英语
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202454
作者单位	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
推荐引用方式 GB/T 7714	Yin Z.,Li X.,Ge Y.,et al. Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning[J],2021,15(6).
APA	Yin Z..,Li X..,Ge Y..,Shang C..,Li X..,...&Ling F..(2021).Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning.Cryosphere,15(6).
MLA	Yin Z.,et al."Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning".Cryosphere 15.6(2021).