Preprints
https://doi.org/10.5194/tc-2020-363
https://doi.org/10.5194/tc-2020-363

  11 Jan 2021

11 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal TC.

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Zhixiang Yin1,2,3, Xiaodong Li1, Yong Ge4, Cheng Shang1,2, Xinyan Li1,2, Yun Du1, and Feng Ling1 Zhixiang Yin et al.
  • 1Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430077, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei, 230601, China
  • 4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract. Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic 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 SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

Zhixiang Yin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2020-363', Anonymous Referee #1, 07 Feb 2021
  • RC2: 'Comment on tc-2020-363', Anonymous Referee #2, 10 Mar 2021

Zhixiang Yin et al.

Zhixiang Yin et al.

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