Articles | Volume 15, issue 6
The Cryosphere, 15, 2835–2856, 2021
The Cryosphere, 15, 2835–2856, 2021

Research article 24 Jun 2021

Research article | 24 Jun 2021

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

Zhixiang Yin et al.

Related subject area

Discipline: Sea ice | Subject: Remote Sensing
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Cited articles

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Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically Significant Effects of Some Approximations in the Bulk Parameterizations of Turbulent Air–Sea Fluxes, J. Phys. Oceanogr., 47, 5–28,, 2017. 
Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.