Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network
1
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY 12222, USA
2
College of Global Change and Earth System Science, and State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
3
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China
4
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
5
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
6
University Corporation for Polar Research, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2864; https://doi.org/10.3390/rs11232864
Received: 22 October 2019 / Revised: 28 November 2019 / Accepted: 29 November 2019 / Published: 2 December 2019
The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.
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Keywords:
snow depth; Arctic sea ice; deep neural network
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MDPI and ACS Style
Liu, J.; Zhang, Y.; Cheng, X.; Hu, Y. Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network. Remote Sens. 2019, 11, 2864.
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