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Remote Sens. 2017, 9(3), 234; doi:10.3390/rs9030234

Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data

1
Finnish Meteorological Institute (FMI), PB 503, FI-00101 Helsinki, Finland
2
National Satellite Ocean Application Service (NSOAS), Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100045, China
*
Author to whom correspondence should be addressed.
Academic Editors: Prashant K. Srivastava, Xiaofeng Li and Prasad S. Thenkabail
Received: 21 October 2016 / Accepted: 23 February 2017 / Published: 3 March 2017

Abstract

We estimate two essential sea ice parameters—namely, sea ice concentration (SIC) and sea ice thickness (SIT)—for the Bohai Sea using a combination of a thermodynamic sea ice model and Earth observation (EO) data from synthetic aperture radar (SAR) and microwave radiometer. We compare the SIC and SIT estimation results with in-situ measurements conducted in the study area and estimates based on independent EO data from near-infrared/optical instruments. These comparisons suggest that the SAR-based discrimination between sea ice and open-water works well, and areas of thinner and thicker ice can be distinguished. A larger comprehensive training dataset is needed to set up an operational algorithm for the estimation of SIC and SIT. View Full-Text
Keywords: synthetic aperture radar; passive microwave remote sensing; ice concentration; ice thickness; thermodynamic ice model synthetic aperture radar; passive microwave remote sensing; ice concentration; ice thickness; thermodynamic ice model
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Karvonen, J.; Shi, L.; Cheng, B.; Similä, M.; Mäkynen, M.; Vihma, T. Bohai Sea Ice Parameter Estimation Based on Thermodynamic Ice Model and Earth Observation Data. Remote Sens. 2017, 9, 234.

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