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Ocean Observation: From Microfluidics to Global Scale
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Forecasting the sea ice drift in the Liaodong Bay using GOCI data
P-OB-09-S
Gong Wenping, Zhang Rui* , Center for Coastal Ocean Science & Technology Research, School of Marine Sciences, SunYat-sen University, Guangzhou 510275, China
Presenter Email: 815382241@qq.com
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The monitoring and forecasting of sea ice drift has profound implications for fisheries, port trade, transportation, oil platforms and economic growth. In this paper, a novel method was developed with an aim to monitoring the sea ice thickness in the Liaodong Bay, based on Rayleigh-corrected reflectance of four GOCI (Geostationary Ocean Color Imager) images in the Feb. of 2012. The result indicates the sea ice thickness by remote sensing retrieval can be well consistent with empirical interpretations. We analyzed the relationship among the surface temperature, rainfall, wind, thickness and sea ice drift. A multiple regression model was obtained to predict the drift distance of sea ice, with the correlation coefficient R up to 0.79, which shows the model can effectively predict the sea ice drift.
Key words: forecasting; Sea ice; drift; Liaodong Bay; GOCI; remote sensing retrieval
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