Sea ice detection and monitoring using GOCI-II

Abstract

Sea ice affects the weather through surface energy exchange and economic activity such as fishery, transportation and oil exploration in the Bohai bay. Moreover, drifting sea ice can cause damage to port facilities and ships. Therefore, many studies of sea ice detection have been conducted using synthetic aperture radar (SAR) and ocean color sensor, such as advanced very high-resolution radiometers (AVHRR) and moderate resolution imaging spectroradiometer (MODIS). Recently, more accurate and detailed sea ice detection is possible using Sentinel-2 and Landsat-8 in the Bohai bay. Although these satellites observed wide area with high spatial resolution, there are limitation to sea ice monitoring due to the its revisit period. In this study, we developed sea ice detection algorithms (threshold algorithm, deep learning algorithm) in Bohai bay using Rayleigh-corrected reflectance (Rrc) data of Geostationary Ocean Color Imager-II (GOCI-II). We proposed a threshold-based algorithm to sea ice detection using Rrc at 680 and 865 nm bands for GOCI-II. Threshold of the ratio of Rrc at two bands and standard deviation method allows the classification of sea ice, clouds and sea water. Additionally, we developed MultiLayer Perceptron (MLP) algorithm using Rrc at 12 bands for GOCI-II. Training data set used the threshold-based algorithm classification data. The algorithm results were validated against detected sea ice using Sentinel-2 data. The results showed that the both algorithms using GOCI-II is suitable for sea ice detection. Furthermore, GOCI-II, which has good temporal resolution, can be used sea ice monitoring for short-term variability and the movement of drifting sea ice by tidal current.