Sea Ice Extent Detection in the Bohai Sea Using Sentinel-3 OLCI Data

Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.

Development of Geo-KOMPSAT-2A Algorithm for Sea-Ice Detection Using Himawari-8/AHI Data

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.

Editorial on Special Issue “Applications of Remote Sensing in Coastal Areas”

Coastal areas are remarkable regions with high spatiotemporal variability [...]