Abstract
Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter.
Highlights
The Bohai Sea is a semi-enclosed sea in change in the Bohai Sea (China) and is the southernmost area of the frozen sea in the Northern Hemisphere
According to the distribution of the box plot, turbid seawater is most likely to interfere with sea ice detection, as it might not be easy to separate from sea ice in NDSIIIOLCI
We have developed the Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI) by adding consideration of Band 12 (750–757.5 nm) and Band 16 (771.25–786.25 nm) to the NDSIIIOLCI
Summary
The identification of sea ice and the accuracy of image interpretation have been improved by processing, respectively, optical and microwave images by hue–intensity–saturation (HIS) adjustment and wavelet transformation and further fusing these through principal component analysis (PCA) [5] Different classifiers such as a decision tree and a support vector machine have been used to directly distinguish sea ice on the basis of multispectral remote-sensing imagery [25,26], in some cases combining multiple features like image texture and surface temperature to improve the accuracy of sea ice extent estimation [27,28]. Data are available from a new-generation sensor called the Ocean and Land Color Instrument (OLCI), which is carried on the Sentinel-3 satellite This sensor has relatively high spectral resolution and spatiotemporal resolution in the visible and near-infrared spectra and is well suited to the requirements of large-scale coastal environmental monitoring. Sea ice information indexes based on OLCI multispectral imagery are developed to detect the extent of sea ice and employed to monitor the spatial and temporal variation of sea ice in the Bohai Sea in the winter of 2017–2018
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