Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Abstract

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

“…Comparing the SIT between 2012 and 2013, we can see that the SIT in February 2013 is larger than that in February 2012, as shown in Figure 6a-l,r-u. This finding is consistent with Yan's results [30]. Focusing on January and February 2013, SIT tends to decrease over time, and the findings confirm the statistical results: the sea ice in LDB began to melt gradually at the end of February and early March.…”

“…Therefore, the information of all bands is needed for ice thickness inversion. In Yan's paper [30], the information of all the bands is also used to carry out ice thickness inversion. Even with combinations of 3 bands, the retrieval results have slightly lower skill scores for JZ20-2 than for JZ9-3 because the in situ SIT falls within a range, and the variation range for JZ20-2 is larger than that for JZ9-3.…”

“…To address the problem of coarse spatiotemporal resolution, Liu [9] proposed a method to estimate SIT using recent Geostationary Ocean Color Imager (GOCI) data and found that GOCI data have the potential to yield extensive, accurate, and near-real-time (with a time resolution of 1 h) monitoring of sea ice changes. Recently, Yan [30] monitored the characteristics of Bohai Sea ice using GOCI data, and Landsat Thematic Mapper (TM) data with higher spatial resolution than the GOCI data were used to validate the data. However, few SIT retrieval models based on Rayleigh scattering of GOCI images exist.…”

Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions

“…For example, the first geostationary ocean color imager (GOCI, data available since 2011), operated by the Korea Institute of Ocean Science & Technology, has demonstrated its unique contributions in capturing the diurnal changes of red tides, turbidity plumes, water quality and ocean surface current, etc. [5][6][7][8][9][10][11][12], given its temporal resolution of eight hourly observations per day. Although they have coarse spectral and spatial resolutions, geostationary meteorological satellites (such as the Himawari-8 from the Japan Meteorological Agency and the Geostationary Operational Environmental Satellite (GOES) from NOAA) boast high observational frequencies (tens of minutes), allowing them to detect, for example, the short-term dynamics of floating algae [13][14][15].…”

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

“…Although the use of optical remote sensors is constrained by the weather conditions, they have the merits of finer spatial resolution, low cost, and a short revisit period (one day or less). Thus, optical remote sensors such as the Advanced Very High-Resolution Radiometer (AVHRR) [14], Moderate Resolution Imaging Spectroradiometer (MODIS) [15][16][17][18], Geostationary Ocean Color Imager (GOCI) [19][20][21], and FengYun-3 Medium Resolution Spectral Imager (MERSI) [22] have been effectively employed to extract sea ice distribution information via a variety of methods. For example, rapid and effective sea ice extraction has been achieved with a ratio-threshold segmentation method based on the red and infrared bands of MODIS images [2,23].…”