Classification of ice and water in the Arctic using radar altimeter and microwave radiometer data from HY-2B satellite
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Abstract: Several Chinese marine satellites have been launched in recent years. Monitoring sea ice and the ocean in the Arctic is of great importance for climate research. Sea ice in the Arctic has changed rapidly during the past few decades with respect to the extent and thickness. In this study, we applied combined passive and active microwave data from the Chinese HaiYang-2B (HY-2B) satellite to classify ice and sea water in the Arctic. We use data from a radar altimeter (RA) and a calibration microwave radiometer (CMR) to discriminate between ice and water by applying several approaches (1) the single parameter threshold criteria, (2) the multi-parameters linear segmentations and (3) the K-means clustering. The results yielded by these methods were in good agreement (classification accuracy >95%) with the Satellite Application Facility on Ocean and Sea Ice products between November and April. For other months (May–October), however, the agreement was less good (lowest classification accuracy approximate 85% in summer). A hybrid approach combined with graphical ice edges detection and microwave radar waveform analysis is therefore developed. A visual comparison with SAR images suggested the hybrid approach results greatly improved the ice and water discrimination in summer. This study demonstrated that multi-sensors (RA and CMR) configurations from HY satellites can offer comparable polar earth observation to the European Space Agency and NOAA satellite products.
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Key words:
- Arctic /
- active microwave data /
- passive microwave data /
- sea ice /
- seawater
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Figure 1. A cartoon illustration of HY-2B’s radar altimeter (RA) and calibration microwave radiometers (CMR) antenna beams orientation. The color arrows represent the observation angle of CMR’s 18.7 GHz (yellow), 23.8 GHz (green), 37 GHz (red).
Figure 2. The HY-2B ground orbit trajectories (blue lines) in the northern hemisphere from November 1–15, 2019.
Figure 3. Daily brightness temperature time series histogram for the 2019 annual season.
Figure 5. Distributions of the different tracking packages flags on July 15, 2019: SMLE packages flags (a); OCOG packages flags (b).
Figure 6. Two-dimensional joint histograms of brightness temperature and backscatter coefficient for the whole year of 2019. For each month, there are 2 (14-day) cycles, marked as a and b, respectively.
Figure 8. Daily accuracy ratings of the threshold segmentation of the sea ice/seawater separation.
Figure 10. Daily accuracy ratings of the K-Means for the ice water/seawater separations.
Figure 11. The join map of the time series plot of ice extent change rates and sea ice extent and the correct classification result of seawater during 2019.
Figure 12. The join map of the time series plot of ice extent change rates and sea ice extent and the correct classification result of sea ice during 2019.
Figure 13. Comparison of the multi-parameter linear segmentation and the OSI-SAF products. The data were collected on July 15, 2019. In the figure, the areas that the OSI-SAF product marked as seawater and the multi-parameter linear segmentation marked as sea ice are shown in red. The areas that the OSI-SAF product marked as sea ice and the multi-parameter linear segmentation marked as seawater are shown in yellow.
Figure 14. Extraction of the ice edge areas on July 15, 2019: a. Sea ice concentrations of OSI-SAF; b. results of the sea ice edge extractions.
Figure 15. Comparison of the results of the different classification methods in the four different seasons.
Table 1. The average correct rates of the four different K-means classification
Sea ice Seawater K-means 96.89% 98.15% K-means (remove 18) 96.30% 97.71% K-means (remove 23) 97.00% 98.16% K-means (remove 37) 96.96% 98.19% 
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