CryoSat-2 radar altimeter sea ice waveform preferred feature classification

doi:10.13474/j.cnki.11-2246.2023.0155

Abstract

Abstract: Monitoring the type change or thickness change of sea ice is a more effective way to monitor sea ice. In this paper, the CryoSat-2 radar altimeter is used to study the types of Arctic sea ice. Using radar altimeter to classify Arctic sea ice, on the one hand, it is difficult to select the optimal characteristic parameters, on the other hand, it is difficult to achieve a relatively refined classification of sea ice with a single radar altimeter data. In view of the above problems, this paper constructed a feature selection method using the combination of chi-square test, mutual information and Wrapper packing method. The feature subset (SSD+Sigma0+LTPP+PP+SK+LEW) selected by this method is combined Random forest classification divides the radar altimeter data in the Arctic into seawater, one-year thin ice, one-year thick ice, and multi-year ice. The correct classification rate of this method is 93.32% in the training set, 92.42% in the validation set, and the Kappa coefficient is 0.90, all of which are better than other feature combinations, which can basically achieve effective classification of sea ice in the Arctic region, and the classification results can also help in the inversion of sea ice thickness.

Key words: waveform classification, feature selection, radar altimeter, Arctic sea ice, CryoSat-2

CLC Number:

P229

WU Bin, WANG Zhiyong, LI Xing, TIAN Kang. CryoSat-2 radar altimeter sea ice waveform preferred feature classification[J]. Bulletin of Surveying and Mapping, 2023, 0(5): 164-169.

References

[1] KWOK R, ROTHROCK D A. Decline in Arctic Sea ice thickness from submarine and ICESat records:1958-2008[J]. Geophysical Research Letters, 2009, 36(15):1-5.
[2] STEELE M, ERMOLD W, ZHANG Jinlun. Arctic ocean surface warming trends over the past 100 years[J]. Geophysical Research Letters, 2008, 35(2):L02614.
[3] 肖峰, 张胜凯, 李佳星, 等. 基于CryoSat-2卫星测高数据的北极海冰厚度变化研究[J]. 中国科学:地球科学, 2021, 51(7):1059-1069.
[4] ZHANG Lu, LIU Huiying, GU Xinwei, et al. Sea ice classification using TerraSAR-X ScanSAR data with removal of scalloping and interscan banding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(2):589-598.
[5] MOHAMMED D, BENOIT M, STEPHEN H. Assessment of the high resolution SAR mode of the RADARSAT constellation mission for first year ice and multiyear ice characterization[J]. Remote Sensing, 2018, 10(4):594.
[6] 王志勇, 王丽华, 张晰, 等. 雷达高度计在海冰厚度探测中的研究进展[J]. 遥感信息, 2020, 35(5):1-8.
[7] FEDOR L S, HAYNE G S, WALSH E J. Ice-type classifications from airborne pulse-limited radar altimeter return waveform characteristics[C]//Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium. Vancouver, BC:IEEE, 2002:1949-1952.
[8] DRINKWATER M. Ku band airborne radar altimeter observations of marginal sea ice during the 1984 Marginal Ice Zone Experiment[J]. Journal of Geophysical Research, 1991, 96(3):4555-4572.
[9] LAXON S. Sea ice altimeter processing scheme at the EODC[J]. International Journal of Remote Sensing, 1994, 15(4):915-924.
[10] ZYGMUNTOWSKA M, KHVOROSTOVSKY K, HELM V, et al. Waveform classification of airborne synthetic aperture radar altimeter over Arctic Sea ice[J]. The Cryosphere, 2013, 7(4):1315-1324.
[11] RINNE E, SIMILÄ M. Utilisation of CryoSat-2 SAR altimeter in operational ice charting[J]. The Cryosphere Discussions, 2015, 9(4):4117-4145.
[12] SHEN Xiaoyi, ZHANG Jie, ZHANG Xi, et al. Sea ice classification using cryosat-2 altimeter data by optimal classifier-feature assembly[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11):1948-1952.
[13] SHU Su, ZHOU Xinghua, SHEN Xiaoyi, et al. Discrimination of different sea ice types from CryoSat-2 satellite data using an object-based random forest (ORF)[J]. Marine Geodesy, 2020, 43(3):213-233.
[14] ZHANG Xi, ZHU Yixun, ZHANG Jie, et al. Assessment of Arctic Sea ice classification ability of Chinese HY-2B dual-band radar altimeter during winter to early spring conditions[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:9855-9872.
[15] JIANG Chengfei, LIN Mingsen, WEI Hao. A study of the technology used to distinguish sea ice and seawater on the Haiyang-2A/B (HY-2A/B) altimeter data[J].Remote Sensing, 2019, 11(12):1490.
[16] HANSEN R, RINNE E, SKOURUP H. Classification of sea ice types in the Arctic by radar echoes from SARAL/AltiKa[J]. Remote Sensing, 2021, 13(16):3183.
[17] KURTZ N T, GALIN N, STUDINGER M. An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting[J]. The Cryosphere, 2014, 8(4):1217-1237.
[18] RICKER R, HENDRICKS S, HELM V, et al. Sensitivity of CryoSat-2 Arctic Sea-ice freeboard and thickness on radar-waveform interpretation[J]. The Cryosphere, 2014, 8(4):1607-1622.