基于CFOSAT散射计的海冰识别方法研究

刘建强; 刘思琦; 林文明; 郎姝燕; 何宜军

doi:10.12284/hyxb2023069

基于CFOSAT散射计的海冰识别方法研究

doi: 10.12284/hyxb2023069

刘建强^1,,
刘思琦²,
林文明^{2, 3, ,},
郎姝燕¹,
何宜军^{2, 3}

1.
国家卫星海洋应用中心，北京 100081
2.
南京信息工程大学，江苏南京 210044
3.
自然资源部空间海洋遥感与应用重点实验室，北京 100081

基金项目: 国家重点研发计划（2021YFC2803300）。

详细信息

作者简介:
刘建强（1964—2023年），男，湖南省益阳市人，研究员，研究方向为卫星海洋遥感。E-mail：jqliu@mail.nsoas.org.cn

通讯作者:
林文明，男，福建省仙游县人，教授，研究方向为卫星海洋遥感。E-mail：wenminglin@nuist.edu.cn

中图分类号: P731.15
计量
- 文章访问数: 267
- HTML全文浏览量: 103
- PDF下载量: 36
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-18
- 修回日期: 2022-12-03
- 网络出版日期: 2023-06-15
- 刊出日期: 2023-06-30

Sea ice identification based on CFOSAT scatterometer

Liu Jianqiang^1
,,
Liu Siqi²,
Lin Wenming^{2, 3
, ,},
Lang Shuyan¹,
He Yijun^{2, 3}

1.
National Satellite Ocean Application Service, Beijing 100081, China
2.
Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

摘要

摘要: 中法海洋卫星散射计（CSCAT）丰富的观测几何信息为极地海冰遥感提供了新的机遇。本文提出一种适用于CSCAT的贝叶斯海冰识别算法，不需要构建海冰地球物理模式函数和计算后向散射系数离海冰地球物理模型函数（GMF）的距离，仅利用海面风场反演伴随的最小残差即可构建CSCAT海冰识别模型。研究结果与欧洲气象卫星组织的海冰边缘线产品进行了比较，表明2021年9月南极和北极区域逐日的海冰覆盖面积估计标准差分别为1%和7%，与其他卫星散射计的海冰识别结果基本一致。这种新的海冰识别方法具有模型参数少、处理速度快、检测结果可靠的优点，对卫星地面系统的业务化处理具有重要的借鉴意义。
- 中法海洋卫星 /
- 散射计 /
- 海冰检测 /
- 贝叶斯原理 /
- 最大似然估计
Abstract: The scatterometer onboard China-France Oceanography Satellite (CFOSAT) observes sea surface with abundant viewing geometries, opening up new opportunities for sea ice detection. This paper proposes a Bayesian sea ice detection method for the CFOSAT satellite scatterometer (CSCAT), which only uses the minimal inversion residual derived from the wind inversion procedure, hence it does not need to develop a sea ice geophysical model function (GMF) and to calculate the distance between CSCAT backscatters and sea ice GMF. The results are compared with the sea ice edge data from European Organisation for the Exploitation of Meteorological Satellites, which shows that the normalized standard deviation error of CSCAT daily sea ice extent is about 1% and 7% in the Antarctic and the Arctic, respectively, agreeing well with the prior scatterometers. In summary, the proposed method is advanced in terms of model input parameters, processing speed and detection accuracy, so it is of great significance to the operational ice detection in the satellite ground segment.
- China-France Oceanography Satellite (CFOSAT) /
- scatterometer /
- sea ice detection /
- Bayesian theory /
- maximum likelihood estimator

HTML全文

图 1 2021年9月1日OSI SAF海冰冰缘数据示意图

a. 北极，b. 南极

Fig. 1 Illustration of OSI SAF sea ice edge on September 1, 2021

a. Arctic, b. Antarctic

下载: 全尺寸图片幻灯片

图 2 中法海洋卫星散射计垂直极化σ⁰（dB）的二维等值线图

a. 南极海域，海水表面；b. 南极海域，稀疏冰面；c. 南极海域，密集冰面；d. 北极海域，海水表面；e. 北极海域，稀疏冰面；f. 北极海域，密集冰面

Fig. 2 Two-dimensional contour plots of CSCAT vertically-polarized σ⁰ (dB)

a. Antarctic, open water; b. Antarctic, open ice; c. Antarctic, close ice; d. Arctic, open water; e. Arctic, open ice; f. Arctic, close ice

下载: 全尺寸图片幻灯片

图 3 南极海域中法海洋卫星散射计垂直极化σ⁰的二维密度图

a. 海水表面；b. 稀疏海冰；c. 密集海冰。图中对应的风速为6～7 m/s，入射角为40°～41°

Fig. 3 Two-dimensional density plots of the CSCAT vertically-polarized σ⁰ at Antarctic

a. Open water; b. open ice; c. close ice. Here the wind speed is 6～7 m/s, and the incidence angle is 40°～41°

下载: 全尺寸图片幻灯片

图 4 南极海域不同海表面的MLE先验概率分布

Fig. 4 A priori probability of MLE over different sea surface in Antarctica

下载: 全尺寸图片幻灯片

图 5 2021年9月1日中法海洋卫星散射计反演的两极海冰概率

a. 北极，b. 南极

Fig. 5 Sea ice probability derived from CSCAT on September 1，2021

a. Arctic, b. Antarctic

下载: 全尺寸图片幻灯片

图 6 2021年9月1日中法海洋卫星散射计获取的海冰覆盖范围图

a. 北极，b. 南极

Fig. 6 Sea ice extents derived from CSCAT on September 1，2021

a. Arcti, b. Antarctic

下载: 全尺寸图片幻灯片

图 7 2021年9月中法海洋卫星散射计逐日海冰覆盖面积检测结果

Fig. 7 The CSCAT-derived daily sea ice coverage in September, 2019

下载: 全尺寸图片幻灯片

参考文献(17)

[1]	Liu Zheng, Schweiger A. Low-level and surface wind jets near sea ice edge in the Beaufort Sea in late autumn[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(13): 6873−6891. doi: 10.1029/2018JD029770
[2]	Wu Lichuan. Effect of atmosphere-wave-ocean/ice interactions on a polar low simulation over the Barents Sea[J]. Atmospheric Research, 2021, 248: 105183. doi: 10.1016/j.atmosres.2020.105183
[3]	Cohen J, Screen J A, Furtado J C, et al. Recent Arctic amplification and extreme mid-latitude weather[J]. Nature Geoscience, 2014, 7: 627−637. doi: 10.1038/ngeo2234
[4]	Parkinson C L. A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(29): 14414−14423. doi: 10.1073/pnas.1906556116
[5]	李彦青. 渤海海冰可见光遥感数据的反演、同化和候平均时间序列的构建分析[D]. 青岛: 中国海洋大学, 2013. Li Yanqing. The retrieval, assimilation, pentadly averaged time series building and analysis of sea ice in the Bohai Sea using the visible remote sensing data[D]. Qingdao: Ocean University of China, 2013.
[6]	石立坚, 王其茂, 邹斌, 等. 利用海洋(HY-2)卫星微波辐射计数据反演北极区域海冰密集度[J]. 极地研究, 2014, 26(4): 410−417. doi: 10.13679/j.jdyj.2014.4.410 Shi Lijian, Wang Qimao, Zou Bin, et al. Arctic sea ice concentration retrieval using HY-2 radiometer data[J]. Chinese Journal of Polar Research, 2014, 26(4): 410−417. doi: 10.13679/j.jdyj.2014.4.410
[7]	Johansson A M, Brekke C, Spreen G, et al. X-, C-, and L-band SAR signatures of newly formed sea ice in Arctic leads during winter and spring[J]. Remote Sensing of Environment, 2018, 204: 162−180. doi: 10.1016/j.rse.2017.10.032
[8]	Cartwright J, Fraser A D, Porter-Smith R. Polar maps of C-band backscatter parameters from the Advanced Scatterometer[J]. Earth System Science Data, 2022, 14(2): 479−490. doi: 10.5194/essd-14-479-2022
[9]	Long D G, Hardin P J, Whiting P T. Resolution enhancement of spaceborne scatterometer data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(3): 700−715. doi: 10.1109/36.225536
[10]	Remund Q P, Long D G. Sea ice extent mapping using Ku band scatterometer data[J]. Journal of Geophysical Research: Oceans, 1999, 104(C5): 11515−11527. doi: 10.1029/98JC02373
[11]	Remund Q P, Long D G. Sea ice mapping algorithm for QuikScat and seawinds[C]//IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No. 98CH36174). Seattle: IEEE, 1998: 1686−1688.
[12]	Rivas M B, Stoffelen A. New Bayesian algorithm for sea ice detection with QuikSCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 1894−1901. doi: 10.1109/TGRS.2010.2101608
[13]	Rivas M B, Verspeek J, Verhoef A, et al. Bayesian sea ice detection with the advanced scatterometer ASCAT[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7): 2649−2657. doi: 10.1109/TGRS.2011.2182356
[14]	赵朝方, 徐锐, 赵可. 基于HY-2A/SCAT数据极地海冰检测方法研究[J]. 中国海洋大学学报(自然科学版), 2019, 49(10): 140−149. doi: 10.16441/j.cnki.hdxb.20190275 Zhao Chaofang, Xu Rui, Zhao Ke. Research of Polar sea ice detection methods based on HY-2A/SCAT[J]. Periodical of Ocean University of China, 2019, 49(10): 140−149. doi: 10.16441/j.cnki.hdxb.20190275
[15]	Zhai Xiaochun, Wang Zhixiong, Zheng Zhaojun, et al. Sea ice monitoring with CFOSAT scatterometer measurements using random forest classifier[J]. Remote Sensing, 2021, 13(22): 4686. doi: 10.3390/rs13224686
[16]	Zou Juhong, Zeng Tao, Guo Maohua, et al. The study on an Antarctic sea ice identification algorithm of the HY-2A microwave scatterometer data[J]. Acta Oceanologica Sinica, 2016, 35(9): 74−79. doi: 10.1007/s13131-016-0927-5
[17]	Lin Wenming, Dong Xiaolong, Portabella M, et al. A perspective on the performance of the CFOSAT rotating fan-beam scatterometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 627−639. doi: 10.1109/TGRS.2018.2858852