微波遥感产品对南极普里兹湾海冰密集度准确性的评估

李若晗; 夏瑞彬; 张晓爽; 晁国芳; 陈忠彪; 王志勇

doi:10.11834/jrs.20232464

大气与海洋 | 浏览量 : 0 下载量: 139 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

微波遥感产品对南极普里兹湾海冰密集度准确性的评估
Accuracy of microwave remote sensing products in evaluating sea ice concentration in Prydz Bay， Antarctica
2023年27卷第11期页码：2499-2515
纸质出版日期： 2023-11-07 ，
DOI： 10.11834/jrs.20232464

扫描看全文

李若晗，夏瑞彬，张晓爽，晁国芳，陈忠彪，王志勇.2023.微波遥感产品对南极普里兹湾海冰密集度准确性的评估.遥感学报，27（11）： 2499-2515

Li R H，Xia R B，Zhang X S，Chao G F，Chen Z B and Wang Z Y. 2023. Accuracy of microwave remote sensing products in evaluating sea ice concentration in Prydz Bay， Antarctica. National Remote Sensing Bulletin， 27（11）：2499-2515
李若晗，夏瑞彬，张晓爽，晁国芳，陈忠彪，王志勇.2023.微波遥感产品对南极普里兹湾海冰密集度准确性的评估.遥感学报，27（11）： 2499-2515 DOI： 10.11834/jrs.20232464.

Li R H，Xia R B，Zhang X S，Chao G F，Chen Z B and Wang Z Y. 2023. Accuracy of microwave remote sensing products in evaluating sea ice concentration in Prydz Bay， Antarctica. National Remote Sensing Bulletin， 27（11）：2499-2515 DOI： 10.11834/jrs.20232464.

摘要

基于两种船测数据集，本文采用点对点和Beitsch共定位比较法，对被动微波遥感观测海冰密集度产品在南极普里兹湾区域适用性展开了一系列评估。首先，根据2012年—2021年中国第29、31、37次南极科考走航船测数据，依据SIC大小的不同，对8种遥感SIC产品进行了分类定量比较，结果表明NSIDC/NT2算法产品在各情况比较中均体现出最高相关性与最佳稳定性，在共定位比较结果中相关系数可达0.926，均方根差12%，平均偏差仅2%。其次，为弥补基于AMSR-2传感器系列产品历史数据缺乏难以分析长期变化的缺陷，本文同时应用1992年—2000年ASPeCt船测数据集，以相同的对比方式对4种遥感数据产品的季节循环和长期变化信号进行了评估。结果表明，该时间段反演准确度较2012年—2021年的个例比较结果有所降低，且反映出较大的季节差异，4种产品的偏差均出现从融冰期到结冰期的增长。该时间段内，基于SSM/I传感器的CDR与Bootstrap算法整体反演较好，相关系数均达到0.8以上，均方根差16%，偏差约为8%，但是在低SIC区域的反演仍然存在较大的偏差。本研究表明，目前微波遥感SIC产品在较小范围海区的精确度存在一定的不足，且随着SIC类型、季节、算法差异出现较大的波动。因此提高分辨率，尽量使用多源数据，结合冰况进行分类分析，对改善遥感SIC产品的准确性十分必要。

Abstract

We use the point-to-point method and Beitsch’s co-location comparison method to conduct a series of evaluations on the passive microwave remote sensing products (PM) for observing sea ice concentration (SIC) in the Prydz Bay

Antarctica by using two kinds of ship-based observation datasets. Considering the difference in ship-based observation data

we divide the comparison into two parts. First

according to the ship-based observation data of China’s 29th

31st

and 37th Antarctic scientific expedition in the period of 2012—2021

eight remote sensing SIC products are classified and quantitatively compared according to the size of SIC. Results show that NSIDC/NT2 product assesses the highest correlation and the best stability in all cases. In the co-location comparison

the correlation coefficient can reach 0.926

the Root Mean Square Error (RMSE) is 12%

and the average bias is only 2%. Second

to make up for the lack of historical data of AMSR2 sensor series products

we evaluate the seasonal cycle and long-term variation signals of four remote sensing data products by using the ASPeCt ship-based observation dataset from 1992 to 2000 in the same way. The inversion accuracy of this period is lower than the case-by-case comparison result from 2012 to 2021

and a tremendous seasonal difference is observed. The bias of the four products increases from the melting period to the freezing period. During this period

the overall inversion results of CDR and bootstrap algorithms based on SSM/I sensors are better

with correlation coefficients of more than 0.8

RMSE of 16%

and bias of approximately 8%. However

a large bias remains in the low SIC region. This study shows that the accuracy of PM SIC products in a small sea area is insufficient

and it fluctuates greatly with the difference in SIC type

season

and algorithm. Therefore

the necessary considerations are to modify the resolution

use multisource data as much as possible

and classify data according to the ice conditions. Referring to Beitsch’s idea of Antarctica partitioning and comparison

we further obtain the accuracy of remote sensing products under different ice conditions in a local region. We add China’s scientific research ship-based observation data to increase the sample numbers for investigating the Prydz Bay area

which covers rich surface ice types. The regional comparison provides a reference for understanding the limitations of PM SIC products in micro-area inversion and also guarantees ice prediction and navigation safety. Considering the rapid reduction in Antarctic sea ice in recent years and the appearance of a 40-year minimum Antarctic sea ice range in February 2022

high-precision real-time PM SIC products need to be developed to determine the causes of sea ice anomalies and simulate sea ice changes in the future. Knowing the inversion accuracy of various PM SIC products under different conditions will help improve the subsequent PM SIC products and fusion algorithms. In the future

more factors that affect the accuracy of PM inversions

such as ice thickness

ice type

and other factors

should be considered to evaluate PM SIC products in other regions of the Antarctic in detail.

关键词

南极普里兹湾海冰密集度被动微波遥感走航观测数据质量评估

Keywords

Prydz Baysea ice concentrationpassive microwave remote sensingship-based observationdata quality assessment

references

Beitsch A, Kern S and Kaleschke L. 2012. Comparison of AMSR-E sea ice concentrations with aspect ship observations around Antarctica//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 3257-3260 [DOI: 10.1109/IGARSS.2012.6350609http://dx.doi.org/10.1109/IGARSS.2012.6350609]

Beitsch A, Kern S and Kaleschke L. 2015. Comparison of SSM/I and AMSR-E sea ice concentrations with ASPeCt ship observations around Antarctica. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 1985-1996 [DOI: 10.1109/TGRS.2014.2351497http://dx.doi.org/10.1109/TGRS.2014.2351497]

Belchansky G I and Douglas D C. 2002. Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data. Remote Sensing of Environment, 81(1): 67-81 [DOI: 10.1016/S0034-4257(01)00333-9http://dx.doi.org/10.1016/S0034-4257(01)00333-9]

Burns B A. 1993. Comparison of SSM/I ice-concentration algorithms for the Weddell Sea. Annals of Glaciology, 17: 344-350 [DOI: 10.3189/S0260305500013082http://dx.doi.org/10.3189/S0260305500013082]

Cavalieri D J, Gloersen P and Campbell W J. 1984. Determination of sea ice parameters with the NIMBUS 7 SMMR. Journal of Geophysical Research: Atmospheres, 89(D4): 5355-5369 [DOI: 10.1029/JD089iD04p05355http://dx.doi.org/10.1029/JD089iD04p05355]

Comiso J C. 1986. Characteristics of arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research: Oceans, 91(C1): 975-994 [DOI: 10.1029/JC091iC01p00975http://dx.doi.org/10.1029/JC091iC01p00975]

Comiso J C. 1995. SSM/I sea ice concentrations using the bootstrap algorithm. Washington DC: National Aeronautics and Space Administration, Goddard Space Flight Center: 1380

EUMETSAT Ocean and Sea Ice Satellite Application Facility. 2011. Global Sea Ice Concentration Reprocessing Dataset 1978-2009 (v2, 2011), Norwegian and Danish Meteorological Institutes. [DOI: 10.15770/EUM_SAF_OSI_0008].

Gloersen P, Campbell W J, Cavalieri D J, Comiso J C, Parkinson C L and Zwally H J. 1992. Arctic and Antarctic sea ice, 1978-1987: satellite passive-microwave observations and analysis. Washington DC: Scientific and Technical Information Program, National Aeronautics and Space Administration

Ji Q and Pang X P. 2016. Comparison and analysis of Arctic sea ice concentration products during the fifth Chinese Arctic expedition. Journal of East China Jiaotong University, 33(5): 33-38

季青, 庞小平. 2016. 北极海冰密集度产品的走航比较与冰情分析. 华东交通大学学报, 33(5): 33-38 [DOI: 10.16749/j.cnki.jecjtu.2016.05.006http://dx.doi.org/10.16749/j.cnki.jecjtu.2016.05.006]

Kern S, Spreen G, Kaleschke L, De La Rosa S and Heygster G. 2007. Polynya Signature Simulation Method polynya area in comparison to AMSR-E 89GHz sea-ice concentrations in the Ross Sea and off the Adélie Coast, Antarctica, for 2002-05: first results. Annals of Glaciology, 46: 409-418 [DOI: 10.3189/172756407782871585http://dx.doi.org/10.3189/172756407782871585]

Knuth M A and Ackley S F. 2006. Summer and early-fall sea-ice concentration in the Ross Sea: comparison of in situ ASPeCt observations and satellite passive microwave estimates. Annals of Glaciology, 44: 303-309 [DOI: 10.3189/172756406781811466http://dx.doi.org/10.3189/172756406781811466]

Le K T and Shi J X. 1997. A study of circulation and mixing in the region of Prydz Bay, Antarctica. Studia Marina Sinica, 38(1): 39-52

乐肯堂, 史久新. 1997. 南极普里兹湾区环流与混合的研究. 海洋科学集刊, 38(1): 39-52

Li Z, Yan M, Liu K, Hui F M and Zhao J C. 2018. The assessment of the applicability of AMSR2 and SSMIS near real time satellite sea ice concentration during 5th CHINARE arctic cruise. Marine Forecasts, 35(3): 8-16

李钊, 严明, 刘凯, 惠凤鸣, 赵杰臣. 2018. 两种准实时遥感海冰密集度产品在中国第五次北极考察期间的适用性评估. 海洋预报, 35(3): 8-16 [DOI: 10.11737/j.issn.1003-0239.2018.03.002http://dx.doi.org/10.11737/j.issn.1003-0239.2018.03.002]

NSIDC. 2022. Arctic Sea Ice News and Analysis. https://nsidc.org/arcticseaicenews/2022/03/https://nsidc.org/arcticseaicenews/2022/03/

Ohshima K I, Fukamachi Y, Williams G D, Nihashi S, Roquet F, Kitade Y, Tamura T, Hirano D, Herraiz-Borreguero L, Field I, Hindell M, Aoki S and Wakatsuchi M. 2013. Antarctic Bottom Water production by intense sea-ice formation in the Cape Darnley polynya. Nature Geoscience, 6(3): 235-240 [DOI: 10.1038/NGEO1738http://dx.doi.org/10.1038/NGEO1738]

Ozsoy-Cicek B, Ackley S F, Worby A, Xie H J and Lieser J. 2011. Antarctic sea-ice extents and concentrations: comparison of satellite and ship measurements from International Polar Year cruises. Annals of Glaciology, 52(57): 318-326 [DOI: 10.3189/172756411795931877http://dx.doi.org/10.3189/172756411795931877]

Peng G, Meier W N, Scott D J and Savoie M H. 2013. A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth System Science Data, 5(2): 311-318 [DOI: 10.5194/essd-5-311-2013http://dx.doi.org/10.5194/essd-5-311-2013]

Pu S Z, Hu X M, Dong Z Q, Yu F and Chen X R. 2002. Features of circumpolar deep water, Antarctic bottom water and their movement near the Prydz Bay. Acta Oceanologica Sinica, 24(3): 1-8

蒲书箴, 胡筱敏, 董兆乾, 于非, 陈幸荣. 2002. 普里兹湾附近绕极深层水和底层水及其运动特征. 海洋学报, 24(3): 1-8 [DOI: 10.3321/j.issn:0253-4193.2002.03.001http://dx.doi.org/10.3321/j.issn:0253-4193.2002.03.001]

Shi J X, Le K T, Choi B H. 2002. Circulation and its seasonal variability in region around the Kerguelen Plateau. Acta Oceanologica Sinica, 21(1): 1-17

Shi Q, Su J, Heygster G, Shi J X, Wang L Z, Zhu L Z, Lou Q L and Ludwig V. 2021. Step-by-step validation of Antarctic ASI AMSR-E sea-ice concentrations by MODIS and an aerial image. IEEE Transactions on Geoscience and Remote Sensing, 59(1): 392-403 [DOI: 10.1109/TGRS.2020.2989037http://dx.doi.org/10.1109/TGRS.2020.2989037]

Shibata H, Izumiyama K, Tateyama K, Enomoto H and Takahashi S. 2013. Sea-ice coverage variability on the Northern Sea Routes, 1980-2011. Annals of Glaciology, 54(62): 139-148 [DOI: 10.3189/2013AoG62A123http://dx.doi.org/10.3189/2013AoG62A123]

Spreen G, Kaleschke L and Heygster G. 2008. Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research: Oceans, 113(C2): C02S03 [DOI: 10.1029/2005JC003384http://dx.doi.org/10.1029/2005JC003384]

Su J, Hao G H, Ye X X and Wang W B. 2013. The experiment and validation of sea ice concentration AMSR-E retrieval algorithm in polar region. Journal of Remote Sensing, 17(3): 495-513

苏洁, 郝光华, 叶鑫欣, 王维波. 2013. 极区海冰密集度AMSR-E数据反演算法的试验与验证. 遥感学报, 17(3): 495-513 [DOI: 10.11834/jrs.20132043http://dx.doi.org/10.11834/jrs.20132043]

Wang J, Qu Y B, Xiong Z H, Yuan X P, Zhou J T, Huang L and Shi L J. 2020. Comparison and verification of remote sensing sea ice concentration products for Arctic shipping regions. Chinese Journal of Polar Research, 32(3): 301-313

王剑, 邱玉宝, 熊振华, 袁希平, 周静恬, 黄琳, 石利娟. 2020. 北极海冰密集度遥感数据产品对比及航道关键区验证研究. 极地研究, 32(3): 301-313 [DOI: 10.13679/j.jdyj.20190054http://dx.doi.org/10.13679/j.jdyj.20190054]

Wang J F, Yang Q H, Yu L J, Song M R, Luo H, Shi Q, Li X W, Min C and Liu J P. 2021. A review on Antarctic sea ice change and its climate effects. Acta Oceanologica Sinica, 43(7): 11-22

王今菲, 杨清华, 于乐江, 宋米荣, 罗昊, 施骞, 李雪薇, 闵超, 刘骥平. 2021. 南极海冰变化及其气候效应研究述评. 海洋学报, 43(7): 11-22 [DOI: 10.12284/hyxb2021151http://dx.doi.org/10.12284/hyxb2021151]

Wang X Y, Guan L and Li L L. 2018. Comparison and validation of sea ice concentration from FY-3B/MWRI and Aqua/AMSR-E observations. Journal of Remote Sensing, 22(5): 723-736

王晓雨, 管磊, 李乐乐. 2018. FY-3B/MWRI和Aqua/AMSR-E海冰密集度比较及印证. 遥感学报, 22(5): 723-736 [DOI: 10.11834/jrs.20187419http://dx.doi.org/10.11834/jrs.20187419]

Weissling B, Ackley S, Wagner P and Xie H. 2009. EISCAM—Digital image acquisition and processing for sea ice parameters from ships. Cold Regions Science and Technology, 57(1): 49-60 [DOI: 10.1016/j.coldregions.2009.01.001http://dx.doi.org/10.1016/j.coldregions.2009.01.001]

Worby A P, Allison I, Dirita V. 1999. A technique for making ship-based observations of Antarctic sea ice thickness and characteristics. Antarctic: Antarctic CRC

Worby A P and Comiso J C. 2004. Studies of the Antarctic sea ice edge and ice extent from satellite and ship observations. Remote Sensing of Environment, 92(1): 98-111 [DOI: 10.1016/j.rse.2004.05.007http://dx.doi.org/10.1016/j.rse.2004.05.007]

Worby A P, Geiger C A, Paget M J, Van Woert M L, Ackley S F and Deliberty T L. 2008. Thickness distribution of Antarctic sea ice. Journal of Geophysical Research: Oceans, 113(C5): C05S92 [DOI: 10.1029/2007JC004254http://dx.doi.org/10.1029/2007JC004254]

Xie S M, Wei L X, Hao C J, Zhang H Y and Mei S. 2003a. Change variation of the Antarctic sea ice and shelf ice. Acta Oceanologica Sinica, 25(3): 32-46

解思梅, 魏立新, 郝春江, 张海英, 梅山. 2003a. 南极海冰和陆架冰的变化特征. 海洋学报, 25(3): 32-46 [DOI: 10.3321/j.issn:0253-4193.2003.03.004http://dx.doi.org/10.3321/j.issn:0253-4193.2003.03.004]

Xie S M, Wei L X, Zhang Z H, Mei S and Hao C J. 2003b. Variation trends of the Antarctic sea ice and shelf ice. Journal of Glaciology and Geocryology, 25(Z2): 234-240

解思梅, 魏立新, 张占海, 梅山, 郝春江. 2003b. 南极海冰和陆架冰的时空变化动态. 冰川冻土, 25(Z2): 234-240 [DOI: 10.3969/j.issn.1000-0240.2003.z2.006http://dx.doi.org/10.3969/j.issn.1000-0240.2003.z2.006]

Xiu Y, Li Z J, Lei R B, Wang Q K, Lu P and Leppäranta M. 2020. Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018. Acta Oceanologica Sinica, 39(9): 38-49 [DOI: 10.1007/s13131-020-1646-5http://dx.doi.org/10.1007/s13131-020-1646-5]

Xue Y G, Guan H, Dong X J and Chen F. 2014. Analysis of changes of Arctic sea ice extents in recent 40 years. Marine Forecasts, 31(4): 85-91

薛彦广, 关皓, 董兆俊, 陈飞. 2014. 近40年北极海冰范围变化特征分析. 海洋预报, 31(4): 85-91 [DOI: 10.11737/j.issn.1003-0239.2014.04.012http://dx.doi.org/10.11737/j.issn.1003-0239.2014.04.012]

Yan B H and Weng F Z. 2008. Intercalibration between special sensor microwave imager/sounder and special sensor microwave imager. IEEE Transactions on Geoscience and Remote Sensing, 46(4): 984-995 [DOI: 10.1109/TGRS.2008.915752http://dx.doi.org/10.1109/TGRS.2008.915752]

Zhao J C, Zhang L, Tian Z X, Li M, Hui F M, Li C H and Han H W. 2014. Sea ice distribution in the Ross Sea, Antarctica, during the austral summer of 2012. Chinese Journal of Polar Research, 26(3): 342-351

赵杰臣, 张林, 田忠翔, 李明, 惠凤鸣, 李春花, 韩红卫. 2014. 南极罗斯海2012年夏季海冰特征分析. 极地研究, 26(3): 342-351 [DOI: 10.13679/j.jdyj.2014.3.342http://dx.doi.org/10.13679/j.jdyj.2014.3.342]

Zhao J C, Zhou X, Sun X Y, Cheng J J, Hu B and Li C H. 2017. The inter comparison and assessment of satellite sea-ice concentration datasets from the Arctic. Journal of Remote Sensing, 21(3): 351-364

赵杰臣, 周翔, 孙晓宇, 程净净, 胡波, 李春花. 2017. 北极遥感海冰密集度数据的比较和评估. 遥感学报, 21(3): 351-364 [DOI: 10.11834/jrs.20176136http://dx.doi.org/10.11834/jrs.20176136]

Zheng S J. 2011. Effects of Sea Ice and Ice Shelf on the Ocean Processes in Prydz Bay, Antarctica. Qingdao: Ocean University of China

郑少军. 2011. 海冰和冰架对南极普里兹湾海洋过程的影响研究. 青岛: 中国海洋大学 [DOI: 10.7666/d.d169404http://dx.doi.org/10.7666/d.d169404]

文章被引用时，请邮件提醒。

提交

北极遥感海冰密集度数据的比较和评估

被动微波遥感反演雪深与气象站观测雪深时空对比

89 GHz多入射角亮温差反演海冰密集度的可行性分析

FY-3B/MWRI北极海冰密集度ASI算法反演研究