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Accuracy of microwave remote sensing products in evaluating sea ice concentration in Prydz Bay， Antarctica

  • role： First author第一作者
  • Affiliation：

    Department of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：201983450046@nuist.edu.cn
  • Introduction：李若晗，研究方向为物理海洋。E-mail： 201983450046@nuist.edu.cn
LI Ruohan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

  • Email：xiarb@nuist.edu.cn
  • Introduction：夏瑞彬，研究方向为物理海洋学、海气相互作用。E-mail： xiarb@nuist.edu.cn
XIA Ruibin1*,  
  • Affiliation：

    Key Laboratory of Ministry of Natural Resource for Marine Environmental Information Technology, National Marine Data and Information Service, Ministry of Natural Resource, Tianjin 300171, China

ZHANG Xiaoshuang2,  
  • Affiliation：

    Key Laboratory of Ministry of Natural Resource for Marine Environmental Information Technology, National Marine Data and Information Service, Ministry of Natural Resource, Tianjin 300171, China

CHAO Guofang2,  
  • Affiliation：

    Department of Marine Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

CHEN Zhongbiao1,  
  • Affiliation：

    North China Sea Marine Forecasting Center of Ministry of Natural Resources of PRC, Qingdao 266100, China

WANG Zhiyong3

résumé

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.

mots-clés

Prydz Bay;sea ice concentration;passive microwave remote sensing;ship-based observation;data quality assessment

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