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2012-2021年间中国第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AMSR2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1992-2000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2012-2021年间的个例比较结果有所降低，且反映出较大的季节差异，四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。

Based on the two kinds of ship-based observation data sets, we use the point-to-point method and Beitsch"s co-location comparison method to carry out a series of evaluations on the passive microwave remote sensing products (PM) for observing sea ice concentration (SIC) in the Prydz Bay, Antarctica. Considering the difference in ship-based observation data, we divided the comparison into two parts: Firstly, according to the ship-based observation data of China"s 29th, 31st, and 37th Antarctic scientific expedition in 2012-2021, eight remote sensing SIC products are classified, and quantitatively compared according to the size of SIC. The 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%. Secondly, to make up for the AMSR2 sensor series products" lack of historical data, we evaluate the seasonal cycle and long-term variation signals of four remote sensing data products by using the ASPeCt ship-based observation data set from 1992 to 2000 in the same way. The results show that the inversion accuracy of this period is lower than the case-by-case comparison result from 2012 to 2021, and reflects a tremendous seasonal difference. The bias of the four products increases from the melting period to the freezing period. During this period, the overall inversion 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 about 8%. However, there remains a large bias in the low SIC region. This study shows that the accuracy of PM SIC products in a small sea area is insufficient, and fluctuates greatly with the difference in SIC type, season, and algorithm. Therefore, it is necessary to modify the resolution, use multi-source 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 to research 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 of Antarctic sea ice in recent years and the appearance of a 40-year minimum Antarctic sea ice range in February 2022, It is imperative to develop high-precision real-time PM SIC products to find out the causes of sea ice anomalies and simulate sea ice changes in future. Knowing the inversion accuracy of various PM SIC products under different conditions will help to 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.