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

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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
收稿：2022-09-26，

纸质出版：2023-11-07
DOI： 10.11834/jrs.20232464
稿件说明：

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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

Keywords

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