摘要
选取国家海洋卫星应用中心提供的海洋二号B星扫描辐射计数据采用NASA TEAM算法反演的海冰密集度产品(简称HY2数据集),中国海洋大学提供的风云三号D星微波成像仪采用DT-ASI算法得到的海冰密集度产品(简称OUC数据集),以及美国冰雪中心提供的海冰密集度产品(简称NSIDC数据集)三种数据源对北极海冰监测能力进行比较分析。通过与德国不莱梅大学发布的海冰密集度数据产品(简称BRM数据集)和MODIS数据提取的海冰信息的比较发现:在低纬度区域(≤70°N),HY2与BRM数据集最为接近;在中纬度区域(70°N—80°N),OUC与BRM数据集的数据吻合程度最高;在高纬度区域(80°N—87°N),NSIDC数据集与BRM数据集最接近。在北极东北航道区域,HY2数据集适用于通航窗口期第一和第四航段内的海冰监测;NSIDC数据集适用于东西伯利亚海域以及临近窗口期时段的海冰监测;而OUC数据集则适用于北极东北航道大部分航段的海冰监测需求。
In this paper,three sea ice concentration products are compared:i)data from the Scanning Microwave Radiometer carried on the HY-2B satellite combined with NASA’s TEAM algorithm(“the HY2 dataset”),ii)data from the Microwave Radiometer Imager carried on FY-3D satellite combined with the DT-ASI algorithm provided by Ocean University of China(“the OUC dataset”),and iii)the sea ice concentration product provided by the Ice and Snow Center of the United States(“the NSIDC dataset”).Using the BRM sea ice concentration product with higher spatial resolution and the Moderate Resolution Imaging Spectrometer(MODIS)remote sensing images as reference datasets,we report that:At low latitudes(≤70°N),HY2 is most consistent with.In the mid-latitude region(70°N–80°N),OUC was the most consistent with BRM.At high latitudes(80°N–87°N),the NSIDC dataset is most consistent with the BRM dataset.In the Northeast Passage region,the HY2 dataset is most suitable for sea ice monitoring in the Chukchi Sea and Norwegian Sea segments of the Arctic Northeast Passage during navigation window periods.The NSIDC dataset performs better in each section of the Arctic Northeast Passage near the navigable window period,especially for sea ice monitoring in the East Siberian Sea.The OUC dataset is suitable for the sea ice monitoring needs of most sections of the Arctic Northeast Passage.
作者
黄睿
王常颖
李劲华
隋毅
Huang Rui;Wang Changying;Li Jinhua;Sui Yi(College of Computer Science&Technology,Qingdao University,Qingdao 266071,China)
出处
《极地研究》
CAS
CSCD
北大核心
2022年第4期471-484,共14页
Chinese Journal of Polar Research
基金
国家自然科学基金(62172247)
山东省重点研发计划重大科技创新工程(2019JZZY020101)
全国统计科学研究项目(2020335)资助。