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Classification of ice and water in the Arctic using radar altimeter and microwave radiometer data from HY-2B satellite
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作者 Chengfei Jiang Mingsen Lin +5 位作者 Ruixue Cao Hao Wei Lijian Shi Bin Cheng Yongjun Jia Qimao Wang 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2023年第5期179-191,共13页
Several Chinese marine satellites have been launched in recent years.Monitoring sea ice and the ocean in the Arctic is of great importance for climate research.Sea ice in the Arctic has changed rapidly during the past... Several Chinese marine satellites have been launched in recent years.Monitoring sea ice and the ocean in the Arctic is of great importance for climate research.Sea ice in the Arctic has changed rapidly during the past few decades with respect to the extent and thickness.In this study,we applied combined passive and active microwave data from the Chinese HaiYang-2B(HY-2B)satellite to classify ice and sea water in the Arctic.We use data from a radar altimeter(RA)and a calibration microwave radiometer(CMR)to discriminate between ice and water by applying several approaches(1)the single parameter threshold criteria,(2)the multi-parameters linear segmentations and(3)the K-means clustering.The results yielded by these methods were in good agreement(classification accuracy>95%)with the Satellite Application Facility on Ocean and Sea Ice products between November and April.For other months(May–October),however,the agreement was less good(lowest classification accuracy approximate 85%in summer).A hybrid approach combined with graphical ice edges detection and microwave radar waveform analysis is therefore developed.A visual comparison with SAR images suggested the hybrid approach results greatly improved the ice and water discrimination in summer.This study demonstrated that multi-sensors(RA and CMR)configurations from HY satellites can offer comparable polar earth observation to the European Space Agency and NOAA satellite products. 展开更多
关键词 ARCTIC active microwave data passive microwave data sea ice seawater
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Variation of Antarctic marginal ice zone extent (1989-2019) 被引量:1
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作者 WANG Miaojiang LIU Tingting +2 位作者 YANG Zijian WU Bing ZHU Xueming 《Advances in Polar Science》 CSCD 2021年第4期341-355,共15页
The Antarctic marginal ice zone(MIZ)is the transition region between open water and consolidated pack ice,which is defined as an area with 15%-80%sea ice concentration.The MIZ represents the outer circle of Antarctic ... The Antarctic marginal ice zone(MIZ)is the transition region between open water and consolidated pack ice,which is defined as an area with 15%-80%sea ice concentration.The MIZ represents the outer circle of Antarctic sea ice and the biological activity circle of Antarctic organisms,which provides a direct indication of the extent of Antarctic sea ice.In this study,the joint total variation and nonnegative constrained least square algorithm are applied to retrieve the Antarctic MIZ extent based on passive microwave data sets from 1989 to 2019.The spatial and temporal variations of the Antarctic MIZ extent and five regions are analyzed.The results show that the Antarctic MIZ extent follows a strong monthly variation pattern,decreasing from November to February and increasing from March to October.The annual MIZ extent is largest in the Weddell Sea and smallest in the Western Pacific Ocean.The edge of the sea ice begins to form a closed ring in May,which eventually closes near the Antarctic Peninsula.The ring width variation is large in summer,but generally stabilizes between 350 and 370 km in winter.The average latitude of the Antarctic MIZ is relatively stable in summer,but changes substantially in winter with a difference of approximately 3°.In October,the lowest mean latitude of the MIZ can reach 64.35°S.The sea surface pressure,2-m temperature,and 10-m wind speed are negatively correlated with the MIZ extent variation,among which the second-order partial correlation coefficient of the sea surface pressure and MIZ extent is−0.8773 in the Western Pacific Ocean. 展开更多
关键词 Antarctic marginal ice zone passive microwave data retrieval algorithm correlation analysis VARIATION
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