In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Baye...In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.展开更多
Sea ice type is an important factor for accurately calculating sea ice parameters such as sea ice concentration, sea ice area and sea ice thickness using satellite remote sensing data. In this study, sea ice in the We...Sea ice type is an important factor for accurately calculating sea ice parameters such as sea ice concentration, sea ice area and sea ice thickness using satellite remote sensing data. In this study, sea ice in the Weddell Sea was classified from scatterometer data by the histogram threshold method and the Spreen model method, and evaluated and validated with the Antarctic Sea Ice Processes and Climate(ASPeCt) sea ice type ship-based observations. The results show that the two methods can both distinguish multi-year(MY) ice and first-year(FY) ice during the ice growth season, and that the histogram threshold method has a relatively larger MY ice classification extent than the Spreen model. The classification accuracy of the histogram threshold method is 77.8%, while the Spreen model method accuracy is 80.3% compared with the ship-based observations, thus indicating that the Spreen model method is better for discriminating MY ice from FY ice from scatterometer data. These results provide a basis and reference for further retrieval of long-time sea ice type information for the whole Antarctica.展开更多
Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The res...Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The results indicate that the radar wave can penetrate Arctic summer sea ice of over 6 meters thick; and the propagation velocity of the radar wave in sea ice is in the range of 0.142 m·ns -1 to 0.154 m·ns -1 . The radar images display the roughness and micro-relief variation of sea ice bottom surface. These features are closely related to sea ice types, which show that radar survey may be used to identify and classify ice types. Since radar images can simultaneously display the linear profile features of both the upper surface and the underside of sea ice, we use these images to quantify their actual linear length discrepancy. A new length factor is suggested in relation to the actual linear length discrepancy in linear profiles of sea ice, which may be useful in further study of the area difference between the upper surface and bottom surface of sea ice.展开更多
基金supported by the National Natural Science Foundation of China(No.42030406)。
文摘In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.
基金the supports from the National Natural Science Foundation of China (Grant nos. 41606215 and 41576188)the National Key Research and Development Program of China (Grant no. 2017YFA0603104)+3 种基金the fund of SOA Key Laboratory for Polar Science (Grant no. PS1502)the fund of Key Laboratory of Global Change and Marine-Atmospheric Chemistry, SOA (Grant no. GCMAC1504)the Fundamental Research Funds for the Central Universities (Grant no. 2042016kf0038)the Chinese Postdoctoral Science Foundation Funded Project (Grant no. 2016M602342)
文摘Sea ice type is an important factor for accurately calculating sea ice parameters such as sea ice concentration, sea ice area and sea ice thickness using satellite remote sensing data. In this study, sea ice in the Weddell Sea was classified from scatterometer data by the histogram threshold method and the Spreen model method, and evaluated and validated with the Antarctic Sea Ice Processes and Climate(ASPeCt) sea ice type ship-based observations. The results show that the two methods can both distinguish multi-year(MY) ice and first-year(FY) ice during the ice growth season, and that the histogram threshold method has a relatively larger MY ice classification extent than the Spreen model. The classification accuracy of the histogram threshold method is 77.8%, while the Spreen model method accuracy is 80.3% compared with the ship-based observations, thus indicating that the Spreen model method is better for discriminating MY ice from FY ice from scatterometer data. These results provide a basis and reference for further retrieval of long-time sea ice type information for the whole Antarctica.
基金This work was supported by the National Natural Science Foundation of China(No.4007 1022,40231013)the Ministry of Science and technology,the People's Republic of China(No.2001DIA50040)Chinese Arctic expedition foundation and Laboratory foundation of Ice Core and Cold Region Environment,Cold and Arid Regions Environmental and Engineering Institute,Chinese Academy of Sciences(No.BX2001-04).
文摘Based on radar penetrating measurements and analysis of sea ice in the Arctic Ocean, The potential of radar wave to measure sea ice thickness and map the morphology of the underside of sea ice is investigated. The results indicate that the radar wave can penetrate Arctic summer sea ice of over 6 meters thick; and the propagation velocity of the radar wave in sea ice is in the range of 0.142 m·ns -1 to 0.154 m·ns -1 . The radar images display the roughness and micro-relief variation of sea ice bottom surface. These features are closely related to sea ice types, which show that radar survey may be used to identify and classify ice types. Since radar images can simultaneously display the linear profile features of both the upper surface and the underside of sea ice, we use these images to quantify their actual linear length discrepancy. A new length factor is suggested in relation to the actual linear length discrepancy in linear profiles of sea ice, which may be useful in further study of the area difference between the upper surface and bottom surface of sea ice.
