红外与毫米波学报2024，Vol.43Issue(3)：399-407,9.DOI:10.11972/j.issn.1001-9014.2024.03.014
基于激光数据的北极海水二氧化碳分压研究
Arctic sea surface CO2 partial pressure based on LiDAR
摘要
Abstract
The spaceborne light detection and ranging(LiDAR),as a novel active remote sensing technology,offers possibilities for global diurnal research.In this study,global sea surface chlorophyll-a(Chla)concentrations were in-verted using satellite data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO).A feed-forward neural network model based on LiDAR data(FNN-LID)was developed to reconstruct a long-term diurnal datas-et of sea surface p CO2 in the Arctic Ocean.Subsequently,verification and analysis were conducted on the polar sea sur-face Chla concentrations and sea surface p CO2 based on active remote sensing.The results demonstrated that the inver-sion products generated by this algorithm exhibit high data quality and exhibit favorable consistency with both other pas-sive remote sensing products and buoy observations.Moreover,these products effectively fill data gaps during polar winters.Along the Arctic Ocean,margin seas significantly influenced by terrestrial sources consistently display high sea surface Chla concentrations.The spatial distribution of sea surface p CO2 in the Arctic Ocean manifests meridional varia-tions,with marked seasonal fluctuations,even higher than 80 μatm.Over the past two decades,the Arctic Ocean has consistently acted as a carbon dioxide sink,while areas with substantial sea ice decline such as the East Siberian Sea and Kara Sea exhibit pronounced increases in sea surface p CO2.关键词
星载激光雷达反演/北冰洋/海水二氧化碳分压/极夜/长时序研究Key words
spaceborne LiDAR/arctic ocean/sea surface CO2 partial pressure/polar night/long-term variation分类
海洋科学引用本文复制引用
张思琪,陈鹏,张镇华,潘德炉..基于激光数据的北极海水二氧化碳分压研究[J].红外与毫米波学报,2024,43(3):399-407,9.基金项目
东海实验室预研项目(DH-2022ZY003),山东省重点研发计划(2023ZLYS01),国家自然科学基金(42322606,42276180,61991453,2022YFC3104200) Supported by the Donghai Laboratory Preresearch Project(DH-2022ZY0003),Key R&D Program of Shandong Province,China(2023ZLYS01),The National Natural Science Foundation of China(42322606,42276180,61991453,2022YFC3104200) （DH-2022ZY003）
