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A 10-year record of Arctic summer sea ice freeboard from CryoSat-2
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-10-29 , DOI: 10.1016/j.rse.2021.112744
Geoffrey Dawson 1 , Jack Landy 1, 2 , Michel Tsamados 3 , Alexander S. Komarov 4 , Stephen Howell 5 , Harry Heorton 3 , Thomas Krumpen 6
Affiliation  

Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.



中文翻译:

来自 CryoSat-2 的 10 年北极夏季海冰干舷记录

迄今为止,对泛北极海冰厚度的卫星观测仅限于冬季月份。对于雷达高度计,传统方法无法区分夏季月份积聚在冰面的融水池中的铅,这是计算冰厚的关键步骤。在这里,我们使用来自夏季月份的 350 多张光学和合成孔径雷达 (SAR) 图像来训练一维卷积神经网络,以将 CryoSat-2 雷达高度计从海冰浮冰返回并以 >80% 的准确度分离。这使我们能够生成 2011 年至 2020 年 5 月至 9 月的第一个泛北极海冰雷达干舷测量值。结果表明,5 月和 9 月的干舷分布与 4 月和 10 月的传统“冬季”处理器的分布非常接近, 分别。干舷捕捉到北极夏季海冰融化的预期模式,与来自博福特环流勘探计划 (BGEP) 系泊设施的冰吃水观测结果相匹配。然而,与冰桥行动的机载激光扫描仪干舷和阿尔弗雷德韦格纳研究所 (AWI) IceBird 计划的机载电磁冰厚调查相比,CryoSat-2 干舷被低估了 0.02-0.2 m,冰厚被低估了 0.28-1.0 m ,最大的差异在于更厚的多年海冰。为了创建第一个泛北极夏季海冰厚度数据集,我们必须解决从雷达干舷到冰厚度转换过程中不确定性的主要来源。与来自博福特环流勘探计划 (BGEP) 系泊设施的冰吃水观测结果相匹配。然而,与冰桥行动的机载激光扫描仪干舷和阿尔弗雷德韦格纳研究所 (AWI) IceBird 计划的机载电磁冰厚调查相比,CryoSat-2 干舷被低估了 0.02-0.2 m,冰厚被低估了 0.28-1.0 m ,最大的差异在于更厚的多年海冰。为了创建第一个泛北极夏季海冰厚度数据集,我们必须解决从雷达干舷到冰厚度转换过程中不确定性的主要来源。与来自博福特环流勘探计划 (BGEP) 系泊设施的冰吃水观测结果相匹配。然而,与冰桥行动的机载激光扫描仪干舷和阿尔弗雷德韦格纳研究所 (AWI) IceBird 计划的机载电磁冰厚调查相比,CryoSat-2 干舷被低估了 0.02-0.2 m,冰厚被低估了 0.28-1.0 m ,最大的差异在于更厚的多年海冰。为了创建第一个泛北极夏季海冰厚度数据集,我们必须解决从雷达干舷到冰厚度转换过程中不确定性的主要来源。冰厚度被低估了 0.28-1.0 m,最大的差异是在较厚的多年海冰上。为了创建第一个泛北极夏季海冰厚度数据集,我们必须解决从雷达干舷到冰厚度转换过程中不确定性的主要来源。冰厚度被低估了 0.28-1.0 m,最大的差异是在较厚的多年海冰上。为了创建第一个泛北极夏季海冰厚度数据集,我们必须解决从雷达干舷到冰厚度转换过程中不确定性的主要来源。

更新日期:2021-10-29
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