来自哨兵-3 号的共址 OLCI 光学成像和合成孔径雷达测高仪用于加强北极春季海冰表面分类

Weibin Chen, M. Tsamados, R. Willatt, So Takao, D. Brockley, Claude De Rijke-Thomas, Alistair Francis, Thomas Johnson, Jack Landy, Isobel R. Lawrence, Sanggyun Lee, Dorsa Nasrollahi Shirazi, Wenxuan Liu, Connor Nelson, Julienne Stroeve, Len Hirata, M. Deisenroth
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引用次数: 0

摘要

分别于2016年2月和2018年4月发射的哨兵-3A号和哨兵-3B号卫星在低温卫星-2号的基础上,提供了北纬81度以内极地地区的高分辨率Ku波段雷达测高数据。将哨兵-3A 和哨兵-3B 的合成孔径雷达(SAR)模式测高仪(SRAL 仪器)与海洋和陆地色彩仪器(OLCI)成像光谱仪相结合,创建了首个提供光学图像和 SAR 雷达测高仪的卫星平台。我们利用测高和成像之间的协同作用,展示了深度学习在区分春季海冰和引线方面的新应用。我们使用 SRAL 分类的线索作为训练输入,从 OLCI 图像中进行泛北极线索检测。这种表面分类是估算海冰厚度以及预测北极和南极地区未来海冰变化的重要步骤。我们建议使用视觉变换器(ViT)来完成这项任务,这是一种改编自流行的深度学习算法变换器的方法。我们在几幅完整的 OLCI 图像上展示了其在包括准确性在内的定量指标和包括模型扩展在内的定性指标两方面的有效性,并且与之前的机器学习和经验方法相比,我们展示了更高的技能。我们还展示了该方法的潜力,即在融化开始前的日照时间,以更高的精度和空间分辨率提供铅含量检索。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification
The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.
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