Coupling coincident satellite observations and machine learning to improve ice-sheet models and sea-level projections

大约3%的全球人口(2.3亿)生活在当前海平面1米以内(Kulp&Strauss，2019年)。这些地势低洼的沿海地区也是许多重要基础设施的所在地，如交通网络、发电站和工厂。然而，这些地区面临着越来越大的灾难性洪水风险，因为全球海平面上升的预测表明，到2100年，海平面可能上升1米(政府间气候变化专门委员会AR6,2021年)。此外，这些预测存在很大的不确定性，部分原因是南极冰盖对未来海平面的不确定贡献。准确和可靠的冰盖模型对于减少未来全球海平面预测中的不确定性至关重要。通过提高我们对复杂冰盖过程的理解，例如冰破坏和冰与海洋的相互作用，可以减少不确定性，这些过程对于评估冰盖稳定性至关重要。南极洲不断增长的卫星观测量表明，需要结合和提取来自多个传感器的信息，以便充分利用它们来研究冰盖动力学。我们建议利用多组遥感卫星观测并开发数字基础设施，以处理和可视化南极洲冰盖表面图像和海拔高度的重合观测。这一新的数据集将是此类数据集中的第一个，也是增进我们对冰盖变化的理解的宝贵资源。它将作为科学界和民间科学家的资源免费提供。此外，作为项目的一部分，我们将使用这一新的数据集通过机器学习来评估冰盖破坏的演变，从而展示同时评估多种形式的卫星数据的优势。该项目将在地球观测专家地球波的业务中嵌入一名来自爱丁堡大学的冰川学家，他具有地球物理数据分析和冰盖建模方面的专门知识。该项目的目标是：-增加地球波处理卫星图像的数字能力，作为其现有多卫星数据服务的一部分。-使南极多卫星数据产品免费可用，适用于更广泛的科学界，并引起更广泛的科学界的兴趣。-生成冰面图像和海拔的数据集，以调查冰盖破坏。-训练神经网络，以量化卫星图像造成的冰损害。直接利益相关者：主办组织：地波，嵌入式研究：M.佩林，更广泛的利益相关者：NERC，NERC-NSF ITGC，ITGC Prophet，国家和地方政府，欧洲航天局，NASA，普通公众/公民科学家，沿海社区关键词：卫星遥感，地球观测，海平面上升，南极洲，大数据，人工智能，机器学习，冰川学，冰盖，冰架

Approximately 3% of the global population (230 M) live within 1 m of current sea level (Kulp & Strauss 2019). These low-lying coastal areas are also home to many forms of vital infrastructure such as transport networks, power stations and factories. However, these areas are increasingly at risk of catastrophic flooding as projections of global sea-level rise suggest that oceans may rise by up to 1 m by 2100 (IPCC AR6 2021). Furthermore, these predictions have large uncertainties, in part due to the uncertain contributions to future sea level from the Antarctic ice sheet. Accurate and reliable ice-sheet models are vital for reducing uncertainties in projections of future global sea-level.Uncertainties can be reduced by improving our understanding of complex ice-sheet processes, such as ice damage and ice-ocean interactions, which are crucial for assessing ice-sheet stability. The ever increasing volume of satellite observations from Antarctica signals a need to combine and distill information from multiple sensors so that they can be fully utilised to investigate ice-sheet dynamics.We propose to harness multiple sets of remote-sensing satellite observations and develop a digital infrastructure to process and visualise coincident observations of ice-sheet surface imagery and elevation in Antarctica. This new dataset will be the first of its kind and a valuable resource for improving our understanding of ice-sheet change. It will be made freely available as a resource for the scientific community and citizen scientists. Furthermore, as part of the project we will use this new dataset to assess the evolution of ice-sheet damage using machine learning thereby demonstrating the advantages of assessing multiple forms of satellite data together.This project will embed a glaciologist from the University of Edinburgh with expertise in geophysical data analysis and ice-sheet modelling within the operations of earth-observation specialists, EarthWave. The aims of the project are:- To increase the digital capabilities of EarthWave to handle satellite imagery as part of their existing multi-satellite data service.- To make the Antarctic multi-satellite data product freely available, suitable for, and of interest to, the wider scientific community.- To generate a dataset of ice-surface imagery and elevation to investigate ice-sheet damage.- Train a neural network to quantify ice damage from satellite imagery. Direct Stakeholders: Host Organisation: EarthWave, Embedded Research: M. Wearing, Wider Stakeholders: NERC, NERC-NSF ITGC, ITGC PROPHET, National and Local government, European Space Agency, NASA, General Public/Citizen Scientists, Coastal CommunitiesKeywords: Satellite remote-sensing, earth observation, sea-level rise, Antarctica, big-data, artificial intelligence, machine learning, glaciology, ice sheets, ice shelves