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

将同步卫星观测和机器学习结合起来，以改进冰盖模型和海平面预测

• 批准号: NE/W007282/1
• 负责人: Martin Wearing
• 金额: $ 6万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FW007282%2F1
• 关键词: Coupling; coincident; satellite; observations; machine

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

全球约3%的人口（2.3亿）生活在目前海平面1米以内（Kulp & Strauss 2019）。这些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，这些地区面临的灾难性洪水风险越来越大，因为对全球海平面上升的预测表明，到2100年，海洋可能上升1米（IPCC AR 6 2021）。此外，这些预测有很大的不确定性，部分原因是南极冰盖对未来海平面的影响不确定。准确可靠的冰盖模型对于减少未来全球海平面预测的不确定性至关重要，可以通过提高我们对复杂冰盖过程的理解来减少不确定性，例如冰破坏和冰-海洋相互作用，这对评估冰盖稳定性至关重要。南极洲卫星观测量的不断增加表明，有必要将多个传感器的信息联合收割机和提取，以便它们可以充分利用来调查冰盖dynamics.We建议利用多套遥感卫星观测，并开发一个数字基础设施，以处理和可视化一致观测冰盖表面图像和海拔在南极洲。这个新的数据集将是第一个此类数据集，也是提高我们对冰盖变化的理解的宝贵资源。它将作为科学界和公民科学家的资源免费提供。此外，作为该项目的一部分，我们将使用这一新的数据集，通过机器学习评估冰盖破坏的演变，从而展示评估多种形式的卫星数据在一起的优势，该项目将嵌入一个来自爱丁堡大学的冰川学家在地球观测专家，地球波的业务在地球物理数据分析和冰盖建模的专业知识。该项目的目标是：-提高EarthWave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提供，适合更广泛的科学界，并使其感兴趣。生成冰面图像和高程数据集，以调查冰盖损坏。训练一个神经网络来量化卫星图像中的冰害。直接利益相关者：主办组织：EarthWave，嵌入式研究：M.穿着，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，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