Day 4

Detailed paper information

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Paper title Monitoring Ice Sheets using Satellite Altimetry and Deep Learning
  1. Joe Phillips Lancaster Environment Centre, Lancaster University Speaker
  2. Malcolm McMillan Lancaster University
  3. Ce Zhang Lancaster University
Form of presentation Poster
  • C1. AI and Data Analytics
    • C1.04 AI4EO applications for Land and Water
Abstract text Since the 1990s, the melting of Earth’s Polar ice sheets has contributed approximately one-third of global sea level rise. As Earth’s climate warms, this contribution is expected to increase further, leading to the potential for social and economic disruption on a global scale. If we are to begin mitigating these impacts, it is essential that we better understand how Earth’s ice sheets evolve over time. 

Currently, our understanding of ice sheet change is largely informed by satellite observations, with the longest continuous record coming from the technique of satellite altimetry. These instruments provide high-resolution measurements of ice sheet surface elevation through time, allowing for estimates of ice sheet volume change and mass balance to be derived. Satellite radar altimeters work by transmitting a microwave pulse towards Earth’s surface and listening to the returned echo, which is recorded in the form of discrete waveforms that encode information about both the ice sheet surface topography and its electromagnetic scattering characteristics. Current methods for converting these waveforms into elevation measurements typically rely on a range of assumptions that are designed to reduce the dimensionality and complexity of the data. As a result, subtle, yet important, information can be lost.  

A potential alternative approach for information extraction comes in the application of deep learning algorithms, which have seen enormous success in diverse fields such as oceanography and radar imaging. Such approaches allow for the development of singular, data-driven methodologies that can bypass the many, successive, human-engineered steps in current processing workflows. Despite this, deep learning has yet to see application in the context of ice sheet altimetry. Here, we are therefore interested in exploring the potential of deep learning to extract deep and subtle information directly from the raw altimeter waveforms themselves, in order to drive new understanding of the contribution of polar ice sheets to global sea level rise. In this presentation we will provide first results from our preliminary analysis, together with a roadmap for the planned activities ahead.