Using Remote Sensing Data For Predicting Sea Ice Thickness
Student Researcher: Theresa Chen
Mentor: Satish Puri
Rapid melting of sea ice is one of the most urgent environmental problems today. In order to better understand the effects of climate change, it is important to develop accurate models of sea ice change. This project will use machine learning to predict the thickness of sea ice around polar regions. Sea ice thickness is not only an important indicator of how the polar oceans are responding to a warming climate but also useful for forecasting of future changes in the polar regions.
Data used for this project was collected from NASA's Ice, Cloud, and Land Elevation Satellite (ICESat‐2), launched in 2018 to measure accurate surface heights for understanding changes in ice sheets and sea ice around the arctic. The satellite uses lidar remote sensing to collect elevation (height) data for different locations around the earth which is used to estimate the thickness of the sea ice. The data consists of tuples, where each tuple has four variables x, y, z, t where x, y are co-ordinates for latitude, longitude, z is the elevation of the surface of the earth and t is time. The data is collected by a satellite, and it is publicly available for science applications. The dataset size is in terabytes.
Students will gain experience with all aspects of data science – data cleaning, data wrangling, data visualization, implementing and evaluating machine learning models for accuracy, etc.
Student Research Activities
Research activities encompass many aspects of data science: data cleaning, data wrangling, data visualization, implementing and evaluating machine learning models for accuracy, etc.