Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach

In recent years, We focus on the Snow Radar [1] dataset collected by the airborne radar sensors, such as the Snow Radar, have been Center for Remote Sensing of Ice Sheets (CReSIS) as part of shown to be able to measure these internal ice layers over NASA's Operation IceBridge. The Snow Radar operates from large areas with a fine vertical resolution. In our previous 2-8 GHz and is able to track deep layers of ice with a high resolution work, we found that temporal graph convolutional networks over wide areas of an ice sheet. The sensor produces perform reasonably well in predicting future snow accumulation a two-dimensional grayscale profile of historic snow accumulation when given temporal graphs containing deep ice layer over consecutive years, where the horizontal axis represents thickness. In this work, we experiment with a graph attention the along-track direction, and the vertical axis represents network-based model and used it to predict more annual layer depth. Pixel brightness is directly proportional to snow accumulation data points with fewer input data points the strength of the returning signal.