Arctic Meltponds: Automated Detection Algorithm Using Enhanced Machine Learning

Abstract

Melt ponds are pools of water that form during summer on the surface of the arctic ice. Due to the lower albedo, melt ponds absorb more solar radiation than surrounding ice and hence have higher temperature. This causes more water to melt, creating a feedback loop. This means that melt pond fraction in ice sheets is an important factor to consider in global climate and sea ice models. In situ measurements are difficult and expensive in terms of time and labor. Furthermore, these measurements can only cover limited areas. This makes using Earth Observation methods for this task particularly attractive. Until today, there is no sophisticated global melt pond data set available: Accurate methods may exist for determining melt ponds from Sentinel-2 data. The downside of using Sentinel-2 is that parts of the High Arctic are not covered by this mission. MODIS data covers the whole globe at least once every three days, but the downside of it is that MODIS resolution is much coarser (250m vs. 10m). Since melt ponds are in general much smaller than 250m, it means that accurately capturing melt pond fraction from these data is difficult. We propose to address these issues by employing Deep Learning techniques. Namely, we use Sentinel-2 data to train a model to super-resolve MODIS images to higher resolution and to use all available MODIS bands and their surrounding pixels for information context when predicting melt pond and open water fractions. In addition, a thorough uncertainty quantification (UQ) will be applied by using the UQ Toolbox.