How Machine Learning and High‐Resolution Imagery Can Improve Melt Pond Retrieval From MODIS Over Current Spectral Unmixing Techniques

Abstract

AbstractMeltwater that pools on the surface of Arctic sea ice enhances solar absorption and accelerates further ice melt. The impact of melt ponds on energy absorption is controlled primarily through their influence on ice albedo, which is, in turn, governed in large part by the ponds' spatial coverage. This work seeks to observe the spatial coverage of melt ponds across the Arctic basin with sufficient accuracy to investigate pond‐albedo feedback and presents an improved technique to achieve this goal. We approach the problem by using the Open Source Sea Ice Processing algorithm to classify surface features in submeter resolution optical satellite imagery over select sites where such imagery is available. These data establish “true” estimates of pond coverage and the ponds' spectral reflectance. This information is then used to inform, improve, and test spectral unmixing and machine learning techniques that seek to determine melt pond coverage from more widely available, but lower resolution, optical satellite imagery (e.g., Moderate Resolution Imaging Spectroradiometer). The new machine learning approach improves accuracy from prior work and can contribute to improved efforts to validate melt pond models or understand trends in pond coverage. Nevertheless, we encounter and carefully document significant challenges to retrieving melt pond fractions from low‐resolution optical imagery. These limit accuracy to levels below that necessary for resolving climatologically important trends. We conclude that greatly expanding the collection of high‐resolution satellite imagery over sea ice is necessary to monitor melt pond coverage with the accuracy needed by the scientific community.