Improved Arctic Melt Pond Fraction Estimation Using Sentinel-2 Imagery

Abstract. Melt ponds play a vital role in determining the Arctic energy budget by accelerating the rate of sea ice loss aided by their lower albedo. Therefore, an accurate long-term estimate of Arctic Melt Pond Fraction (MPF) is necessary to forecast summer Arctic ice-free conditions. Earth Observation (EO) satellite systems provide ideal tools to monitor the evolution of melt ponds, both spatially and temporally, in near-real time. However, the MPF estimates from these studies are affected by the presence of small, fragmented ice floes called brash ice, and submerged ice. An improved workflow is necessary to remove the effects of the aforementioned sea ice features from the MPF estimate. Here, we estimate MPF using Sentinel-2 imagery, by coupling a Random Forest (RF) model with mathematical morphological algorithms – morphological dilation and morphological reconstruction – which improves the estimate of MPF by reducing misclassifications from nilas, submerged, and brash ice. Further, we present an inter-seasonal MPF time-series from 2018 to 2021 and show that employing morphological operations after the RF reduces the mean MPF by greater than 40 %. Our results show that the MPF exhibited considerable intra- and inter-seasonal variations, with the maximum MPF reaching as high as 57 %.