A New Algorithm for Sea Ice Melt Pond Fraction Estimation From High‐Resolution Optical Satellite Imagery

Abstract

AbstractMelt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high‐resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel‐2 imagery to demonstrate those previous algorithms assuming fixed melt pond‐reflectance greatly underestimate MPF. We propose a new algorithm (“LinearPolar”) based on the polar coordinate transformation that treats melt ponds as variable‐reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate θ of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel‐2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms.