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

Abstract

Melt 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. Plain Language Summary Melt ponds are pools of open water that form on the sea ice surface in the Arctic in summer months. Sea ice covered by melt ponds absorbs more solar heat than solid ice which speeds up the rate of sea ice melt. To study this process, we need melt pond coverage data that are generally obtained from satellite observations. There are several methods to calculate the melt pond fraction from high-resolution optical satellite imagery but these methods assume that melt ponds have constant optical properties, which is not true. In this study, we show that these existing methods significantly underestimate melt pond fraction. To address this problem, we present a new method that treats melt ponds as features having variable optical properties. Images of sea ice from aircraft are used to assess the new algorithm and two previous methods, demonstrating that the new algorithm has higher accuracy and precision.

Melt Pond Fraction Derived from Sentinel-2 Data: Along the MOSAiC Drift and Arctic-wide

“…The LinearPolar Algorithm by Wang et al (2020) was developed for small subsets (less than 2 km edge length) of Sentinel-2 scenes to extract MPF from the optical imagery. We adopt the fundamental approach and introduce changes to make the algorithm applicable to larger subsets (larger than 50 km length) and a wider variability of scenes.…”

“…The algorithm is based on the bands 2 (B2 ) and 8 (B8 ) of the Sentinel-2 instrument with central wavelengths of 490 nm and 842 nm, respectively. This is because of the significant difference between the spectral behavior of ice, melt pond and open water surfaces at these wavelengths (Rösel et al, 2012;Wang et al, 2020).…”

“…There are numerous efforts to enhance the understanding of melt pond physics based on in-situ (Eicken et al, 2002;Light et al, 2008;Nicolaus et al, 2012), air-borne (D. Perovich et al, 2002;Miao et al, 2015;Buckley et al, 2020), and high resolution (O(m)) satellite measurements (Markus et al, 2002;Rösel & Kaleschke, 2011;Istomina et al, 2015b;Li et al, 2020;Wang et al, 2020). Due to the limited availability of observational data, the available studies focus on case studies and are often used for validation purposes of medium and low resolution satellite observations, which cover larger areas and longer time periods (Rösel et al, 2012;Zege et al, 2015;Lee et al, 2020;Wright & Polashenski, 2020;Peng et al, 2022).…”

“…Due to the limited availability of observational data, the available studies focus on case studies and are often used for validation purposes of medium and low resolution satellite observations, which cover larger areas and longer time periods (Rösel et al, 2012;Zege et al, 2015;Lee et al, 2020;Wright & Polashenski, 2020;Peng et al, 2022). Wang et al (2020) have developed an algorithm to extract MPF from small subsets of optical satellite measurements from the Copernicus Sentinel-2 mission.…”

Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice

“…Most recent measurements on melt ponds concentrated on the horizontal scale, such as melt pond fraction and size [4][5][6], whereas with the development of numerical models, more and more studies demonstrated that H p and H i are the key factors that affect the optical properties of melt pond. For example, Lu et al [7] confirmed that the melt pond albedo increases mainly with H i for thin ice (H i < 1 m), while it depends on H p and H i for ice thickening, and finally chiefly on H p for H i > 3 m. The transmittance was dependent only on H i .…”