Abstract Melt ponds on sea ice play an important role in the seasonal evolution of the summer ice cover. In this study we present two machine learning algorithms, one (multi-layer neural network) for the retrieval of melt pond binary classification and another (multinomial logistic regression) for melt pond fraction using moderate resolution visible satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). To minimize the impact of the anisotropic reflectance characteristics of sea ice and melt ponds, normalized MODIS band reflectance differences from top-of-the-atmosphere (TOA) measured reflectances were used. The training samples for the machine learning were based on MODIS reflectances extracted for sea ice, melt ponds and open water classifications based on high resolution (~2 m) WorldView (WV) data. The accuracy assessment for melt pond binary classification and fraction is further evaluated against WV imagery, showing mean overall accuracy (85.5%), average mean difference (0.09), and mean RMSE (0.18). In addition to cross-validation with WV, retrieved melt pond data are validated against melt pond fractions from satellite and ship-based observations, showing average mean differences (MD), root-mean-square-error (RMSE), and correlation coefficients (R) of 0.05, 0.12, and 0.41, respectively. We further investigate a case study of the spectral characteristics of melt ponds and ice during refreezing, and demonstrate an approach to mask out refrozen pixels by using yearly maps of melt onset and freeze-up data together with ice surface temperatures (IST). Finally, an example of monthly mean pan-Arctic melt pond binary classification and fraction are shown for July 2001, 2004, 2007, 2010, 2013, 2016, and 2019. Bulk processing of the entire 20 years of MODIS data will provide the science community with a much needed pan-Arctic melt pond data set.

中文翻译：

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从可见卫星图像中检索泛北极融水池的机器学习方法

摘要 海冰上的融水池在夏季冰盖的季节演变中起着重要作用。在这项研究中，我们提出了两种机器学习算法，一种（多层神经网络）用于检索熔池二元分类，另一种（多项逻辑回归）使用来自中等分辨率成像光谱仪的中等分辨率可见卫星图像来检索熔池部分（莫迪斯）。为了尽量减少海冰和融化池各向异性反射特性的影响，使用了与大气顶部 (TOA) 测量反射率的归一化 MODIS 波段反射率差异。机器学习的训练样本基于为海冰、融化池和开放水域分类提取的 MODIS 反射率，这些数据基于高分辨率 (~2 m) WorldView (WV) 数据。针对 WV 图像进一步评估了熔池二元分类和分数的准确性评估，显示平均总体准确性 (85.5%)、平均平均差异 (0.09) 和平均 RMSE (0.18)。除了与 WV 进行交叉验证外，还针对来自卫星和船舶观测的熔池分数对检索到的熔池数据进行了验证，显示了平均差值 (MD)、均方根误差 (RMSE) 和相关系数 ( R) 分别为 0.05、0.12 和 0.41。我们进一步研究了重新冻结过程中融化池和冰的光谱特征的案例研究，并展示了一种通过使用融化开始和冻结数据的年度地图以及冰面温度 (IST) 来掩盖重新冻结像素的方法。最后，