Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

Abstract

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.002). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces.

Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide

“…This temporal evolution is in good agreement with a comparison of different pan‐Arctic melt pond products performed by Peng et al. (2022). Note that the variability in our time series partly results from combining observations from different years and regions with different conditions (atmosphere, surface, and ice) which influence pond formation significantly (e.g., Li et al., 2020; Liu et al., 2015).…”

“…There are numerous efforts to advance the understanding of melt pond physics based on in‐situ (e.g., Light et al., 2008; Nicolaus et al., 2012), airborne (e.g., Buckley et al., 2020; Miao et al., 2015), and high resolution ($$\mathcal{O}$$(m)) satellite measurements (e.g., Istomina, Heygster, Huntemann, Marks, et al., 2015; Markus et al., 2002). Due to the limited availability of observational data, most research is focused on case studies and is often used for validation purposes of medium‐resolution and low‐resolution satellite observations, which cover larger areas and longer time periods (e.g., Peng et al., 2022; Zege et al., 2015).…”

“…validation purposes of medium-resolution and low-resolution satellite observations, which cover larger areas and longer time periods (e.g., Peng et al, 2022;Zege et al, 2015).…”

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

“…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). Wang et al (2020) have developed an algorithm to extract MPF from small subsets of optical satellite measurements from the Copernicus Sentinel-2 mission.…”