A novel sea ice floe fragmentation index using Sentinel-2 and AMSR2 satellite data based on machine learning

• Floe fragmentation index (FFI) quantifies the degree of sea ice floe fragmentation. • FFI revealed floe structural variation even at the same sea ice concentration (SIC). • FFI enables daily floe fragmentation monitoring via microwave and machine learning. • FFI proacted clearly to sea ice loss, even when SIC remained stable. • Rapid increase in FFI was consistently followed by a sharp decline in SIC. Sea ice indices such as sea ice concentration (SIC) play a key role in monitoring climate change. However, it does not fully capture the vulnerability of sea ice to melting, especially under conditions of floe fragmentation. To address this limitation, we introduce a novel metric—the floe fragmentation index (FFI)—designed to quantify the degree of fragmentation of sea ice. We constructed the FFI reference map using high-resolution Sentinel-2 imagery based on k-means clustering and manual editing. This reference was then paired with AMSR2 passive microwave data to train three machine learning models—gradient boosting (GB), random forest (RF), and support vector regression (SVR)—enabling consistent, daily mapping of FFI across the Arctic. FFI increases as sea ice becomes more fragmented. Even under identical SIC conditions (e.g., 50%), the reference FFI captured distinct differences in floe structure, demonstrating its ability to represent fragmentation more explicitly than SIC. In comparison with the reference FFI derived from Sentinel-2, the gradient boosting model demonstrated the best performance, with an R 2 exceeding 0.94 and a root mean square error (RMSE) below 0.22. Since RMSE was computed against the reference FFI, it is expressed in the same unit as FFI, which is dimensionless. To examine differences between SIC and FFI under relatively stable sea ice conditions, we focused on the Laptev Sea—a region where import and export of sea ice are minimal in summer. At the point of sea ice disappearance within this area, no early signs were observed in SIC, whereas FFI did reveal such signals. In particular, under conditions where the sea ice was highly fragmented and thus more likely to drift away or melt, SIC remained close to 100%, while FFI captured the relatively severe fragmentation of the sea ice. The time-series comparison between the FFI and SIC revealed that a rapid increase in FFI, highly exceeding its seasonal tendency, was followed by or occurred simultaneously with a rapid decrease in SIC, which also exceeded twice the magnitude of its seasonal tendency. Ultimately, the FFI complemented SIC by capturing fragmentation changes, potentially allowing earlier detection of SIC change in the melting seasons.

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