A satellite-based ice fraction record for small water bodies of the Arctic Coastal Plain

Abstract. Ice cover of water bodies in the northern high latitudes (NHL) is highly sensitive to the changing climate, and its dynamics exert substantial impacts on the NHL ecosystems, hydrological processes, and the carbon cycle. Yet, operational quantification of ice cover dynamics for smaller water bodies (e.g., ≤ 25 km²) over vast, remote NHL regions remains limited. Here, we developed an ice fraction dataset for small water bodies (900 m² to 25 km²) across the Arctic Coastal Plain of Alaska (ACP) from 2017 through 2023, using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, texture features, and Daymet air temperature data. The dataset has a spatial resolution of 1 km and a temporal resolution of approximately 6 days. Compared with the Google Dynamic World (DW) product derived from Sentinel-2 optical remote sensing, our dataset shows high consistency with DW (R = 0.91, RMSE = 0.19) while having enhanced temporal coverage due to less SAR constraints from solar illumination, cloud cover, and atmospheric conditions. Validation against in-situ observations suggests that our dataset is more capable of capturing small water body ice phenology (e.g., freeze-up and break-up dates) relative to DW, with an 11-day reduction in mean absolute error. Our ice fraction dataset reveals high spatial heterogeneity in ice conditions mainly occurring in June for small water bodies across the ACP. The ice phenology analysis over three selected subregions further shows that a warmer transition period generally leads to earlier ice break-up and later freeze-up, while the responses of ice fraction to warming climate vary among and within individual water bodies. The resulting dataset is anticipated to fill a gap in ice phenology studies for small water bodies, improve our understanding on the interactions between ice dynamics and climate change, and enhance the coupled modelling of ice and carbon processes. The S1 ice fraction dataset is publicly available at https://doi.org/10.5281/zenodo.17033546 (Lin et al., 2025)