A machine learning, multi-band spectral reflectance clustering approach for examining physical transformations in landfast sea ice environments affected by spring freshets

Abstract. Spring freshets account for more than 50 % of the annual terrestrial freshwater discharge into coastal margins in the Alaskan Arctic. Given the usual timing of Arctic freshets, much of this freshwater is discharged into coastal waters that are still covered by landfast sea ice formed the prior winter. This riverine freshwater both floods the sea ice surface and creates freshwater plumes immediately underneath landfast ice. We employed machine learning clustering algorithms to identify and characterize spatial and temporal variability in spring freshet overflows in the Alaskan Arctic, using the Sagavanirktok River as a model system. Multiband imagery from Landsat 8/9 OLI at the mouth of this river during the 2016 spring freshet were examined using the Caliniski-Harabasz method, which identified five unique clusters putatively representing areas of dry ice and snow, wet ice and/or snow, snow-free ice, ice-free open water, and areas of spring freshet overflow. A Gaussian Mixture Model algorithm, used to estimate cluster purity, indicated that the cluster representing freshet overflow is the most distinct from other clusters. Applying these approaches to an unusually comprehensive time-series of ten OLI images from 2022 revealed interesting spatial and temporal dynamics of these clusters as the freshet evolved, including the maximum spatial extent of freshet-flooded ice (271 km²) occurring 2 weeks after peak estimated volumetric discharge, and the persistence of organic material-laden freshwater on top of landfast ice up to 10 km offshore until complete ice loss in early August.