Linking winter and spring thermodynamic sea-ice states at critical scales using an object-based image analysis of Sentinel-1.

Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction f p has been shown to be correlated with the September minimum ice extent due to its impact on ice albedo and heat uptake. Ice forecasts should benefit from knowledge of f p as melt ponds form several months in advance of ice retreat. This study goes further back by examining the potential to predict f p during winter using backscatter data from the commonly available Sentinel-1 synthetic aperture radar. An object-based image analysis links the winter and spring thermodynamic states of first-year and multiyear sea-ice types. Strong correlations between winter backscatter and spring f p, detected from high-resolution visible to near infrared imagery, are observed, and models for the retrieval of f p from Sentinel-1 data are provided (r 2 ≥ 0.72). The models utilize HH polarization channel backscatter that is routinely acquired over the Arctic from the two-satellite Sentinel-1 constellation mission, as well as other past, current and future SAR missions operating in the same C-band frequency. Predicted f p is generally representative of major ice types first-year ice and multiyear ice during the stage in seasonal melt pond evolution where f p is closely related to spatial variations in ice topography. [ABSTRACT FROM AUTHOR]