An Arctic sea ice multi-step classification based on GNSS-R data from the TDS-1 mission

Abstract

Abstract This study examines the potential of using bistatic radar reflections from the Global Navigation Satellite System (GNSS) to classify sea ice types in the Arctic Ocean during the sea ice formation period. For this study, we used data obtained from the United Kingdom (UK) TechDemoSat-1 (TDS-1) satellite mission. The main objective of the TDS-1 mission is to provide L-band radar reflections of the ocean surface in order to infer wind speed through an estimation of the ocean surface roughness. Given the orbit inclination of TDS-1, polar coverage is obtained providing datasets over the Arctic sea ice cover. Recent studies demonstrated the use of TDS-1 data for accurately deriving ocean-ice detection using the shape of the bistatic radar waveforms derived from delay-Doppler maps. We aim to advance this previous research by demonstrating the sensitivity of GNSS bistatic radar signals to sea ice types. The originality of the study presented in this manuscript is the classification of sea ice types, never implemented before with any GNSS-R mission. For this study, we examined the fall period of October 2015 in the Beaufort and Chukchi seas region, when considerable expanses of young ice, first-year ice (FYI), and multi-year ice (MYI) were present. We developed a sea ice multi-step classification approach based on bistatic radar reflections to generate not only an ocean-ice classification but to classify between the three dominant ice types present. The classification was done in multiple steps based on GNSS bistatic radar observations only and includes checks for spatio-temporal consistency with each dominant class. Validation results are based on the comparison of the GNSS classification against SAR-derived sea ice type maps produced at the US National Ice Center. First, we derived classifications of sea ice–open water samples with a success rate of 97%, comparable to previous studies. Next, the sea ice type classification results identified FYI, MYI and young ice with success rates of 70%, 82% and 81%, respectively, indicating a strong sensitivity of the L-band GNSS bistatic radar signals to the different surface scattering properties of these primary ice types. With this manuscript we demonstrate the potential of the GNSS bistatic radar signals to classify ice types during the sea ice formation period. GNSS bistatic radar signals provide unique forward scattered measurements at L-band, potentially on a daily temporal revisit, that can be used to produce enhanced information on sea ice characteristics, such as those currently generated by other sensors. In order to determine the use of GNSS-R measurements on an operational basis, further studies are needed over a year-long period of sea ice growth and melt to determine the full benefit of GNSS for improving and providing complementary information to that generated by both microwave and optical sensors.