Enhanced neural network classification for Arctic summer sea ice

Abstract

Lead/floe discrimination is essential for calculating sea ice freeboard and thickness (SIT) from radar altimetry. During the summer months (May–September) the classification is complicated by the presence of melt ponds. In this study, we develop a neural network to classify CryoSat-2 measurements during the summer months, building on the work by Dawson et al. (2022) with various improvements: (i) we expand the training dataset and make it more geographically and seasonally diverse, (ii) we introduce an additional radar detectable class for thinned floes, (iii) we design a deeper neural network and train it longer and (iv) we update the input parameters to data from the latest publicly available CryoSat-2 processing baseline. We show that both the expansion of the training data and the novel architecture increase the classification accuracy. The overall test accuracy improves from 77 ± 5 % to 84 ± 2 % and the lead user accuracy increases from 82 ± 10 % to 88 ± 5 % with the novel classifier. When used for SIT calculation, we observe minor improvements in agreement with the validation data. However, as more leads are detected with the new approach, we achieve better coverage especially in the marginal ice zone. The novel classifier presented here is used for the Summer Sea Ice CryoTEMPO (CryoSat Thematic Product).