Spatial Temporal Compensation for Sea Ice Classification From GNSS-R Data

This study proposes the use of a per-track normalization scheme and ancillary temporal and temperature data to improve the performance of global navigation satellite system reflectometry (GNSS-R)-based sea ice classification. GNSS-R data and sea ice-type labels were provided by TechDemoSat-1 (TDS-1) and National Snow and Ice Data Center (NSIDC) datasets, respectively. The delay Doppler maps (DDMs) from TDS-1 were used to compute DDM average (DDMA) observable values to which the proposed per-track normalization scheme was applied. The transformed observables with ancillary time and temperature data were provided to a random forest (RF) machine learning classifier to perform the final sea ice classifications. The purpose of this proposed method was to standardize power measurements across different TDS-1 tracks and to compensate for seasonal changes in sea ice that complicate sea ice classification. When using TDS-1 data within the temporal and geographic scope of this study, the method reported a testing accuracy of 84.86% and per-class $F1$ -scores of 90.11%, 64.20%, and 81.59% for first-year ice (FYI), multiyear ice (MYI), and thin ice (TI), respectively. The proposed method was compared with existing methods of GNSS-R-based sea ice classification and demonstrated improved or comparable performance. The proposed method shows promise, as it provides good performance while demonstrating generalizability to geographic and temporal data selection, and requires less data to train to a level comparable to established methods.