Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced surface classification in sea ice

In our research, we leverage the capabilities of the Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018, respectively, to deepen our understanding of the polar regions. These satellites offer a unique blend of high-resolution Ku-band radar altimetry data, synthetic aperture radar (SAR) mode altimetry, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer. This combination enables the acquisition of both optical imagery and SAR radar altimetry data, extending up to 81 degrees North. Central to our study is the application of deep learning techniques, specifically the Vision Transformers (ViT), which adapt the Transformer algorithm for surface classification in polar environments. This approach is instrumental in distinguishing between sea ice and leads, demonstrating robust performance across various metrics, including accuracy and model roll-out on comprehensive OLCI image datasets. We produce our first lead classification maps at the original OLCI swath level resolution of 300m and a lead fraction prototype mosaic spring pan-Arctic product at gridded level of 1km, 5km and 10km resolution and on daily, weekly and monthly timescales. The use of binned statistics in conjunction with our deep learning classifications provides valuable insights into the spatial distribution and changes of leads within the polar ice. We compare our prototype product with other existing lead products and with auxiliary datasets on thin ice (roughness, thickness). Our work combining different satellite products at pan-Arctic intermediate resolution enhances our capacity to estimate sea ice thickness and aids in forecasting future changes in the Arctic and Antarctic regions, thereby contributing to the field of polar remote sensing with direct applications to the future polar missions CRISTAL and CMIR.