An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery

Abstract. The decline in Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather and climate in the Arctic and beyond. The coverage of Arctic sea ice (i.e. sea ice concentration (SIC)) has been monitored since 1972 using satellite passive microwave (PMW) measurements because of their extensive coverage and all-weather capability. However, the fundamental basis of algorithms for estimating SIC has not improved much since the early days due to the lack of reference SIC data, leading to discrepancies between existing PMW SIC algorithms. To overcome this issue, this study aims to construct data records of reference SIC over Arctic sea ice using 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. In order to collect relatively bright and clear scenes, thresholds of solar elevation >15° and cloud cover <10 % were applied in this study. Clouds in each Landsat-8 scene were masked using the cloud-masking array provided in Landsat data. Due to the poor accuracy of the cloud-masking array over ice-covered surface types, an additional step of visually inspecting the state of the cloud mask using the true-colour image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of the cloud mask. The normalized difference snow index and OLI band-5 reflectivity were used to differentiate between ice and open water within each selected Landsat-8 pixel. The classified data were projected onto a 6.25 km polar stereographic grid, and SIC for each grid cell was obtained by counting ice-classified pixels within the grid. SIC was only computed for grid cells where more than 99 % of their area was covered with Landsat-8 pixels to limit the uncertainty in SIC arising from grids that are not fully concentrated with Landsat-8 pixels. Uncertainty in the produced SIC was 1 %–4 %, inferred using the Gaussian error propagation method. Out of 15 286 collected Landsat-8 images, 14 297 images were translated into SIC maps, and a total of 2 934 399 Landsat-8 SIC grid cells were generated. Evaluation of Landsat-8 SIC with SIC from ice charts revealed a good linear relationship (correlation coefficient of 0.96) between the two products, as well as a mean negative bias which fell within the uncertainty range of Landsat-8 SIC. SIC based on Landsat-8 can be used as reference SIC to evaluate existing SIC products, and, thus, one can improve SIC products, as well as use the improved SIC for other applications such as data assimilation and retrieval studies. The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://doi.org/10.5281/zenodo.10973297 (Jung et al., 2024), and the Python codes for the production and evaluation of the Landsat-8 SIC dataset are accessible at https://doi.org/10.5281/zenodo.12754602 (Jung, 2024).