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

Abstract. The decline of Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather/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) onboard 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 cloud mask using the true-color image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of cloud mask. 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 with more than 99 % of its area 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 the use of 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 estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://zenodo.org/doi/10.5281/zenodo.10973297 (Jung et al., 2024), and the python codes for production and evaluation of the Landsat-8 SIC dataset is accessible at https://zenodo.org/doi/10.5281/zenodo.12754603 (Jung, 2024).