Objectives To achieve the accurate monitoring of sea ice conditions in the key area of Arctic passage, high resolution sea ice thickness is essential. However, sea ice thickness with kilometer-scale resolution cannot meet the requirement at present.

Methods Considering the relationship between sea ice thickness and image albedo,this paper tried to establish a regression model between the ICESat-2 ATL10 sea ice freeboard product and the high resolution Sentinel-2 optical image albedo by taking advantage of the intensive altimetry data of ICESat-2 along the orbit to obtain dense sea ice freeboard. Based on high resolution sea ice thickness calculated from dense freeboard with the hydrostatic equilibrium model and combined with high resolution sea ice concentration derived from Sentinel-2 image, we classified ice areas into different navigational categories according to the ice-break capability of ship and compared optimal route design at multiple spatial scales.

Results Regression models established from the ATL10 sea ice freeboard and the Sentinel-2 image albedo have good fitting accuracy. The $R^{\mathrm{2}}$ of the regression model is higher than 0.5, the average deviation is less than 0.05 m, and the root mean square error of the accuracy validation is less than 0.2 m. High resolution sea ice parameters derived by the proposed method can describe the distribution of fine leads between floating ice which are difficult to be captured by low resolution sea ice parameters.

Conclusions High resolution sea ice parameters can describe the details of sea ice conditions more accurately. Therefore, the proposed high resolution inversion method of sea ice parameters can improve the capability of navigation planning for ships in Arctic passage, further improving the navigation safety.