Abstract: Based on multisensory data, including sptical and thermal images from MODIS, and dual-polarization SAR image from RadarSat-2, sea ice in Beaufort Sea is classified, sea ice leads are identified and accuracy of the result is also evaluated. Experiment shows that thermal images from MODIS is only useful in extracting lead area with coarse resolution, while the high-resolution SAR image could provide more information on sea ice type in lead. However, overlay of signals from different ice type largely reduces the overall accuracy from supervised classification of RadarSat-2 image. Therefore, decision tree is established to incorporate multisensory data. Characteristics of different ice types and leads is analyzed and utilized in decision tree to detect ice leads in different stages of development. Validation shows that overall accuracy of the result from decision tree is 14.8% higher than that from supervised classification. The sequential structure composed of various development stages of ice leads is confirmed in Sentinel-2 images. The result might facilitate accurate calculation of heat flux and ice production, as well as ship navigation with detail ice type in lead