IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

• Published in: Remote Sensing
• Main Authors: Charles Gignac, Monique Bernier, Karem Chokmani, Jimmy Poulin
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2017
• Subjects: sea ice; Hudson Bay; algorithm; MODIS; downscaling; Arctic; mapping; Canada; Hudson; Sea ice
• Online Access: https://doi.org/10.3390/rs9010070

The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps.