Next Article in Journal
Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record
Previous Article in Journal
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Open AccessArticle

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

1
Institut National de la Recherche Scientifique—Centre Eau Terre Environnement, 490 rue de la Couronne, Quebec, QC G1K 9A9, Canada
2
Centre d’Etudes Nordiques, Universite Laval, Pavillon Abitibi-Price, 2405 rue de la Terrasse, Local 1202, Quebec, QC G1V 0A6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Remote Sens. 2017, 9(1), 70; https://doi.org/10.3390/rs9010070
Received: 24 October 2016 / Revised: 4 January 2017 / Accepted: 9 January 2017 / Published: 13 January 2017
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. View Full-Text
Keywords: sea ice; Hudson Bay; algorithm; MODIS; downscaling; Arctic; mapping sea ice; Hudson Bay; algorithm; MODIS; downscaling; Arctic; mapping
Show Figures

Graphical abstract

MDPI and ACS Style

Gignac, C.; Bernier, M.; Chokmani, K.; Poulin, J. IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data. Remote Sens. 2017, 9, 70. https://doi.org/10.3390/rs9010070

AMA Style

Gignac C, Bernier M, Chokmani K, Poulin J. IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data. Remote Sensing. 2017; 9(1):70. https://doi.org/10.3390/rs9010070

Chicago/Turabian Style

Gignac, Charles; Bernier, Monique; Chokmani, Karem; Poulin, Jimmy. 2017. "IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data" Remote Sens. 9, no. 1: 70. https://doi.org/10.3390/rs9010070

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop