IceMap250Automatic 250 m Sea Ice Extent Mapping Using MODIS Data

被引:20
作者
Gignac, Charles [1 ,2 ]
Bernier, Monique [1 ,2 ]
Chokmani, Karem [1 ,2 ]
Poulin, Jimmy [1 ,2 ]
机构
[1] Inst Natl Rech Sci, Ctr Eau Terre Environm, 490 Rue Couronne, Quebec City, PQ G1K 9A9, Canada
[2] Univ Laval, Ctr Etudes Nord, Pavillon Abitibi Price,2405 Rue Terrasse, Quebec City, PQ G1V 0A6, Canada
关键词
sea ice; Hudson Bay; algorithm; MODIS; downscaling; Arctic; mapping; PASSIVE MICROWAVE; SURFACE-TEMPERATURE; MELT PONDS; CLEAR-SKY; RESOLUTION; PRODUCT; COVER; CLOUD; MODEL; SNOW;
D O I
10.3390/rs9010070
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:24
相关论文
共 59 条
[51]   Sea ice remote sensing using AMSR-E 89-GHz channels [J].
Spreen, G. ;
Kaleschke, L. ;
Heygster, G. .
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2008, 113 (C2)
[52]   Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea [J].
Su, Hua ;
Wang, Yunpeng ;
Xiao, Jie ;
Li, Lili .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2013, 85 :13-20
[53]   Monitoring the Spatiotemporal Evolution of Sea Ice in the Bohai Sea in the 2009-2010 Winter Combining MODIS and Meteorological Data [J].
Su, Hua ;
Wang, Yunpeng ;
Yang, Jingxue .
ESTUARIES AND COASTS, 2012, 35 (01) :281-291
[54]  
Trishchenko A.P., 2008, COLD REGION ATMOSPHE, P327
[55]  
Trishchenko A.P., 2006, P SPIE
[56]   Derivation of melt pond coverage on Arctic sea ice using MODIS observations [J].
Tschudi, Mark A. ;
Maslanik, James A. ;
Perovich, Donald K. .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (05) :2605-2614
[57]   A global, self-consistent, hierarchical, high-resolution shoreline database [J].
Wessel, P ;
Smith, WHF .
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 1996, 101 (B4) :8741-8743
[58]   Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a Normalized Difference Snow and Ice Index [J].
Xiao, XM ;
Shen, ZX ;
Qin, XG .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2001, 22 (13) :2479-2487
[59]   SAR sea-ice image analysis based on iterative region growing using semantics [J].
Yu, Qiyao ;
Clausi, David A. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2007, 45 (12) :3919-3931