| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 6347242 | Remote Sensing of Environment | 2013 | 15 Pages |
Abstract
⺠This Work describes a method able to fill data gaps in satellite chlorophyll (CHL) images. ⺠It is based on the principle of Self Organizing Maps (SOM) classification methods. ⺠It relies on the assumption that an ocean state is locally defined by its values of SST, SSH and CHL. ⺠A codebook of possible (SST, SSH, CHL) situations, if large enough, allows the reconstruction of incomplete situations of CHL. ⺠The results proved its efficiency to reproduce CHL patterns in cases of extreme cloud cover (100%).
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Manel Jouini, Marina Lévy, Michel Crépon, Sylvie Thiria,