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http://dx.doi.org/10.7780/kjrs.2021.37.6.1.2

Application of DINEOF to Reconstruct the Missing Data from GOCI Chlorophyll-a  

Hwang, Do-Hyun (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology)
Jung, Hahn Chul (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology)
Ahn, Jae-Hyun (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology)
Choi, Jong-Kuk (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_1, 2021 , pp. 1507-1515 More about this Journal
Abstract
If chlorophyll-a is estimated through ocean color remote sensing, it is able to understand the global distribution of phytoplankton and primary production. However, there are missing data in the ocean color observed from the satellites due to the clouds or weather conditions. In thisstudy, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product wasreconstructed by using DINEOF (Data INterpolation Empirical Orthogonal Functions). DINEOF reconstructs the missing data based on spatio-temporal data, and the accuracy was cross-verified by removing a part of the GOCI chlorophyll-a image and comparing it with the reconstructed image. In the study area, the optimal EOF (Empirical Orthogonal Functions) mode for DINEOF wasin 10-13. The temporal and spatialreconstructed data reflected the increasing chlorophyll-a concentration in the afternoon, and the noise of outliers was filtered. Therefore, it is expected that DINEOF is useful to reconstruct the missing images, also it is considered that it is able to use as basic data for monitoring the ocean environment.
Keywords
Ocean color; GOCI (Geostationary Ocean Color Imager); Chlorophyll-a; DINEOF (Data INterpolation Empirical Orthogonal Functions); EOF (Empirical Orthogonal Functions);
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Times Cited By KSCI : 1  (Citation Analysis)
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