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

Do Hyun Hwang, Hahn Chul Jung, Jae Hyun Ahn, Jong Kuk Choi

Research output: Contribution to journalArticlepeer-review


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 this study, the missing data of the GOCI (Geostationary Ocean Color Imager) chlorophyll-a product was reconstructed 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 was in 10-13. The temporal and spatial reconstructed 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.

Original languageEnglish
Pages (from-to)1507-1515
Number of pages9
JournalKorean Journal of Remote Sensing
Issue number1-6
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors.

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)
  • Engineering (miscellaneous)


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