We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Missing Pattern Analysis of the GOCI-I Optical Satellite Image Data.
- Authors
Ho-Kun Jeon; Hong Yeon Cho
- Abstract
Data missing in optical satellite images caused by natural variations have been a crucial barrier in observing the status of marine surfaces. Although there have been many attempts to fill the gaps of nonobservation, there is little research to analyze the ratio of missing grids to overall sea grids and their seasonal patterns. This report introduces the method of quantifying the distribution of missing points and then shows how the missing points have spatial correlation and seasonal trends. Both temporal and spatial integration methods are compared to assess the effectiveness of reducing missing data. The temporal integration shows more outstanding performance than the spatial integration. Moran's I and K-function with statistical hypothesis testing show that missing grids are clustered and there is a non-random distribution from daily integration. The result of the seasonality test for Moran's I through a periodogram shows dependency on full-year, half-year, and quarter-year periods respectively. These analysis results can be used to deduce appropriate integration periods with permissible estimation errors.
- Subjects
OPTICAL images; STATISTICAL hypothesis testing; TEMPORAL integration; LANDSAT satellites; REMOTE-sensing images; SPATIAL distribution (Quantum optics)
- Publication
Ocean & Polar Research, 2022, Vol 44, Issue 2, p179
- ISSN
1598-141X
- Publication type
Article
- DOI
10.4217/OPR.2022009