Missing Pattern Analysis of the GOCI-I Optical Satellite Image Data

Ho-Kun Jeon; Hong Yeon Cho

doi:10.4217/OPR.2022009

All Issue

2022 Vol.44, Issue 2 Next Page

Note

Missing Pattern Analysis of the GOCI-I Optical Satellite Image Data

30 June 2022. pp. 179-190

PDF XML

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 non- observation, 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.

Keywords

optical satellite

GOCI

missing ratio

spatial pattern

integration scale

References

Akmaev RA (2003) Comment on "Time series, periodograms, and significance" by G. Hernandez. J Geophys Res 108 (A5):1187. doi:10.1029/2002JA009687 10.1029/2002JA009687

Anselin L (1995) Local indicators of spatial association-LISA. Geogr Anal 27(2):93-115. doi:10.1111/j.1538-4632.1995.tb00338.x 10.1111/j.1538-4632.1995.tb00338.x

Baddeley A, Turner R (2005) Spatstat: an R package for analyzing spatial point patterns. J Stat Softw 12(6):1-42. doi:10.18637/jss.v012.i06 10.18637/jss.v012.i06

Cho HY (2020) Periodicity test of the time-series data. KIDS Rep 2(1):8-18

Cliff AD, Ord JK (1969) The problem of spatial autocorrelation. In: Scott A (ed) London papers in regional science. Pion, London, pp 25-55

Fisher RA (1929) Tests of significance in harmonic analysis. P R Soc A 125(796):54-59. doi:10.1098/rspa.1929.0151 10.1098/rspa.1929.0151

Hair Jr. JF, Black WC, Babin BJ, Anderson RE (2010) Multivariate data analysis: a global perspective. Pearson Education, London, 44 p

Hernandez G (1999) Time series, periodograms, and significance. J Geophys Res 104(A5):10355-10368. doi:10.1029/1999JA900026 10.1029/1999JA900026

Jeon H-K, Kim S, Edwin J, Yang C-S (2020) Sea fog identification from GOCI images using CNN transfer learning models. Electronics 9(2):311. doi:10.3390/electronics9020311 10.3390/electronics9020311

Jossart J, Theuerkauf SJ, Wickliffe LC, Morris Jr. JA (2020) Applications of spatial autocorrelation analyses for marine aquaculture siting. Front Mar Sci 6:806. doi:10.3389/fmars.2019.00806 10.3389/fmars.2019.00806

Kim W, Moon JE, Park YJ, Ishizaka J (2016) Evaluation of chlorophyll retrievals from geostationary ocean color imager (GOCI) for the north-east Asian region. Remote Sens Environ 184:482-495. doi:10.1016/j.rse.2016.07.031 10.1016/j.rse.2016.07.031

Mercury M, Green R, Hook S, Oaida B, Wu W, Gunderson A, Chodas M (2012) Global cloud cover for assessment of optical satellite observation opportunities: a HyspIRI case study. Remote Sens Environ 126:62-71. doi:10.1016/ j.rse.2012.08.007 10.1016/j.rse.2012.08.007

Robinson RT, Rosser N, Waters RJ (2019) The spatial and temporal influence of cloud cover on satellite-based emergency mapping of earthquake disasters. Sci Rep 9:12455. doi:10.1038/s41598-019-49008-0 10.1038/s41598-019-49008-031462664PMC6713744

Stock A, Subramaniam A, Van Dijken GL, Wedding LM, Arrigo KR, Mills MM, Micheli F (2020) Comparison of cloud-filling algorithms for marine satellite data. Remote Sensing 12(20):3313. doi:10.3390/rs12203313 10.3390/rs12203313

Weerahandi S (1995) ANOVA under unequal error variances. Biometics 51(2):589-599. doi:10.2307/2532947 10.2307/2532947

Information

Publisher :Korea Institute of Ocean Science and Technology
Publisher(Ko) :한국해양과학기술원
Journal Title :Ocean and Polar Research
Journal Title(Ko) :Ocean and Polar Research
Volume : 44
No :2
Pages :179-190
Received Date : 2022-01-14
Revised Date : 2022-03-15
Accepted Date : 2022-03-29
DOI :https://doi.org/10.4217/OPR.2022009

Ocean and Polar Research ISSN:2234-7313(Online) Ocean and Polar Research

All Issue