Reconstruction of geostationary satellite ocean color data based on DINEOF

doi:10.3969/j.issn.1001-909X.2019.04.002

Abstract

Abstract: Geostationary Ocean Color Imager (GOCI) provides ocean water color data with time resolutions up to the hour, making it possible to monitor the marine environment in a time-varying manner. However, due to the influence of clouds, fog and haze over the ocean, the continuous high-missing rate or even complete loss of data makes the data use value greatly reduced. Based on the Data INterpolating Empirical Orthogonal Functions, highlighting the position of time elements in reconstruction, a reconstruction method using abnormal pixel detection, Laplacian smoothing and temporal coefficient twice decomposition interpolation was proposed for geostationary satellite ocean color data (DINEOF-G). This method is used to reconstruct the total suspended matter data of Hangzhou Bay in 2017. The results show that the proposed method improves the reconstruction accuracy by 8% and the data reconstruction rate by 36% compared with the classical method. The reconstructed data reflects the seasonal variation and spatial distribution characteristics of the total suspended matter in Hangzhou Bay.

Key words: data reconstruction, DINEOF, GOCI, Hangzhou Bay, total suspended matter

CLC Number:

TP75

CHEN Yi-jun, ZHANG Feng, DU Zhen-hong, LIU Ren-yi. Reconstruction of geostationary satellite ocean color data based on DINEOF[J]. Journal of Marine Sciences, 2019, 37(4): 14-23.

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