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Abstract

Opaque masses (e.g., cloud and haze) are the main obstacles interrupting remote observations of ocean color using optical sensors. We performed a statistical analysis for 1 year of ocean color data derived from the Geostationary Ocean Color Imager (GOCI), which performs eight observations per day. We discovered that the valid ranges of the data vary depending on the local times and the seasonal characteristics and are related to the pattern of solar altitudes to a certain degree. Here, it is shown that multiple ocean color scenes observed on a given day can be merged to recover the contaminated areas. However, merging multiple ocean color scenes from heterogeneous sensors (e.g., MODIS, SeaWiFS, and MERIS) for a given day takes considerable effort. In contrast, multiple scenes from a single sensor such as GOCI can be merged with a relatively simple approach such as averaging. Here, we focus on how much unavailable data can be recovered quantitatively in a given day by merging multiple scenes from GOCI. To this end, a large data set composed of GOCI scenes from January 2012 to December 2012 was used. The results demonstrate that ocean color availability in a composite scene merged from eight multiple GOCI scenes could be expanded by about 2.54 times relative to a single scene.

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