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Spatial and temporal characteristics and removal methodology of suspended particulate matter speckles from Geostationary Ocean Color Imager data
- Lee, Min-Sun, Park, Kyung-Ae, Moon, Jeong-Eon, Kim, Wonkook, Park, Young-Je
- International journal of remote sensing 2019 v.40 no.10 pp. 3808-3834
- color, particulates, reflectance, remote sensing, temporal variation, water vapor
- Reviewing six years of Geostationary Ocean Color Imager (GOCI) suspended particulate matter (SPM) concentration images from 2011 to 2016 revealed unexpected and some enormously high or low values. These speckles are randomly scattered throughout the entire study area or congregated at a certain part, which has strongly restricted the scientific applications of GOCI data thus far. They can be classified into four types: isolated, near-cloud, patch-type, and slot-related speckles, based on spatial distribution and potential causes. These types are investigated. The speckles are induced by a moving-cloud during a complete observation of 8 bands for each slot of GOCI, partly by an incomplete cloud masking procedure near cloud edges, by relatively low reflectance of pixels corresponding to a cloud shadow, by imperfect atmospheric corrections related to water vapor after cloud passage, or by sensor-related troubles related to the effects of stray light. Spatial and temporal variabilities of the identified speckles are investigated and used to develop a methodology for their removal. The SPM concentration values of the error-free pixels, passing through the post-processing of the speckle removal procedure, are compared to those previously with speckles. As a result, the enormously large values of SPM, occupying 6.06% of the pixel numbers, are all eliminated. Typical seasonality of SPM, unrevealed using the speckled images, is clearly presented through the removal procedure. This study addresses the importance of preprocessing for speckle error in SPM concentrations from GOCI data and implies more reliable SPM data without speckles can be used in scientific application research.