Retrieval of suspended sediment concentration in the coastal waters of yellow Sea from Geostationary Ocean Color Imager (GOCI)

Title
Retrieval of suspended sediment concentration in the coastal waters of yellow Sea from Geostationary Ocean Color Imager (GOCI)
Author(s)
민지은; 최종국; 박영제; 유주형
KIOST Author(s)
Choi, Jong Kuk(최종국)Park, Young Je(박영제)Ryu, Joo Hyung(유주형)
Publication Year
2013-07-04
Abstract
In this study we developed two different types of SSC algorithms, one is the empirical algorithm and the other one is the semi-analytical model based algorithm. For the algorithm development, 197 in situ measurements for chlorophyll, SS concentration, surface remote sensing reflectance (Rrs) and IOPs were obtained on the coastal waters of Yellow Sea from 2011 to 2012. To validate SSC algorithms, we used Geostationary Ocean Color Imager (GOCI) which was captured around the time of field sampling. GOCI, the world’s first geostationary ocean color observation satellite, can obtain data at every hour during the daytime. Owing to this advantage of GOCI, we took much more matching data than polar orbit sensors such as SeaWiFS and MODIS. Atmospheric correction of GOCI was carried out using MUMM method. To verify the accuracy for the newly developed SSC algorithms in this study, we also examined existing GOCI standard SS algorithm, YOC algorithm and single-band exponential format algorithm for comparison. The results from new SSC algorithm based on the semi-analytical model showed good correlation with the in situ SSC. But in the area of extremely high SSC (> 100 g/m3) GOCI-derived SSC was underestimated. In the future we will continuously enhance the semi-analytical model based SSC algorithm using much more GOCI-matching dataset.centration, surface remote sensing reflectance (Rrs) and IOPs were obtained on the coastal waters of Yellow Sea from 2011 to 2012. To validate SSC algorithms, we used Geostationary Ocean Color Imager (GOCI) which was captured around the time of field sampling. GOCI, the world’s first geostationary ocean color observation satellite, can obtain data at every hour during the daytime. Owing to this advantage of GOCI, we took much more matching data than polar orbit sensors such as SeaWiFS and MODIS. Atmospheric correction of GOCI was carried out using MUMM method. To verify the accuracy for the newly developed SSC algorithms in this study, we also examined existing GOCI standard SS algorithm, YOC algorithm and single-band exponential format algorithm for comparison. The results from new SSC algorithm based on the semi-analytical model showed good correlation with the in situ SSC. But in the area of extremely high SSC (> 100 g/m3) GOCI-derived SSC was underestimated. In the future we will continuously enhance the semi-analytical model based SSC algorithm using much more GOCI-matching dataset.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/26872
Bibliographic Citation
Inernatinoal Symposium on Remote Sensing 2013, pp.809 - 812, 2013
Publisher
Korean
Type
Conference
Language
English
Publisher
Korean
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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