Remote estimation of Secchi depth using GOCI data

Title
Remote estimation of Secchi depth using GOCI data
Author(s)
김원국
Publication Year
2017-04-17
Abstract
This study provides estimation of Secchi disk depth using GOCI data, and the validation results derived from in-situ measurements of Secchi depth. For the estimation, the input parameters inherent optical properties (IOP) is derived using quasi-analytical algorithm (QAA) based on the Doron’s classic algorithm. The validation results show that the mean normalized gross error is around 30% with mean normalized bias around 10%, when estimated by GOCI.uasi-analytical algorithm (QAA) based on the Doron’s classic algorithm. The validation results show that the mean normalized gross error is around 30% with mean normalized bias around 10%, when estimated by GOCI.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24157
Bibliographic Citation
Pacific-Asian Marginal Seas (PAMS) Meeting, pp.1, 2017
Publisher
PAMS
Type
Conference
Language
English
Publisher
PAMS
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