Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables
[Date:2011-03-22]

Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables
 
Wei Yanga,b, Bunkei Matsushitab*, Jin Chena , Takehiko Fukushimab
 
a State key laboratory of earth surface processes and resource ecology, Beijing Normal University,Beijing, 100875, China
b Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai,Tsukuba, Ibaraki, 305-8572, Japan
 
Abstract: Remote estimation of water constituent concentrations in case II waters has been a great challenge, primarily due to the complex interactions among the phytoplankton, tripton, colored dissolved organic matter (CDOM) and pure water. Semi-analytical algorithms for estimating constituent concentrations are effective and easy to implement, but two challenges remain. First, a dataset without a sampling bias is needed to calibrate estimation models; and second, the semi-analytical indices were developed based on several specific assumptions that may not be universally applicable. In this study, a semi-analytical model-optimizing and look-up-table (SAMO-LUT) method was proposed to address these two challenges. The SAMO-LUT method is based on three previous semi-analytical models to estimate chlorophyll a, tripton and CDOM. Look-up tables and an iterative searching strategy were used to obtain the most appropriate parameters in the models. Three datasets (i.e., noise-free simulation data, in situ data and Medium Resolution Imaging Spectrometer (MERIS)satellite data) were collected to validate the performance of the proposed method. The results show that the SAMO-LUT method yields error-free results for the ideal simulation dataset; and is able also to accurately estimate the water constituent concentrations with an average bias (mean normalized bias, MNB) lower than 9% and relative random uncertainty (normalized root mean square error, NRMS) lower than 34% even for in situ and MERIS data. These results demonstrate the potential of the proposed algorithm to accurately monitor inland and coastal waters based on satellite observations.
Keywords: Semi-analytical models Look-up table Bio-optical model Case II water
 
Published in Remote Sensing of Environment, 2011, 115: 1247–1259.