Estimating constituent concentrations in case II waters from MERIS satellite data by semi-analytical model optimizing and look-up tables

REMOTE SENSING OF ENVIRONMENT

卷:115

Article

Yang, Wei^1,2  Matsushita, Bunkei²  Chen, Jin¹  Fukushima, Takehiko² 

[1] Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China

[2] Univ Tsukuba, Grad Sch Life & Environm Sci, Tsukuba, Ibaraki 3058572, Japan

关键词: Semi-analytical models;    Look-up table;    Bio-optical model;    Case II water;   

DOI  :  10.1016/j.rse.2011.01.007

来源: Elsevier

【 摘 要 】

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. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

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