Estimation of chlorophyll-a concentration in estuarine waters: case study of the Pearl River estuary, South China Sea
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AbstractThe objective of this work is to estimate chlorophyll-a (chl-a) concentration in the Pearl River estuary in China. To test the performance of algorithms for the estimation of the chl-a concentration in these productive turbid waters, the maximum band ratio (MBR) and near-infrared-red (NIR-red) models are used in this study. Specific focus is placed on (a) comparing the ability of the models to estimate chl-a in the range 1-12 mg m(-3), which is typical for coastal and estuarine waters, and (b) assessing the potential of the Moderate Resolution Imaging Spectrometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) to estimate chl-a concentrations. Reflectance spectra and water samples were collected at 13 stations with chl-a ranging from 0.83 to 11.8 mg m(-3) and total suspended matter from 9.9 to 21.5 g m(-3). A close relationship was found between chl-a concentration and total suspended matter concentration with the determining coefficient (R-2) above 0.89. The MBR calculated in the spectral bands of MODIS proved to be a good proxy for chl-a concentration (R-2 > 0.93). On the other hand, both the NIR-red three-band model, with wavebands around 665, 700, and 730 nm, and the NIR-red two-band model (with bands around 665 and 700 nm) explained more than 95% of the chl-a variation, and we were able to estimate chl-a concentrations with a root mean square error below 1 mg m(-3). The two-and three-band NIR-red models with MERIS spectral bands accounted for 93% of the chl-a variation. These findings imply that the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl-a in the Pearl River estuary.
All Author(s) ListZhang YZ, Lin H, Chen CQ, Chen LD, Zhang B, Gitelson AA
Journal nameEnvironmental Research Letters
Detailed descriptiondoi:10.1088/1748-9326/6/2/024016
Year2011
Month4
Day1
Volume Number6
Issue Number2
PublisherIOP PUBLISHING LTD
ISSN1748-9326
LanguagesEnglish-United Kingdom
Keywordschlorophyll; reflectance; remote sensing
Web of Science Subject CategoriesEnvironmental Sciences; ENVIRONMENTAL SCIENCES; Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences; METEOROLOGY & ATMOSPHERIC SCIENCES

Last updated on 2020-04-07 at 02:57