A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters
By Anatoly A. Gitelson, D. Gurlin, W. Moses, Tadd M. Barrow. 2009
217 citations recorded by the source corpus.
The objective of this work was to test the performance of a recently developed three-band model and its special case, a two-band model, for the remote estimation of the chlorophyll- a (chl-a) concentration in turbid productive case 2 waters. We specifically focused on (a) determining the ability of the models to estimate chl- a <20 mg m−3, typical for coastal and estuarine waters, and (b) assessing the potential of MODIS and MERIS to estimate chl-a concentrations in turbid productive waters, using red and near-infrared (NIR) bands. Reflectance spectra and water samples were collected in 89 stations over lakes in the United States with a wide variability in optical parameters (i.e. 2.1<chl-a<184 mg m−3; 0.5<Secchi disk depth<4.2 m; 1.2<total suspended matter<15 mg l−1). The three-band model, using wavebands around 670, 710 and 750 nm, explains more than 89% of the chl- a variation for chl- a ranging from 2 to 20 mg m−3 and can be used to estimate chlorophyll-a concentrations with a root mean square error (RMSE) of <1.65 mg m−3. MODIS (bands 13 and 15) and MERIS (bands 7, 9, and 10) red and NIR reflectances were simulated from the collected reflectance spectra and potential estimation errors were assessed. The MODIS two-band model is able to estimate chl- a concentrations with a RMSE of<7.5 mg m−3 for chl-a ranging from 2 to 50 mg m−3; however, the model loses its sensitivity for chl- a<20 mg m−3. Benefiting from the higher spectral resolution of the MERIS data, the MERIS three-band model accounts for 93% of chl- a variation and is able to estimate chl-a concentrations with a RMSE of<5.1 mg m−3 for chl-a ranging from 2 to 50 mg m−3, and a RMSE of<1.7 mg m−3 for chl-a ranging from 2 to 20 mg m−3. These findings imply that, provided that an atmospheric correction scheme specific to the red and NIR spectral region is available, the extensive database of MODIS and MERIS images could be used to quantitatively monitor chl- a in case 2 waters.
Subfields: Industrial and Manufacturing Engineering, Oceanography, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/df0578df30815cc7c37bf407b92ebebcc2a549ee
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In Oceanography
Citations in the corpus
- Optical types of inland and coastal waters (2017, methodology): “…techniques including agglomerative hierarchical (Shi et al. 2014), k-means clustering (Palacios et al. 2012), fuzzy clustering (Gonz alez Vilas et al. 2011; Moore et al. 2014), and artificial neural networks (Canziani et al. 2008) have been used to uncover clusters present in these…”
- Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach (2020, methodology): “Some measurements were made via a pair of intercalibrated (Ocean Optics ® or RMSES ® ) spectrometers with one measuring subsurface upwelling radiance and the other measuring total downwelling irradiance above the surface (e.g., Nebraska Lakes and Reservoirs) (Gitelson et al., 2009).”
- Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data—successes and challenges (2009, methodology): “Thus the twoband model for MERIS data (equation (4)) is less sensitive to uncertainties in the retrieved NIR reflectance than is the twoband model (equation (3)) for MODIS data (Gitelson et al 2009).”