Estimation of chlorophyll-a concentration using field spectral data: a case study in inland Case-II waters, North China

In the remote sensing of chlorophyll-a (Chla) in inland Case-II waters, the assumption that the optical parameter of Chla specific absorption coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$a_{\rm ph}^{\ast}$\end{document} remains constant usually restrains application of many models. In this paper, we presented a newly developed model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\big[ {R_{\rm rs} ^{-1}( {\lambda _1 } ) -R_{\rm rs} ^{-1}( {\lambda _2 } )} \big]\times R_{\rm rs} ( {\lambda _3 } )\times a_{\rm ph}^{\ast}{}^{-1}( {\lambda _1 } )$\end{document} which was improved on a previous three-band model to isolate interferences from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$a_{\rm ph}^{\ast}$\end{document}. In terms of the importance of water optical properties in the model development, spectral and absorption characteristics were analyzed for Shitoukoumen Reservoir and Songhua Lake in Northeast China, as typical examples of inland Case-II waters. Both waters showed overwhelming absorption sum of tripton and chromophoric dissolved organic matter (CDOM) owing to their relatively low Chla contents (1.53 to 19.35 μgl − 1). According to the optical characteristics of waters studied, optimal positions for λ1, λ2 and λ3 were spectrally tuned to be at 664, 684 and 705 nm, respectively. The model allowed accurate Chla estimation with a determination coefficient (R2) close to 0.98 and a root mean square error (RMSE) of 0.87 μgl − 1. Comparison of different models further showed the stability of the improved model, implying its potential use in water color remote sensing. Although the findings underline the rationale behind the improved model, an extensive database containing data in different water conditions and water types is required to generalize its application.