Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model

Accurate remote retrieval of chlorophyll-a (Chl-a) concentrations for inland and coastal turbid waters is a challenging task due to their optical complexity. An adaptive model was developed based on the merits of coupling a genetic algorithm to select spectral variables and partial least squares (GA-PLS) for regression. The objectives of this paper are: (1) to evaluate the GA-PLS model performance using datasets collected from 1140 stations encompassing a wide range of Chl-a and suspended sediment from nine water bodies across Central Indiana (CIN), USA, South Australia (SA), Taihu Lake (THL) in East China and Shitoukoumen Reservoir (STKR) in Northeast China with comparison to a widely accepted three-band model, and (2) to evaluate the GA-PLS spatial transferability with simulated ESA/Sentinel3/OLCI and Hyperion spectra. The GA-PLS and the three-band model yield accurate calibrations (Cal) for the SA dataset with R2 above 0.98, and the corresponding validation (Val) shows relative root mean squared error (rRMSE) of less than 6.2% with narrow-band spectra. Both the GA-PLS and three-band model show stable performance for the CIN dataset (Cal: R2=0.91 and 0.77; Val: rRMSE=20.1% and 33.4%), THL dataset (Cal: R2=0.91 and 0.88; Val: rRMSE=30.1% and 33.7%), and STKR dataset (R2=0.84 and 0.82; rRMSE=29.1% and 33.2%). The results also reveal that simulated OLCI datasets degrade both the GA-PLS performance, and particularly the performance of the three-band model due to the coarser and discontinuous spectral configuration. Contrastingly, both the GA-PLS and the three-band model show improved results with the simulated Hyperion datasets. Our observation indicates that the GA-PLS model outperforms the three-band model in terms of spatial transferability; however, the three-band model has its own merits, considering its simplicity. Further analyses indicate that spectral measurement protocols, instrumentations, and inorganic suspended matter affect the GA-PLS and three-band model performances. •We estimate Chl-a through hybrid and three-band models using in situ spectral data.•GA-PLS outperforms three-band model (TBM) for Chl-a estimates.•GA-PLS performs stable with both narrow-band and simulated spectra.•Inorganic suspended matter exerts a significantly effect on Chl-a estimates.•Both GA-PLS and TBM show promising potentials for Chl-a mapping.