Abstract
Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).
Highlights
Traditional water environment monitoring relies on a large amount of human labors by collecting in-situ water samples and conducting chemical analysis, and the results of chemical analysis were compared with water quality standards to obtain the water quality status
To improve the accuracy of model we introduced the optimization two band ratio algorithm based on (B1/B2) by doing a decision tree method shown in Equation (9)
An optimal two band ratio algorithm was proposed for Chl-a estimation in Lake Donghu using Landsat 8 Operational Land Imager (OLI) data
Summary
Traditional water environment monitoring relies on a large amount of human labors by collecting in-situ water samples and conducting chemical analysis, and the results of chemical analysis were compared with water quality standards to obtain the water quality status. As a complement to traditional monitoring, remote sensing method was applied for deriving water quality parameters. For inland water, especially for some relatively small lakes and reservoirs (Lee et al 2016), Landsat satellite data series are perfect for monitoring water quality and ecological change because of its relatively high resolution (30m) and long operating history (46 years). I. Ogashawara (2016) proposed a multi-band model to monitor the dynamic of algal blooms using the Landsat 8 OLI data. Ogashawara (2016) proposed a multi-band model to monitor the dynamic of algal blooms using the Landsat 8 OLI data These Landsat 8 OLI data derived Chl-a estimation algorithms are empirical models and are not applicable to lakes in other region (Li et al, 2018)
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