Evaluating the Performance of Algorithms in Estimating the Chl-a Concentration of Lake Bafa

Monitoring and estimating pigment concentrations in water bodies have a critical role early intervention or investigation of causes for prevention. Remote sensing data are the most effective alternative due to its advantages as effortless, requiring less labor, displaying large areas single frame. Analyzing Chlorophyll-a (Chl-a) constitute important research topics because all phytoplankton contain Chl-a. In this study, we evaluated performance algorithms Chl-a concentration Lake Bafa based on Sentinel 2 bands which simulated from in-situ reflectance data. We used 1/R665xR705, 1/R665-1/R705, (1/R665-1/R705) x R740, R705/(R560+R665), so called M09, G09-2B, G09-3B, K07, respectively Normalized Difference Chlorophyll Index (NDCI) evaluation. Water samples measurements were collected obtained two field campaigns. Bands then calibrate validate models estimation. R² values 0.679, 0.749, 0.395, 0.726, RMSE 0.7 1.882, 1.663, 1.737, 1.818 μg/L been NDCI algorithms, respectively. images map validation. Our results show that M09 performed better compared other three our range at Bafa.