Estimation of chlorophyll-a concentration of different seasons in outdoor ponds using hyperspectral imaging

Abstract

Chlorophyll a (Chl-a) is regarded as one of the important components to estimate water quality and sustainability of freshwater aquaculture operations. In the current study, a hyperspectral imaging (HSI) system was used to determine the effect of season models on the accuracy of Chl-a estimation in outdoor aquaculture ponds. A visible and near infrared hyperspectral imaging system (400–1000nm) was used to measure surface spectral reflectance (R) of water collected from outdoor ponds in four different seasons. Firstly, values of surface spectral reflectance (R) were amplified by a baseline correction (740nm). Two-band, three-band and four-band spectral reflectance were used to compute Chl-a concentration and a new cross band ratio algorithm with six wavelengths was proposed in the study. Results indicated that two-band model established based on reflectance ratio (R702/R666) had better performances for Chl-a prediction with determination coefficients (r2) of 0.908 than those by (R675-1−R691-1)*R743 and (R675-1−R691-1)/(R743-1−R691-1) models with r2 of 0.902 and 0.896, respectively. Six optimal wavelengths (410, 682, 691, 966, 972, and 997) were identified using successive projections algorithm (SPA). The optimized regression model (R410-1−R966-1)/(R682-1−R972-1)/(R691-1−R997-1) showed best result with r2 of 0.961 for Chl-a prediction. Model of cross band ratio algorithm with six wavelengths was mapped to each pixel in the image to display Chl-a component in outdoor ponds under four different seasons. The current study showed that it was feasible to use the HSI system for monitoring the influence of seasons for outdoor aquaculture water quality.