Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high-and medium-spatial resolution satellite algorithm simulation

摘要

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012-16; 10 seasons) and spatial (30 km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n = 75) were used to select the most suitable spectral bands in seven high-and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and-6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ_(550)-λ_(650) + λ_(800)) C (λ_(550) + λ_(650) + λ_(800)). The best-performing chlorophyll algorithm was Sentinel 2 ((λ_(560)-λ_(660) + λ_(703)) C (λ_(560) + λ_(660) + λ_(703)); R~2 = 0.80), followed by WorldView 2 ((λ_(550)-λ_(660) + λ_(720)) C (λ_(550) + λ_(660) + λ_(720)); R~2 = 0.78), Landsat and the SPOT series ((λ_(550)-λ_(650) + λ_(800)) C (λ_(550) + λ_(650) + λ_(800)); R~2 = 0.67-0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.