Abstract
Monitoring cyanobacteria is an essential step for the development of environmental and public health policies. While traditional monitoring methods rely on collection and analysis of water samples, remote sensing techniques have been used to capture their spatial and temporal dynamics. Remote detection of cyanobacteria is commonly based on the absorption of phycocyanin (PC), a unique pigment of freshwater cyanobacteria, at 620 nm. However, other photosynthetic pigments can contribute to absorption at 620 nm, interfering with the remote estimation of PC. To surpass this issue, we present a remote sensing algorithm in which the contribution of chlorophyll-a (chl-a) absorption at 620 nm is removed. To do this, we determine the PC contribution to the absorption at 665 nm and chl-a contribution to the absorption at 620 nm based on empirical relationships established using chl-a and PC standards. The proposed algorithm was compared with semi-empirical and semi-analytical remote sensing algorithms for proximal and simulated satellite sensor datasets from three central Indiana reservoirs (total of 544 sampling points). The proposed algorithm outperformed semi-empirical algorithms with root mean square error (RMSE) lower than 25 µg/L for the three analyzed reservoirs and showed similar performance to a semi-analytical algorithm. However, the proposed remote sensing algorithm has a simple mathematical structure, it can be applied at ease and make it possible to improve spectral estimation of phycocyanin from space. Additionally, the proposed showed little influence from the package effect of cyanobacteria cells.
Highlights
Cyanobacteria, known as blue-green algae, has been observed in lentic freshwater systems worldwide [1]
This compositional heterogeneity can facilitate the evaluation of the geographic transferability of the selected remote sensing algorithms
It was observed that a lower performance occurred when the correction coefficient of achl-a at 620 nm determined for one algorithm was used in the implementation of the other algorithm. These differences were not meaningful as compared to those resulting from using hyperspectral measurements (Table 7). While these results show that the proposed algorithm has potential to be applicable to Medium Resolution Imaging Spectrometer (MERIS)/OLCI spectral data, what should be kept in mind is that this conclusion is based on the use of simulated OLCI spectra in which the interference of atmosphere is minimal
Summary
Cyanobacteria, known as blue-green algae, has been observed in lentic freshwater systems worldwide [1]. Their success in aquatic systems has been associated with ecophysiological strategies which allow them to exploit the variability in different environmental factors, nutrient over-enrichment and hydrologic alterations to ecosystems [2,3,4]. The traditional management of cyanobacteria focus on the in situ collection of water samples for monitoring their cell abundance, biomass and indicators such as chlorophyll-a (chl-a) and microcystin. These methods are expensive, time consuming [7], and have difficulty capturing the spatial and temporal heterogeneity [8]. Remote sensing studies have evaluated the use of phycocyanin (PC) to estimate cyanobacterial concentration [9,10,11,12,13,14,18,19,20] in aquatic systems
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