Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Zolfaghari, K, Pahlevan, N, Binding, CE, Gurlin, D, Simis, SGH, Verdu, AR, Li, L, Crawford, CJ, VanderWoude, A, Errera, R, Zastepa, A and Duguay, CR 2021 Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment. IEEE Transactions on Geoscience and Remote Sensing. 1-20. https://doi.org/10.1109/TGRS.2021.3114635

Preview

Text Impact_of_Spectral_Resolution_on_Quantifying_Cyanobacteria_in_Lakes_and_Reservoirs_A_Machine-Learning_Assessment.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview

Abstract/Summary

Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of best-available multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance ( Rrs) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%) outperforms other ML models. This model is subsequently applied to Rrs spectra resampled to the band configuration of existing satellite instruments and of the one proposed for the next Landsat sensor. These results confirm that employing MLP models to estimate PC from hyperspectral data delivers tangible improvements compared with retrievals from multispectral data and benchmark algorithms (with median errors between ~73% and 126%) and shows promise for developing a globally applicable cyanobacteria measurement approach.

Item Type:	Publication - Article
Additional Keywords:	Cyanobacteria harmful algal bloom (Cyano HAB),hyperspectral, machine learning (ML),neural network, phycocyanin (PC), spectral resolution.
Divisions:	Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User:	S Hawkins
Date made live:	26 Nov 2021 15:26
Last Modified:	26 Nov 2021 15:26
URI:	https://plymsea.ac.uk/id/eprint/9475

Actions (login required)

View Item