Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm

Matthews, MWBernard, StewartEvers-King, HRobertson Lain, L2021-04-102021-04-102020-10Matthews, M., Bernard, S., Evers-King, H. & Robertson Lain, L. 2020. Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. Remote Sensing of Environment, 248. http://hdl.handle.net/10204/119720034-42571879-0704https://doi.org/10.1016/j.rse.2020.111981https://www.sciencedirect.com/science/article/pii/S0034425720303515http://hdl.handle.net/10204/11972A hyperspectral inversion algorithm was used to distinguish between cyanobacteria and algal blooms in optically complex inland waters. A framework for the algorithm is presented that incorporates a bio-optical model, a solution for the radiative transfer equation using the EcoLight-S radiative transfer model, and a non-linear optimization procedure. The natural variability in the size of phytoplankton populations was simulated using a two-layered sphere model that generated size-specific inherent optical properties (IOPs). The algorithm effectively determined the type of high-biomass blooms in terms of the relative percentage species composition of cyanobacteria. It also provided statistically significant estimates of population size (as estimated by the effective diameter), chlorophyll-a (chl-a) and phycocyanin pigment concentrations, the phytoplankton absorption coefficient, and the non-algal absorption coefficient. The algorithm framework presented here can in principle be adapted for distinguishing between phytoplankton groups using satellite and in situ remotely sensed reflectance.AbstractenAlgorithmsBio-opticsCyanobacteriaHarmful algal bloomsHyperspectralRemote sensingDistinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithmArticleMatthews, M., Bernard, S., Evers-King, H., & Robertson Lain, L. (2020). Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. Remote Sensing of Environment, 248, http://hdl.handle.net/10204/11972Matthews, MW, Stewart Bernard, H Evers-King, and L Robertson Lain "Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm." Remote Sensing of Environment, 248 (2020) http://hdl.handle.net/10204/11972Matthews M, Bernard S, Evers-King H, Robertson Lain L. Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm. Remote Sensing of Environment, 248. 2020; http://hdl.handle.net/10204/11972.TY - Article AU - Matthews, MW AU - Bernard, Stewart AU - Evers-King, H AU - Robertson Lain, L AB - A hyperspectral inversion algorithm was used to distinguish between cyanobacteria and algal blooms in optically complex inland waters. A framework for the algorithm is presented that incorporates a bio-optical model, a solution for the radiative transfer equation using the EcoLight-S radiative transfer model, and a non-linear optimization procedure. The natural variability in the size of phytoplankton populations was simulated using a two-layered sphere model that generated size-specific inherent optical properties (IOPs). The algorithm effectively determined the type of high-biomass blooms in terms of the relative percentage species composition of cyanobacteria. It also provided statistically significant estimates of population size (as estimated by the effective diameter), chlorophyll-a (chl-a) and phycocyanin pigment concentrations, the phytoplankton absorption coefficient, and the non-algal absorption coefficient. The algorithm framework presented here can in principle be adapted for distinguishing between phytoplankton groups using satellite and in situ remotely sensed reflectance. DA - 2020-10 DB - ResearchSpace DP - CSIR J1 - Remote Sensing of Environment, 248 KW - Algorithms KW - Bio-optics KW - Cyanobacteria KW - Harmful algal blooms KW - Hyperspectral KW - Remote sensing LK - https://researchspace.csir.co.za PY - 2020 SM - 0034-4257 SM - 1879-0704 T1 - Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm TI - Distinguishing cyanobacteria from algae in optically complex inland waters using a hyperspectral radiative transfer inversion algorithm UR - http://hdl.handle.net/10204/11972 ER -24227