Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data

Journal of Applied Remote Sensing
By: , and 



Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.

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Publication type Article
Publication Subtype Journal Article
Title Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data
Series title Journal of Applied Remote Sensing
DOI 10.1117/1.JRS.16.044522
Volume 16
Issue 4
Year Published 2022
Language English
Publisher SPIE Digital Library
Contributing office(s) Idaho Water Science Center, Texas Water Science Center
Description 044522, 22 p.
Country United States
State Idaho, Oregon
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