A remote sensing tool for near real‐time monitoring of harmful algal blooms and turbidity in reservoirs

Abstract

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real‐time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll‐a concentrations (chl‐a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real‐time monitoring of HABs. Landsat‐8 and Sentinel‐2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl‐a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl‐a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open‐source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

“…Single bands are widely used to correlate with in situ data and estimate water quality parameters, such as chlorophyll [30,53], total suspended solids [54], turbidity [55], and temperature [56]. Surface reflectance values have shown good performance in these estimations and are even being used in artificial intelligence [57,58], including in algal bloom detection [24,32,55,59]. NDVI and FAI indices have been used in research related to chlorophyll and algal bloom estimation with good precision [18,60,61].…”

La importancia de usar el biovolumen en estudios de fitoplancton y monitoreo ambiental de cianobacterias

“…La fluorescencia in vivo es de medición instantánea y se puede obtener un gran número de datos con alta frecuencia (en boyas con sondas acopladas), lo que puede facilitar la toma de decisiones ante incrementos de cianobacterias (Ahn et al 2007). Más recientemente, el uso de sensores remotos se ha extendido como herramienta de monitoreo para cuerpos de agua límnicos afectados por floraciones de cianobacterias, utilizando algoritmos para estimar la concentración de la clorofila a y la ficocianina a partir del análisis de imágenes satelitales (Mishra and Mishra 2014;Maciel et al 2022;Pamula et al 2023;Zabaleta et al 2023). Otros métodos en desarrollo incluyen aplicaciones para teléfonos inteligentes, basadas en niveles de color del agua (Mitroi et al 2020), o incluso el monitoreo visual involucrando a la población en monitoreos participativos (Bazán et al 2020;Nabout et al 2022).…”

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

“…This problem has been partially solved, thanks to the remote sensing. Using multispectral images gathered by satellites, it has been feasible to quantify turbidity and identify some origins, or at least quantify the amount of a certain group, such as the phytoplankton or organic matter [16]. This methodology is based on the spectral signature of different substances [17].…”