Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir.

Autor: Lopez Barreto BN; Environmental Systems Graduate Group Department of Civil & Environmental Engineering University of California Merced Merced CA USA.; Center for Information Technology Research in the Interest of Society The Banatao Institute University of California Merced Merced CA USA., Hestir EL; Environmental Systems Graduate Group Department of Civil & Environmental Engineering University of California Merced Merced CA USA.; Center for Information Technology Research in the Interest of Society The Banatao Institute University of California Merced Merced CA USA., Lee CM; NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA., Beutel MW; Environmental Systems Graduate Group Department of Civil & Environmental Engineering University of California Merced Merced CA USA.
Jazyk: angličtina
Zdroj: GeoHealth [Geohealth] 2024 Feb 23; Vol. 8 (2), pp. e2023GH000941. Date of Electronic Publication: 2024 Feb 23 (Print Publication: 2024).
DOI: 10.1029/2023GH000941
Abstrakt: Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll- a (chl- a ) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl- a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl- a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl- a and cyanobacteria, respectively, classified chl- a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl- a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.
Competing Interests: The authors declare no conflicts of interest relevant to this study.
(© 2024 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)
Databáze: MEDLINE
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