ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Terrestrial Water Cycle
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1633522
This article is part of the Research TopicGlobal Change and Lake Monitoring: Harnessing Earth Observations for Environmental AnalysisView all articles
Advanced Phycocyanin Detection in a South American Lake Using Landsat Imagery and Remote Sensing
Provisionally accepted
Lien Rodríguez López1*
David Francisco Bustos Usta2
Iongel Duran-Llacer3
Lisandra Bravo Alvarez4
Luc BOURREL5
Frédéric Frappart6Roberto Urrutia7- 1San Sebastián University, Santiago, Chile
- 2Universidad de Concepcion Facultad de Ciencias Naturales y Oceanograficas, Concepcin, Chile
- 3Universidad Mayor, Santiago, Chile
- 4Universidad de Concepcion Facultad de Ingenieria, Concepcin, Chile
- 5Laboratoire Geosciences Environnement Toulouse, Toulouse, France
- 67INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, 33140 Villenave-d’Ornon, Bordeaux, France
- 7Facultad de Ciencias Ambientales, Universidad de Concepción, Concepción, Chile
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In this study, multispectral images were used to detect toxic blooms in Villarrica Lake in Chile, using a time series of water quality data from 1989 to 2024, based on the extraction of spectral information from Landsat 8 and 9 satellite imagery. To explore the predictive capacity of these variables, we constructed 255 multiple linear regression models using different combinations of spectral bands and indices as independent variables, with phycocyanin concentration as the dependent variable. The most effective model, selected through a stepwise regression procedure, incorporated seven statistically significant predictors (p < 0.05) and took the following form: FCA = N/G + NDVI + B + GNDVI + EVI + SABI + CCI. This model achieved a strong fit to the validation data, with an R² of 0.85 and an RMSE of 0.10 µg/L, indicating high explanatory power and relatively low error in phycocyanin estimation. When applied to the complete weekly time series of satellite observations, the model This is a provisional file, not the final typeset article successfully captured both seasonal dynamics and interannual variability in phycocyanin concentrations (R² = 0.92; RMSE = 0.05 µg/L). These results demonstrate the robustness and practical utility for long-term monitoring of harmful algal blooms in Lake Villarrica.
Keywords: remote sensing, Phycocyanin, algal blooms, lake, Chile
Received: 22 May 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Rodríguez López, Bustos Usta, Duran-Llacer, Bravo Alvarez, BOURREL, Frappart and Urrutia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lien Rodríguez López, San Sebastián University, Santiago, Chile
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