Next Article in Journal
Detection of Benggang in Remote Sensing Imagery through Integration of Segmentation Anything Model with Object-Based Classification
Next Article in Special Issue
Twin Satellites HY-1C/D Reveal the Local Details of Astronomical Tide Flooding into the Qiantang River, China
Previous Article in Journal
Enhancing Satellite Image Sequences through Multi-Scale Optical Flow-Intermediate Feature Joint Network
Previous Article in Special Issue
Preliminary Investigation of Sudden Ground Subsidence and Building Tilt in Balitai Town, Tianjin City, on 31 May 2023
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

1
Departamento Manejo de Bosques y Medio Ambiente, Facultad de Ciencias Forestales, Universidad de Concepción, Concepción 4070386, Chile
2
Instituto de Geología Económica Aplicada, Universidad de Concepción, Concepción 4070386, Chile
3
UMR 5563 Géosciences Environnement Toulouse, Université de Toulouse, CNRS-IRD-OMP-CNES, 31000 Toulouse, France
4
Programa de Máster en Ingeniería de Montes, E.T.S.I Escuela Técnica Superior de Ingeniería, Universidad de Huelva, 21071 Huelva, Spain
5
Doctoral Program in Advanced Forestry Engineering, E.T.S.I Montes, Forestal y Medio Natural, Universidad Politecnica de Madrid—UPM, 28040 Madrid, Spain
6
Forestal ARAUCO S.A., Gerencia de Planificación y Mejora Continua, Concepción 4030000, Chile
7
Departamento de Geofísica, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Concepción 4070386, Chile
8
Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Lientur 1457, Concepción 4030000, Chile
9
Departamento Ciencias de la Tierra, Ciencias Químicas, Universidad de Concepción, Concepción 4030000, Chile
10
ISPA, INRAE, Bordeaux Sciences Agro, 33140 Villenave d’Ornon, France
11
Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology, Fruitcentre, Parc Científic i Tecnològic Agroalimentari de Lleida 23, 25003 Lleida, Spain
12
Grumets Research Group, Departament de Geografia, Edifici B. Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(2), 427; https://doi.org/10.3390/rs16020427
Submission received: 15 November 2023 / Revised: 9 January 2024 / Accepted: 20 January 2024 / Published: 22 January 2024
(This article belongs to the Special Issue New Developments in Remote Sensing for the Environment II)

Abstract

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.
Keywords: eutrophication; Landsat; Chl-a; turbidity; spectral signatures; OLI; Chile eutrophication; Landsat; Chl-a; turbidity; spectral signatures; OLI; Chile
Graphical Abstract

Correction Statement

This article has been republished with a minor correction to the existing affiliation information. "Grumets Research Group, Departament de Geografia, Edifici B. Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain" should change to "Grumets Research Group, Departament de Geografia, Edifici B. Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain". This change does not affect the scientific content of the article.

Share and Cite

MDPI and ACS Style

Yépez, S.; Velásquez, G.; Torres, D.; Saavedra-Passache, R.; Pincheira, M.; Cid, H.; Rodríguez-López, L.; Contreras, A.; Frappart, F.; Cristóbal, J.; et al. Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile. Remote Sens. 2024, 16, 427. https://doi.org/10.3390/rs16020427

AMA Style

Yépez S, Velásquez G, Torres D, Saavedra-Passache R, Pincheira M, Cid H, Rodríguez-López L, Contreras A, Frappart F, Cristóbal J, et al. Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile. Remote Sensing. 2024; 16(2):427. https://doi.org/10.3390/rs16020427

Chicago/Turabian Style

Yépez, Santiago, Germán Velásquez, Daniel Torres, Rodrigo Saavedra-Passache, Martin Pincheira, Hayleen Cid, Lien Rodríguez-López, Angela Contreras, Frédéric Frappart, Jordi Cristóbal, and et al. 2024. "Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile" Remote Sensing 16, no. 2: 427. https://doi.org/10.3390/rs16020427

APA Style

Yépez, S., Velásquez, G., Torres, D., Saavedra-Passache, R., Pincheira, M., Cid, H., Rodríguez-López, L., Contreras, A., Frappart, F., Cristóbal, J., Pons, X., Flores, N., & Bourrel, L. (2024). Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile. Remote Sensing, 16(2), 427. https://doi.org/10.3390/rs16020427

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop