chlorophyll-a at depth; lake; machine learning; remote sensing; Chlorophyll a; Chlorophyll-a at depth; Chlorophyll-a concentration; Detection algorithm; Field data; Freshwater ecosystem; Machine-learning; Remote sensing data; Remote-sensing; Statistic modeling; Earth and Planetary Sciences (all)
Abstract :
[en] In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.
Research Center/Unit :
Geology - ULiège
Disciplines :
Earth sciences & physical geography
Author, co-author :
Rodríguez-López, Lien ; Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Concepcion, Chile
Alvarez, Denisse ; Centro Bahía Lomas, Facultad de Ciencias, Universidad Santo Tomás, Concepcion, Chile
Bustos Usta, David; Facultad de Oceanografía, Universidad de Concepción, Concepcion, Chile
Duran-Llacer, Iongel ; Hémera Centro de Observación de la Tierra, Facultad de Ciencias, Ingeniería y Tecnología, Universidad Mayor, Santiago, Chile
Bravo Alvarez, Lisandra ; Department of Electrical Engineering, Universidad de Concepción, Concepcion, Chile
Fagel, Nathalie ; Université de Liège - ULiège > Département de géologie > Argiles, géochimie et environnements sédimentaires
Bourrel, Luc; Géosciences Environnement Toulouse, UMR 5563, Université de Toulouse, CNRS-IRD-OMP-CNES, Toulouse, France
Frappart, Frederic ; INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-d’Ornon, France
Urrutia, Roberto ; Université de Liège - ULiège > Département de géologie > Argiles, géochimie et environnements sédimentaires ; Facultad de Ciencias Ambientales, Universidad de Concepción, Concepcion, Chile
Language :
English
Title :
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Publication date :
February 2024
Journal title :
Remote Sensing
eISSN :
2072-4292
Publisher :
Multidisciplinary Digital Publishing Institute (MDPI)
Proyecto Interuniversitario de Iniciación en Investigación Asociativa (Chile)
Funders :
WBI - Wallonia-Brussels International
Funding text :
This research was funded by “Proyecto Interuniversitario de Iniciación en Investigación Asociativa: P3IA-22/23” and Vicerrectoría de Investigación y Doctorados Universidad San Sebastián.
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