Secchi Depth Retrieval in Oligotrophic to Eutrophic Chilean Lakes Using Open Access Satellite-Derived Products
Year: 2024
Type: article
Abstract: The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (rs = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties.
Source: Remote Sensing
DOI: 10.3390/rs16224327
Institutions: University of Concepción, Universidad Autónoma de Chile, Centro de Recursos Hídricos para la Agricultura y la Minería, Agencia Nacional de Investigación y Desarrollo, University of Bío-Bío, Centro de Ciencias Ambientales EULA-Chile de la Universidad de Concepción, Universidad Nacional Agraria La Molina, Leibniz Institute of Freshwater Ecology and Inland Fisheries
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
Awards: G1197213904, G1507641020, G1902165130, G2079469327, G2475137278, G4206786294, G4649278195, G5026842276, G5650031767, CLIMATE CHANGE AND MULTIPLE LOCAL STRESSORS IN FRESHWATER ECOSYSTEMS, A THEORETICAL-MATHEMATICAL MODEL SYSTEM FOR MONITORING THE ECOLOGICAL INTEGRITY OF FRESHWATER ECOSYSTEMS A REMOTE SENSING ENVIRONMENTAL OBSERVATORY FEEDING INTO AN ARTIFICIAL INTELLIGENCE SYSTEM, G588429227, WATER RESEARCH CENTER FOR AGRICULTURE AND MINING (CRHIAM), MULTIDIMENSIONAL ANALYSIS OF CLIMATE CHANGE-BIOMASS INTERACTION IN RIPARIAN FORESTS TOWARDS A BETTER UNDERSTANDING OF ECOLOGICAL INTEGRITY IN WATERSHEDS, CENTRO DE RECURSOS HIDRICOS PARA LA AGRICULTURA Y MINERIA (CRHIAM)
OpenAlex ID: W4404546184