Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain). Water 2023, 15, 3669. https://doi.org/10.3390/w15203669
Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain). Water 2023, 15, 3669. https://doi.org/10.3390/w15203669
Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain). Water 2023, 15, 3669. https://doi.org/10.3390/w15203669
Molner, J.V.; Soria, J.M.; Pérez-González, R.; Sòria-Perpinyà, X. Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain). Water 2023, 15, 3669. https://doi.org/10.3390/w15203669
Abstract
In this study, we investigated water transparency estimation models in the hypertrophic lagoon of the Albufera of Valencia using Sentinel-2 images. Water transparency, a crucial environmental indicator, was assessed via Secchi disk depth (ZSD) measurements. Three optical models (R490/R560, R490/R705, R560/R705) were explored to establish a robust algorithm for ZSD estimation. Through extensive field sampling and laboratory analyses, weekly data spanning 2018 to 2023 were collected, including water transparency, temperature, conductivity, and chlorophyll-a concentration. Remote sensing imagery from the Sentinel-2 mission was employed, and images were processed using SNAP software. The R560/R705 model, calibrated for turbid lakes, emerged as the most suitable. The algorithm's calibration was validated with high correlation coefficients (R2) in both calibration (0.6149) and validation (0.916) phases, demonstrating the model's accuracy in estimating ZSD. This new algorithm significantly outperformed a previous approach, highlighting the importance of tailoring algorithms to specific water body characteristics. The study contributes to improved water quality assessment and resource management, underscoring the value of remote sensing in environmental research.
Keywords
Secchi disk depth; water quality; remote sensing; eutrophication; optical modeling; water management
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
