Atmospheric correction (AC) of satellite images is fundamental for water quality studies due to the low reflectance of water in the VIS-NIR region of the spectrum, which rarely exceeds 20%, and then the dominant contribution of atmosphere to the radiance signal measured by satellites in such spectral region. This presents a challenge, especially in inland waters, where the content of different optically active components, such as phytoplankton, organic matter, non-algal particles, increase the reflectance values in the NIR region compared to oceanic water. This hampers the application of usual AC algorithms, where water bodies are assumed to have reflectances values close to zero in the NIR region. In this work, four AC algorithms specially developed for application in water bodies (C2RCC, ACOLITE, Polymer and Level-2 OLCI product) were evaluated using the Sentinel-3-OLCI sensor (S3-OLCI) images, a multispectral radiometer specifically designed for use over water bodies. The reflectance spectra obtained from the processors were validated with in-situ remote sensing reflectance (Rrs) data measured during field campaigns carried out in reservoirs and coastal water of the Iberian Peninsula (Spain) with different trophic states, between 2018 and 2023. For this purpose, 27 images of S3-OLCI coinciding (±1 day) with dates of the field radiometry measurements, were atmospherically corrected using the four AC algorithms. The Rrs obtained from each pixel band coinciding with the sampling points were statistically filtered, and the resulting dataset was validated with the in-situ radiometry data.

Primary Presenter: Patricia Urrego, Image Processing Laboratory (IPL), University of Valencia (patricia.urrego@uv.es)

Authors:

Esther Patricia Urrego, Image Processing Laboratory (IPL), University of Valencia  (patricia.urrego@uv.es)

Carolina Tenjo, Image Processing Laboratory (IPL), University of Valencia  ()

Xavi Sòria-Perpinyà, Image Processing Laboratory (IPL), University of Valencia  ()

Jesús Soriano, Balearic Islands Coastal Observing and Forecasting System (SOCIB)  ()

Antonio Ruíz-Verdú, Image Processing Laboratory (IPL), University of Valencia  ()

Jesús Delegido, Image Processing Laboratory (IPL), University of Valencia  ()

Eduardo Vicente, Cavanilles Institute of Biodiversity and Evolutionary Biology (ICBiBE), University of Valencia  ()

José Moreno, Image Processing Laboratory (IPL), University of Valencia  ()