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Articles | Volume XLVIII-4/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 371–378, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-371-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 371–378, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-371-2022
 
06 Aug 2022
06 Aug 2022

SEA WATER TURBIDITY ANALYSIS FROM SENTINEL-2 IMAGES: ATMOSPHERIC CORRECTION AND BANDS CORRELATION

A. Pisanti1, S. Magrì2,3, I. Ferrando3, and B. Federici3 A. Pisanti et al.
  • 1Department of Earth, Environmental and Life Sciences (DISTAV), University of Genoa, Corso Europa 26, 16132 Genoa, Italy
  • 2Environment State and Protection from Natural Hazards Department, Regional Agency for the Environmental Protection of Liguria (ARPAL), via Bombrini 8, 16149 Genoa, Italy
  • 3Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, via Montallegro 1, 16145 Genoa, Italy

Keywords: Sea water turbidity, Earth Observation, Sentinel-2, atmospheric correction, bands correlation, Aerosol Optical Depth

Abstract. Turbidity is a visual property of water, related to the presence of suspended particles in waters. This parameter is measured in different water quality monitoring programmes as it can determine negative environmental effects both on the biotic and abiotic marine ecosystem. Traditional methods, e.g., in situ monitoring, offer high accuracy but provide sparse information in space and time. On the other hand, Earth Observation (EO) techniques have the potential to provide a comprehensive, fast and inexpensive monitoring system to observe the biophysical and biochemical conditions of water bodies. In the present work, a method for seawater turbidity retrieval from Sentinel-2 multispectral optical images, freely available within the EU Copernicus programme, is presented. The study explores different atmospheric correction methods available in open source software (QGIS, GRASS GIS and SNAP), in order to convert Level-1C (L1C) Top-Of-Atmosphere (TOA) images to Level-2A (L2A) Bottom-Of-Atmosphere (BOA), when the latter is not directly available. Once the proper method for atmospheric correction was identified and applied, the correlation between the in situ dataset and the individual bands known to be most sensitive to water turbidity, i.e., blue (B2), green (B3), red (B4) and near infrared (B8 and B8A) bands, were investigated and a linear regression model between selected band values and turbidity was identified.