Natural effects on remote sensing of water quality parameters data: a case study on available algorithms at the Jupia Reservoir, Brazil

Henrique R. Leite; Fabiano A. de Oliveira; Danielle Drago; Andressa Muraro; Luiz F. B. Teixeira; Fabiano S. Hainosz; Ronan M. Prochnow; Soraia T. Quicu; Carlos Nascimento

doi:10.1117/12.2532327

14 October 2019 Natural effects on remote sensing of water quality parameters data: a case study on available algorithms at the Jupia Reservoir, Brazil

Henrique R. Leite, Fabiano A. de Oliveira, Danielle Drago, Andressa Muraro, Luiz F. B. Teixeira, Fabiano S. Hainosz, Ronan M. Prochnow, Soraia T. Quicu, Carlos Nascimento

Author Affiliations +

Proceedings Volume 11150, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019; 1115006 (2019) https://doi.org/10.1117/12.2532327
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

This paper attempts to exploit how the geometry of the radiative flux, sensor characteristics, atmospheric effects and particularities inherent to water bodies can affect water quality indicators parameters data retrieved from satellite imagery. This was done by comparing data from bio-optical models applied to Sentinel-2 MSI imagery with field samples from 12 campaigns ranging from 2016 to 2019 at the Jupia Reservoir (Brazil), along with analyzing the spectra of different atmospheric correction algorithms, in the search of possible natural effects that hampered the quality of the satellite-derived data. Compared water quality parameters were: Turbidity, chlorophyll-a, total suspended matter and Secchi disk depth. Results showed that, the data presented very low, if not inexistent, correlation. However, the error values estimated reflected that the data were not too far apart, despite having no correlation. The effects that may have caused the low correlation and the errors were analyzed through the retrieval of the spectra in the field sampling points. The compared spectra showed that, effects of haze caused by aerosols, bottom reflectance from optical depth and the presence of submerged plants were the most critical reflectance altering phenomena, which reflected in the extracted water quality data. Inelastic scattering, fluorescence and ozone layer influence were undetected, while the adjacency effect and Sun glint presented little to no effect on the data. Further focused analysis of these specific effects is a promising field of study in order to improve atmospheric correction and bio-optical algorithms.

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Henrique R. Leite, Fabiano A. de Oliveira, Danielle Drago, Andressa Muraro, Luiz F. B. Teixeira, Fabiano S. Hainosz, Ronan M. Prochnow, Soraia T. Quicu, and Carlos Nascimento "Natural effects on remote sensing of water quality parameters data: a case study on available algorithms at the Jupia Reservoir, Brazil", Proc. SPIE 11150, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019, 1115006 (14 October 2019); https://doi.org/10.1117/12.2532327

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