Poster Session 4, Thursday, October  6, 11:00–12:40

Poster 76


Remote sensing is a useful tool for mapping turbidity in coastal waters, and different algorithms have been developed for that purpose. Existing algorithms, however, need to be calibrated for different regions for higher accuracy on turbidity estimates. The present study aims to investigate the applicability of the algorithms proposed by Dogliotti et. al (2015) and Nechad et. al (2009) for the turbid Patos Lagoon estuary, Southern Brazil. Landsat 8 satellite images (79 scenes, from 2016-2021) were corrected atmospherically and turbidity products were estimated using the ACOLITE processor. Both algorithms were tested, determining the best algorithm and wavelength (i.e., red or near infrared) to avoid saturation of the reflectance signal. Statistic parameters (i.e., Kendall-Tau correlation coefficient (), and root mean square of errors (RMSE)) were calculated to evaluate the performance of the algorithms by comparing turbidity estimates against in-situ data. Results showed that the Dogliotti et. al (2015) algorithm yielded better turbidity estimates, especially in the red band (RMSE = 28.96; = 0.68, p-value = <<0,001), while the Nechad et. al (2009) algorithm applied to the near infrared band showed the worst results (RMSE = 39.04; = 0.38, p-value = <0,001). Subsequently, the 5 years-long derived time series of turbidity allowed further inferences on the spatial and temporal variability of the Patos Lagoon estuary turbidity. Thus, the use of Landsat 8 products, especially in the red band, proved to be adequate for estimating turbidity in the Patos Lagoon estuary.

*Araiene Pereira, Federal University of Rio Grande – FURG, [email protected]

Juliana Távora, Faculty Geo-Information Science and Earth Observation (ITC), University of Twente, [email protected], 0000-0002-0314-1250

Elisa Fernandes, Federal University of Rio Grande (FURG), [email protected], 0000-0003-1869-0233

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