Spatio-temporal variability of turbidity derived from Sentinel-2 in Reloncaví sound, Northern Patagonia, Chile

Abstract

Turbidity is associated with the loss of water transparency due to the presence of particles, sediments, suspended solids, and organic or inorganic compounds in the water, of natural or anthropogenic origin. Our study aimed to evaluate the spatio-temporal variability of turbidity from Sentinel-2 (S2) images in the Reloncaví sound and fjord, in Northern Patagonia, Chile, a coastal ecosystem that is intensively used by finfish and shellfish aquaculture. To this end, we downloaded 123 S2 images and assembled a five-year time series (2016–2020) covering five study sites (R1 to R5) located along the axis of the fjord and seaward into the sound. We used Acolite to perform the atmospheric correction and estimate turbidity with two algorithms proposed by Nechad et al. (2009, 2016 Nv09 and Nv16, respectively). When compared to match-up, and in situ measurements, both algorithms had the same performance (R2 = 0.40). The Nv09 algorithm, however, yielded smaller errors than Nv16 (RMSE = 0.66 FNU and RMSE = 0.84 FNU, respectively). Results from true-color imagery and two Nechad algorithms singled an image from the austral autumn of 2019 as the one with the highest turbidity. Similarly, three images from the 2020 austral autumn (May 20, 25, 30) also exhibited high turbidity values. The turbid plumes with the greatest extent occurred in the autumn of 2019 and 2020, coinciding with the most severe storms and runoff events of the year, and the highest turbidity values. Temporal trends in turbidity were not significant at any of the study sites. However, turbidity trends at sites R1 and R2 suggested an increasing trend, while the other sites showed the opposite trend. Site R1 recorded the highest turbidity values, and the lowest values were recorded at R5 in the center of the sound. The month of May was characterized by the highest turbidity values. The application of algorithms from high-resolution satellite images proved to be effective for the estimation and mapping of this water quality parameter in the study area. The use of S2 imagery unraveled a predictable spatial and temporal structure of turbidity patterns in this optically complex aquatic environment. Our results suggest that the availability of in situ data and the continued evaluation of the performance of the Nechad algorithms can yield significant insights into the dynamics and impacts of turbid waters in this important coastal ecosystem.

Keywords

• Remote sensing
• Satellite algorithms
• Ocean color
• Aquaculture
• Temporal patterns
• Water quality monitoring