Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

Abstract

Turbid coastal plumes carry sediments, nutrients, and pollutants. Satellite remote sensing is an effective tool for studying water quality parameters in these turbid plumes while covering a wide range of hydrological and meteorological conditions. However, determining boundaries of turbid coastal plumes poses a challenge. Traditionally, thresholds are the approach of choice for plume detection as they are simple to implement and offer fast processing (especially important for large datasets). However, thresholds are site-specific and need to be re-adjusted for different datasets or when meteorological and hydrodynamical conditions differ. This study compares state-of-the-art threshold approaches with a novel algorithm (PLUMES) for detecting turbid coastal plumes from satellite remote sensing, tested for Patos Lagoon, Brazil. PLUMES is a semi-supervised, and spatially explicit algorithm, and does not assume a unique plume boundary. Results show that the thresholds and PLUMES approach each provide advantages and limitations. Compared with thresholds, the PLUMES algorithm can differentiate both low or high turbidity plumes from the ambient background waters and limits detection of coastal resuspension while automatically retrieving metrics of detected plumes (e.g., area, mean intensity, core location). The study highlights the potential of the PLUMES algorithm for detecting turbid coastal plumes from satellite remote sensing products, which can have significantly positive implications for coastal management. However, PLUMES, despite its demonstrated effectiveness in this study, has not yet been applied to other study sites.

Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration

“…Previous studies on turbidity in Patos Lagoon based on remote sensing focused on applying algorithms to study the plume [34,57] and water quality in the region [10]. For the L8 and S2 satellites, POLYMER atmospheric correction and N09-recalibrated coefficients [34] achieved better performance in the NIR band (based on Kendall's tau correlation coefficient).…”

“…Távora et al also suggested that Patos Lagoon exhibits optical properties that differ from those considered in the semi-analytical algorithm of Nechad et al [27]. Thus, the study area could serve as a case study for the regionality of turbidity and SPM estimates, which was addressed in previous works [10,34] by regionally recalibrating the coefficients of the turbidity algorithm for Landsat 5, 7, and 8. Therefore, we also provided regionally recalibrated coefficients specific to the study area, expanding this work to include SPM and other satellites, applying a more robust recalibration method, and examining the improvements and limitations associated with this approach.…”

“…A direct and linear relationship between turbidity and SPM has been observed in the region [43], allowing the use of turbidity as a proxy for SPM concentration. Furthermore, turbidity can be used to delimit Patos Lagoon coastal plumes using remote sensing [34]. Turbidity values in the Patos Lagoon estuarine zone usually range from 0 to 100 NTU but can exceed 300 NTU (Figure 1).…”

“…This choice was based on previous comparisons of these two methods, showing that they have distinct spectral uncertainties [11] and performances for different final products [25]. In Patos Lagoon, previous works have applied ACOLITE [10,42] and POLYMER [34], but without a direct comparison of these methods. Additionally, [10] lists the advantages and limitations of each correction.…”

“…The regional calibration of algorithms is an alternative [10,34], combining the advantages of two distinct methodologies-a global and semi-analytical relationship between the reflectance and the variable of interest-combined with empirical adjustments to account for the specificity of the study area (related, for example, to the mass-specific coefficient [32]). Recalibration can be performed using satellite reflectance data, potentially improving the algorithm performance in regions with limited in situ radiometric data, with the disadvantage of adding uncertainty associated with the atmospheric correction process [10,34]. Additionally, spectral convolution is an important step when applying remote-sensing algorithms due to the spectral differences between hyperspectral (commonly used for algorithm development) and multispectral (most satellites) sensors [10].…”