Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

Abstract

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m3). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m3, class 2 varies 19.51–87.63 mg/m3, and class 3 varies 75.89–938.97 mg/m3. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R2 = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R2 = 0.93; MAPE = 23.35%), and 2-band (R2 = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R2 = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R2 = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters.

Trophic state in a tropical lake based on Chlorophyll‐a profiler data and Sentinel‐2 images: The onset of an algal bloom event

“…The concentration of Chl‐a and a proxy for turbidity was estimated by applying the MSI imagery from Sentinel‐2 satellite Level 2A products, based on empirical equations that combine reflectance from different bands (Grendaitė et al, 2018; Peppa et al, 2020). The indices used in this study for parameter estimation were (B5‐B6) for Chl‐a and (B4) for turbidity (Cairo et al, 2020; Membrillo‐Abad et al, 2016; Peppa et al, 2020), where B4, B5, and B6 are the bands of reflectance at , , and central wavelengths, respectively, (see for the analysis of the accuracy of the estimations). In the case for the Chl‐a, this index weights the height of the peak of the B5 band against the baseline of its adjacent peaks of either the B4 and/or the B6 spectral bands.…”

“…Because monitoring a lake is an intricate task that demands time and resources, an excellent alternative procedure for lake monitoring is the remote sensing techniques that have been developed and improved in recent years, particularly the use of satellite sensors (Cairo et al, 2020 ; Toming et al, 2016 ). Recently, satellite data has been used to monitor water quality and trophic state based on the concentration of Chl‐a, turbidity, among other parameters.…”

How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?

“…On the other hand, the WHO has established the value of 10 µg chl_a L −1 as the first alert level to freshwater bodies use for recreational activities [44]. In this context, many studies have demonstrated that the returned signature from oligotrophic waterbodies is different compared to that returned from mesotrophic or eutrophic waterbodies [45][46][47], meaning that sensitive spectral regions to water bodies highly laden in chl_a are not necessarily the same for water bodies moderately or poorly laden in chl_a concentrations. As a consequence, using a specific model to each trophic level could enhance the quality of chl_a estimates than using a unique model for all trophic levels [48].…”

Retrieving Inland Reservoir Water Quality Parameters Using Landsat 8-9 OLI and Sentinel-2 MSI Sensors with Empirical Multivariate Regression

“…Reservoirs are also simple targets for waste disposal [5]. The biotic and abiotic variables of these functionally complex ecosystems undergo rapid changes owing to natural environmental changes, changes in watershed land cover and land use, and changes in water demand [6]. The construction and use of reservoirs change the hydrodynamics of rivers, with different impacts on terrestrial and aquatic systems [7].…”