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.
Highlights
IntroductionThey are subject to rapid changes in their biotic and abiotic variables, which are due to the natural variability of the environment, to drainage basin land cover and land-use changes, as well as to water resource demands [1,2]
Reservoirs are transitional systems between rivers and lakes created from the damming of a river.Being complex functioning ecosystems, they are subject to rapid changes in their biotic and abiotic variables, which are due to the natural variability of the environment, to drainage basin land cover and land-use changes, as well as to water resource demands [1,2]
The research methodology comprises the following steps: (i) organization of a database composed of chl-a concentrations up to 1000 mg/m3 and Rrs with glint correction obtained during eight field campaigns at Ibitinga Reservoir (São Paulo/Brazil) which took place in the years of 2005, 2013, 2014, and 2018; (ii) optical classification of chl-a concentration range classes based on the Spectral Angle
Summary
They are subject to rapid changes in their biotic and abiotic variables, which are due to the natural variability of the environment, to drainage basin land cover and land-use changes, as well as to water resource demands [1,2] Their main functions are power generation, domestic supply, flood control, and irrigation [3]. Anthropogenic activities have promoted water eutrophication due to nutrient enrichment caused by domestic and industrial sewage discharges from urban centers and by the runoff from agricultural regions [5] Such high inputs of nutrients over time impair water quality, leading to increases in primary production, frequency of cyanobacteria blooms, and fish mortality [5,6]. For better governance of inland water resources and in order to meet the millennium goals, it is mandatory to improve the monitoring of water quality in reservoirs [8]
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