Abstract
Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.
Highlights
Inland water bodies comprise large networks of geographically distributed hydrologic freshwater ecosystems, which integrate their surrounding terrestrial area and are sites of intense biogeochemical activity [1]
We evaluated several algorithms for the estimation of chl-a concentration in riverconnected lakes using hyperspectral, simulated Sentinel-2/MultiSpectral Instrument (MSI) signal and a single Sentinel2/MSI image
We used chl-a concentrations measured via high-performance liquid chromatography (HPLC) from 19 lakes in NorthEast Germany collected from June to October 2019
Summary
Inland water bodies comprise large networks of geographically distributed hydrologic freshwater ecosystems, which integrate their surrounding terrestrial area and are sites of intense biogeochemical activity [1]. They are sentinel systems for the response of the entire watershed to climate variation [2]. Neither laboratory- nor probe-based water quality assessments can readily capture the spatial heterogeneity combined with the temporal dynamics of water quality [12] To quantify these dynamics, remote sensing techniques have been developed to monitor proxies for water quality parameters, including chl-a ([8,13,14]) and water transparency [15,16] in inland water systems. Current remote sensing techniques are useful tools for water quality monitoring, enabling a time-saving and cost-effective water resource management [12]
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