Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

Abstract

For effective lakes' management, highfrequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m -3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

“…The 6SV-derive reflectance was converted in remote sensing reflectance (RRS) dividing by pi. The reflectance products obtained were previously validated in Bresciani et al (2018) [48].…”

“…RRS products were then used as the input of a non-linear optimization algorithm called BOMBER (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images) [49] for estimating the substrate coverage. The parameterization of the bio-optical model implemented in BOMBER was based on a comprehensive in situ dataset dedicated to sub-alpine lakes [48,50,51]. With respect to Lee et al [29], who described bottom albedo ρ (λ) as the albedo value at a reference wavelength, in this study, the ρ (λ) was expressed as a linear combination of three substrates with different albedo values.…”

Simile, a Geospatial Enabler of the Monitoring of Sustainable Development Goal 6 (Ensure Availability and Sustainability of Water for All)

“…The data considered for the analysis of the water quality are those of the Sentinel 2 and Sentinel 3 satellites. The MSI (MultiSpectral Instrument) sensor of Sentinel 2A and 2B has not been specifically configured for aquatic environments, but recent studies (Bresciani et al, 2018) have shown that it can also be used in such an environment if properly corrected. The advantage is that MSI spatial resolution is very high, varying between 10 and 60 meters.…”

Tracking pollution and its sources in the catchment‐lake system of major waterbodies in Kazakhstan

“…Water quality assessment results are typically presented in the form of tables, graphs and diagrams (Ali, Hamed, & El‐Azim, ; Dembowska et al, ; El‐Kassas & Gharib, ; Klimaszyk, Rzymski, Piotrowicz, & Joniak, ; Ochocka & Pasztaleniec, ), making it sometimes difficult to readily interpret them. Accordingly, an informative method of presenting spatial data is development of statistical maps, which can be used for any data, including physical (Firoz, Laxmi, & Abdul, ), hydrochemical, toxicological (Krupa, Barinova, Tsoy, Lopareva, & Sadyrbaeva, ), biological (Barinova, Krupa, & Kadyrova, ; Bresciani et al, ), evaluative and integrative (Barinova, Bilous, & Ivanova, ; Bilous, Barinova, Ivanova, & Huliaieva, ). Previous research has highlighted the effectiveness of this method for solving theoretical and applied ecology issues (Barinova & Krupa, ; Barinova et al, ; Krupa & Barinova, ; Krupa, Barinova, Amirgaliyev, Issenova, & Kozhabayeva, ; Krupa, Barinova, Assylbekova, & Isbekov, ; Krupa, Barinova, Isbekov, & Assylbekova, ; Krupa, Barinova, Ponamareva, & Tsoy, ; Krupa, Barinova, Tsoy, et al, ).…”