Lakes are inestimable renewable natural resources that are under significant pressure by human activities. Monitoring lakes regularly is necessary to understand their dynamics and the drivers of these dynamics to support effective management. Remote sensing by satellite sensors offers a significant opportunity to increase the spatiotemporal coverage of environmental monitoring programs for inland waters. Lake color is a water quality attribute that can be remotely sensed and is independent of the sensor specifications and water type. In this study we used the Multispectral Imager (MSI) on two Sentinel-2 satellites to determine the color of water of 170 Italian lakes during two periods in 2017. Overall, most of the lakes appeared blue in spring and green-yellow in late summer, and in particular, we confirm a blue-water status of the largest lakes in the subalpine ecoregion. The color and its seasonality are consistent with characteristics determined by geomorphology and primary drivers of water quality. This suggests that information about the color of the lakes can contribute to synoptic assessments of the trophic status of lakes. Further ongoing research efforts are focused to extend the mapping over multiple years.

Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

Phytoplankton and its most common pigment chlorophyll a (Chl-a) are important parameters in characterizing lake ecosystems. We compared six methods to measure the concentration of Chl a (CChl-a) in two optically different lakes: stratified clear-water Lake Saadjärv and non-stratified turbid Lake Võrtsjärv. CChl-a was estimated from: in vitro (spectrophotometric, high-performance liquid chromatography); fluorescence (in situ automated high-frequency measurement (AHFM) buoys) and spectral (in situ high-frequency hyperspectral above-water radiometer (WISPStation), satellites Sentinel-3 OLCI and Sentinel-2 MSI) measurements. The agreement between methods ranged from weak (R2 = 0.26) to strong (R2 = 0.93). The consistency was better in turbid lake compared to the clear-water lake where the vertical and short-term temporal variability of the CChl-a was larger. The agreement between the methods depends on multiple factors, e.g., the environmental and in-water conditions, placement of sensors, sensitivity of algorithms. Also in case of some methods, seasonal bias can be detected in both lakes due to signal strength and background turbidity. The inherent differences of the methods should be studied before the synergistic use of data which will clearly increase the spatial (via satellites), temporal (AHFM buoy, WISPStation and satellites) and vertical (profiling AHFM buoy) coverage of data necessary to advance the research on phytoplankton dynamics in lakes.