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For effective lakes’ management, high-frequent 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.

Under the current high anthropic pressure and climate change scenarios, a trend towards increasing changes in the trophic status of shallow lakes, and the development of opportunistic floating species is to be expected. This raises the need for monitoring and management actions to prevent widespread environmentally negative effects (e.g., anoxia). An efficient approach to monitoring water quality and primary producers in inland waters is to integrate in situ with remote sensing data. In this work, satellite multispectral data acquired by Sentinel-2 A are used to assess the intra-annual spatial and temporal dynamics of phytoplankton abundance, in terms of chlorophyll-a (Chl-a) concentration and macrophyte Leaf Area Index (LAI) in a shallow eutrophic fluvial lake system (Mantua Lakes, Italy). Chl-a concentrations and LAI were derived from Sentinel-2 A data by applying a semi-empirical band ratio algorithm combined with a bio-optical model (BOMBER) for the former (Chl-a), and a semi-empirical model for the latter (LAI). These products were validated against in situ data (rRMSE = 20% for both products; R2 = 0.93 for Chl-a; R2 = 0.83 for LAI). Phytoplankton maps showed a marked intra-annual spatial and temporal variability, generally revealing a Chl-a concentration gradient from lotic to lentic waters. Air temperature was the main driver of Chl-a concentration, followed by water discharge and precipitation. The macrophyte LAI followed aquatic plant growth seasonality, and was independent of the hydro-meteorological data. Allochthonous and invasive macrophyte species (such as Nelumbo nucifera and Ludwigia hexapetala) had higher LAI compared than the Mantua Lakes’ autochthonous floating-leaved species (e.g., Trapa natans and Nuphar lutea). Maps of the abundance of primary producers can be used to follow the temporal and spatial evolution of different communities and support management actions, e.g., by identifying potential algal bloom hotspots, or the optimal timing for measures to control invasive species overgrowth.

In this study we evaluate the capabilities of three satellite sensors for assessing water composition and bottom depth in Lake Garda, Italy. A consistent physics-based processing chain was applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat-8 Operational Land Imager (OLI) and RapidEye. Images gathered on 10 June 2014 were corrected for the atmospheric effects with the 6SV code. The computed remote sensing reflectance (Rrs) from MODIS and OLI were converted into water quality parameters by adopting a spectral inversion procedure based on a bio-optical model calibrated with optical properties of the lake. The same spectral inversion procedure was applied to RapidEye and to OLI data to map bottom depth. In situ measurements of Rrs and of concentrations of water quality parameters collected in five locations were used to evaluate the models. The bottom depth maps from OLI and RapidEye showed similar gradients up to 7 m (r = 0.72). The results indicate that: (1) the spatial and radiometric resolutions of OLI enabled mapping water constituents and bottom properties, (2) MODIS was appropriate for assessing water quality in the pelagic areas at a coarser spatial resolution, and (3) RapidEye had the capability to retrieve bottom depth at high spatial resolution. Future work should evaluate the performance of the three sensors in different bio-optical conditions.

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.