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Mineral dust aerosol is generated by Aeolian erosion of soil in desert areas. Due to its origin, it is rich in nutrients and trace metals (e.g. Fe, Si, Al, Mg) useful for phytoplankton growth. Several studies show dust deposition in oligotrophic stratified waters, could induce algal blooms. They have been conducted on oceans and Mediterranean Sea waters, and remote lakes, exploiting Remote Sensing data and techniques. These techniques allow in fact a frequent collection of data with a synoptic point of view on the study area, at relative low costs. In the activities of LTER CNR Sirmione station and SINOPIAE project framework, the effect of dust deposition was inspected on deep clear meso-oligotrophic Lake Garda. For this analysis, different optical satellites images (e.g. MERIS, MODIS) were exploited to retrieve chlorophyll-a concentration (chl-a), proxy of phytoplankton abundance: a processing chain for the retrieval of chl-a values was calibrated and validated. Chl-a is in fact retrieved through empirical or bio-optical models exploiting water reflectance. This physical quantity is not directly measured by sensors, but it can be retrieved from their products through the removal of the atmospheric contribution to the signal and other disturbing factors (e.g. specular reflection and adjacency effect). For MERIS imagery, this operation has been conducted through BEAM-C2R Neural Network, which allows the retrieval of both reflectance and chl-a values, while for MODIS and LANDSAT imagery, 6SV and bio-optical model BOMBER were used. Aerosol parameters retrieved by AERONET sunphotometers in Northern Italy (the last one installed in 2014 in Sirmione) were exploited for both image atmospheric correction and dust deposition events individuation. Aerosol can in fact be characterized by some optical and physical properties (e.g. AOD, refractive index, particle size distribution) which allow to infer their origin. Once the events had been individuated, chl-a concentration in the time-window comprising each deposition event, was analyzed. To assess the actual contribution of deposition to possible chl-a increase, other influencing factors were analyzed (i.e. temperature, to which phytoplankton growth is positive correlated, precipitation, which could induce both wet deposition and the run-off of nutrients previously deposited along the coast). For this purpose an user friendly interface was realized to allow easy identifying anomalies in chl-a from time series of chl-a maps. In particular, the tool allows classifying waters on the basis of some chl-a threshold based on WFD criteria, identifying anomalies in chl-a long time series and evaluating the area where dust deposition could have mainly affected chl-a anomalies, considering both temperature and precipitation. Further progressions of this study include the validation of a new processing chain for the new-generation sensors, Sentinel-2 and Sentinel-3, from which some preliminary results are thus presented

Freshwater ecosystems underpin global water and food security, yet are some of the most endangered ecosystems in the world because they are particularly vulnerable to land management change and climate variability. The US National Research Council's guidance to NASA regarding missions for the coming decade includes a polar orbiting, global mapping hyperspectral satellite remote sensing mission, the Hyperspectral Infrared Imager (HyspIRI), to make quantitative measurements of ecosystem change. Traditionally, freshwater ecosystems have been challenging tomeasure with satellite remote sensing because they are small and spatially complex, require high fidelity spectroradiometry, and are best describedwith biophysical variables derived from high spectral resolution data. In this study, we evaluate the contribution of a hyperspectral global mapping satellite mission to measuring freshwater ecosystems. We demonstrate the need for such a mission, and evaluate the suitability and gaps, through an examination of the measurement resolution issues impacting freshwater ecosystem measurements (spatial, temporal, spectral and radiometric). These are exemplified through three case studies that use remote sensing to characterize a component of freshwater ecosystems that drive primary productivity. The high radiometric quality proposed for the HyspIRImission makes it uniquely well designed for measuring freshwater ecosystems accurately at moderate to high spatial resolutions. The spatial and spectral resolutions of the HyspIRI mission are well suited for the retrieval of multiple biophysical variables, such as phycocyanin and chlorophyll-a. The effective temporal resolution is suitable for characterizing growing season wetland phenology in temperate regions, but may not be appropriate for tracking algal bloom dynamics, or ecosystem responses to extreme events inmonsoonal regions. Global mappingmissions provide the systematic, repeated measurements necessary tomeasure the drivers of freshwater biodiversity change. Archival globalmapping missionswith open access and free data policies increase end user uptake globally. Overall, an archival, hyperspectral global mapping mission uniquely meets the measurement requirements of multiple end users for freshwater ecosystem science and management.

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.

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.

Accurate atmospheric correction products in inland water are required to retrieve water leaving reflectance and water quality parameters from Remote Sensing: a good estimate of water reflectance is mandatory to feed bio-optical models or empirical algorithms for the retrieval of concentrations of water constituents. The water leaving reflectance of lakes is also a new Essential Climate Variable and accurate estimates of this quantity are required by the Climate Change Initiative. The atmospheric correction algorithms used in this study and applied to Sentinel-2-MSI (S2) images are those recently proposed by EU projects (e.g. GLaSS, INFORM): a 6SV-based tool, ATCOR, SNAP-Sen2cor and ACOLITE. They were evaluated by using radiometric measurements gathered in few Italian lakes (i.e. Garda, Iseo, Maggiore, and Mantua fluvial lakes) with varying trophic conditions. A total of 9 images with corresponding 40 stations of in situ data were available. The images processing was accomplished as follow. The 6SV-based tool was used with standard parametrization, with fixed aerosol type for all the images and with Aerosol Optical Thickness (AOT) retrieved from AERONET sun-photometers measurements, or with AERONET aerosol microphysical properties for aerosol definition. ATCOR was run with \'Rural\' aerosol type and visibility and water vapour fixed (with the same values used in the 6SV-based tool) or image-based estimated and variable within the scene. SNAP-Sen2cor was run with a \'Rural\' aerosol type and with image-based estimated visibility. ACOLITE was used with SWIR bands combination, with bands ratio ? for aerosol contribution varying pixel-per-pixel. In few cases, Landsat-8-OLI (L8) images were acquired on the same day of S2, providing further data to be compared to S2. When compared to in situ data, the results on oligo-mesotrophic lakes, showed that the best Rrs estimate was performed by ACOLITE (mean ?2 was 0.04 and 0.02 respectively for Garda and Iseo lakes, taking into account the first nine S2 bands). For the more turbid productive waters of Mantua lakes, the best results in terms of Rrs estimate were obtained through 6SV-based tool and by ATCOR. Similar results were also obtained with L8 data. Then, by comparing S2 and L8 data acquired on the same days, the best match to field data was obtained for ACOLITE-S2 in Lake Iseo and with ATCOR-L8 in Mantua lakes. The results of this study are showing that for clearer waters (as those of Garda and Iseo lakes) the water-oriented processor ACOLITE is performing better than the land-oriented processors (such as ATCOR) that instead are performing well in more productive eutrophic waters. Nevertheless, further investigations are necessary to both augment the number of match-ups and to evaluate the uncertainties that is also associated to radiometric measurements gathered in the field