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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.

Additional file 10. Metabolome-proteome correlation (Pearson). Results for Fatty acids C6 onwards .

Additional file 8. Metabolome-proteome correlation (Pearson). Results for Guanosine derivate.

Additional file 9. Metabolome-proteome correlation (Pearson). Results for Arabinose .

This thesis provides new directions in the impedance spectroscopy, making it an interesting investigation technique for emerging smart sensors. Modern technologies increasingly require sensors capable of giving accurate measurements extracted among a lot of undesired surrounding information while maintaining low power consumptions. In this scenario, the main focuses of this thesis are: 1) developing an accurate complex impedance system, 2) extracting the augmented information using multivariate statistical analysis and 3) implementing IS-based systems with low power consumptions. The first project shows the design of a miniaturized, low power and accurate vector analyser for multi-parameter measurements in real-time. It is a versatile platform well-suited to be interfaced with various impedance-based sensors. The vector analyser, based on an accurate application specific integrated circuit and a digital interface, has been statistically characterized in order to evaluate accuracy and resolution. The validation of the entire system was performing on two real-time biomedical applications. The second project concerns the combination of powerful statistical methods inside moisture content sensors. The multivariate statistical approaches boost the prediction capability of the sensors exploiting the impedance mismatch between a transmitting and reflecting excitation on a soil. Two probe systems have been manufactured and associated with linear and non-linear models for being tested on three soil types. The third project shows a low-power implementation of an impedance sensor based on a digital random excitation. The entire system is almost digital, made up by an ultra-low power platform with the aim to become a wearable device. In future developments, these new investigated directions can be simultaneously applied in the design of IS based sensors which extract the desired information with high accuracy and reduced power budget. The potential of such improved system can be employed in a lot of smart sensors, involving electrochemical, environmental, food, biological applications and wearable devices.

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at dierent temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly oers an unprecedented resource in the research and management of aquatic resources.

Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at dierent temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly oers an unprecedented resource in the research and management of aquatic resources.

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A “ready to use” approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.

Climate change has increased the temperature and altered the mixing regime of high-value lakes in the sub-alpineregion of Northern Italy. Remote sensing of chlorophyll-a can help provide a time-series to allow an assessment of the ecological implications of this. Non-parametric multiplicative regression (NPMR) was used to visualize and understand the changes that have occurred between 2003-2018 in lakes Garda, Como, Iseo and Maggiore. In all four deep sub-alpine lakes there has been a disruption from a traditional pattern of a significant spring chlorophyll-a peak followed by a clear water phase and summer/autumn peaks. This was replaced after 2010-2012, with lower spring peaks and a tendency for annual maxima to occur in summer. There was a tendency for this switch to be interspersed by a two-year period of low chlorophyll-a, which seemed to extend until 2018 for Lake Garda. Variables that were significant in NPMR included time, air temperature, wind speed, cloud cover, winter temperature and winter values for the North Atlantic Oscillation and Eastern Atlantic pattern. The change from spring to summer chlorophyll-a maxima, relatively sudden in an ecological context, could be interpreted as a regime shift. The cause is probably cascading effects from increased winter temperatures,reduced winter mixing and altered nutrient dynamics. Future trends will depend on climate change and inter-decadal climate drivers.

Phytoplankton blooms in internal water bodies are an unpleasant sight that often emerges on top like a layer of foam containing high concentrations of toxins (scum event). Monitoring the concentration of algae and the occurrence of scum in lakes and lagoons has become a topic of interest for management and science. Optical remote sensing is a validated tool but unfortunately it is highly hindered by clouds. For regions with frequent cloud cover, such as the Baltic region, this means loss of data, which limits the purpose of sensing to spatially and temporally characterize any scum for a comprehensive ecological analysis. The aim of this paper is to investigate whether the use of synthetic aperture radar (SAR) images can compensate for the weaknesses of optical images for cyanobacteria bloom monitoring purposes in the event of cloudy skies. A “ready to use” approach to detect cyanobacteria bloom in the Curonian Lagoon based on the level 2 ocean product of Sentinel-1 images is proposed. This method is empirically validated for the images of summer/autumn 2018 of the Curonian Lagoon.

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.