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Abstract Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes.

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

The water resource is severely compromised by climate change, and its availability and quality can no longer be taken for granted, even in places considered pristine, such as mountains. In this study, we evaluated the water colour variability of three artificial mountain lakes located in a relatively small basin (Western Italian Alps) at high elevations, and related this variability to the local climate conditions of the hydrological basin to which they belong. We estimated the dominant wavelength (DW) of lake water from Sentinel-2 acquisitions for the period 2017–2022, performing a chromaticity analysis. We correlated DW with climatic parameters recorded by two automated weather stations. Average DW varies from 497 nm of Serrù Lake and Agnel Lake to 512 nm of Lake Ceresole, where DW varies seasonally (variation of 75–100 nm in one year). During April–July, the DW of Lake Ceresole is significative correlated with air temperatures and snow cover (−0.8 and +0.8, respectively). During August–October, the relationship with temperature decreases to −0.5, and a correlation of 0.5 with the amount of rainfall appears. This work shows that mountain lake waters can exhibit variable quality (expressed here by water colour) in response to meteorological and hydrological conditions and events.

Abstract The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

A study was carried out to investigate the effects of wildfires on lake water quality using a source dataset of 2024 lakes worldwide, covering different lake types and ecological settings. Satellite-derived datasets (Lakes_cci and Fire_cci) were used and a Source Pathway Receptor approach applied which was conceptually represented by fires (burned area) as a source, precipitation/drought representing transport dynamics, and lakes as the ultimate receptor. This identified 106 lakes worldwide that are likely prone to be impacted by wildfires via a terrestrial pathway. Satellite-derived chlorophyll-a (Chl-a) and turbidity variables were used as indicators to detect changes in lake water quality potentially induced by wildfires over a four-year period. The lakes with the largest catchment areas burned and characterized by regular annual fires were located in Africa. Evidence for a strong influence of wildfires was not found across the dataset examined, although clearer responses were seen for some individual lakes. However, among the hydro-morphological characteristics examined, lake depth was found to be significant in determining Chl-a concentration peaks which were higher in shallow and lower in deep lakes. Lake turbidity responses indicated a dependence on lake catchment and weather conditions. While wildfires are likely to contribute to the nutrient load of lakes as found in previous studies, it is possible that in many cases it is not a dominant pressure and that its manifestation as a signal in lake Chl-a or turbidity values depends to a large part on lake typology and catchment characteristics. Assessment of lake water quality changes six months after a fire showed that Chl-a concentrations either increased, decreased, or showed no changes in a similar number of lakes, indicating that a lake specific ecological and hydro-morphological context is important for understanding lake responses to wildfires.

Over the past half century, the demand for sand and gravel has led to extensive quarrying activities, creating many pit lakes (PLs) which now dot floodplains and urbanized regions globally. Despite the potential importance of these environments, systematic data on their location, morphology and water quality remain limited. In this study, we present an extensive assessment of the physical and optical properties in a large sample of PLs located in the Po River basin (Italy) from 1990 to 2021, utilizing a combined approach of remote sensing (Landsat constellation and Sentinel-2) and traditional limnological techniques. Specifically, we focused on the concentration of Suspended Particulate Matter (SPM) and the dominant wavelength (λdom, i.e., water colour). This study aims to contribute to the analysis of PLs at a basin scale as an opportunity for environmental rehabilitation and river floodplain management. ACOLITE v.2022, a neural network particularly suitable for the analysis of turbid waters and small inland water bodies, was used to atmospherically correct satellite images and to obtain SPM concentration maps and the λdom. The results show a very strong correlation between SPM concentrations obtained in situ and those obtained from satellite images, both for data derived from Landsat (R2 = 0.85) and Sentinel-2 images (R2 = 0.82). A strong correlation also emerged from the comparison of spectral signatures obtained in situ via WISP-3 and those derived from ACOLITE, especially in the visible spectrum (443–705 nm, SA = 10.8°). In general, it appeared that PLs with the highest mean SPM concentrations and the highest mean λdom are located along the main Po River, and more generally near rivers. The results also show that active PLs exhibit a poor water quality status, especially those of small sizes (dom: higher values in winter gradually decreasing until spring–summer, then increasing again. Finally, it emerged that the end of quarrying activity led to a reduction in SPM concentration from a minimum of 43% to a maximum of 72%. In this context, the combined use of Landsat and Sentinel-2 imagery allowed for the evaluation of the temporal evolution of the physical and optical properties of the PLs in a vast area such as the Po River basin (74,000 km2). In particular, the Sentinel-2 images consistently proved to be a reliable resource for capturing episodic and recurring quarrying events and portraying the ever-changing dynamics of these ecosystems.

Accurate determination of the water depth and benthic macroalgae composition in coastal and inland water bodies is important due to the high commercial and ecological value of these regions. Benthic habitat mapping by conventional methods provides good accuracy, but these methods are very expensive and limited by manpower and time factor, which is necessary for mapping large areas. Remote sensing methods significantly complement contact measurements and give additional information about the hard-to-reach areas. The usefulness of free Sentinel-2 data in bathymetry and habitat mapping has been demonstrated in clear oceanic waters. The aim of this study was to further test the suitability of Sentinel-2 imagery in creating maps of dominant benthic types, as well as in estimating bathymetry in optically complex marine and lake waters. Two study sites were selected to cover a representative range of optical variability - Lake Garda in northern Italy (an intermediate between clear ocean and optically very complex waters) and Viimsi peninsula on the Estonian side of the Gulf of Finland, in the Baltic Sea. The results show that Sentinel-2 imagery with 10 m spatial resolution is suitable for bathymetry and habitat mapping in optically complex inland and coastal waters. Our results show that bathymetry mapping is sufficiently accurate in waters less than 4 m deep in the case of the Baltic Sea and up to 7 m deep in Lake Garda. In such depths, the R2 was above 0.93 in all four Sentinel-2 images used in the study. Bottom type mapping accuracy was in all cases over 73%, which is considered to be good, but due to the limited number of sampling points in both test sites, further studies are needed. The Sentinel-2 data quality and no cost of the imagery for users make it very useful for mapping bathymetry and shallow water habitats over large coastal areas or high number of lakes, especially in hard to reach by in situ methods areas. Moreover, the frequent revisit time allows moving from one-off maps to monitoring of temporal changes happening in dynamic shallow inland and coastal waters.

Lakes have been observed as sentinels of climate change. In the last decades, global warming and increasing aridity has led to an increase in both the number and severity of wildfires. This has a negative impact on lake catchments by reducing forest cover and triggering cascading effects in freshwater ecosystems. In this work we used satellite remote sensing to analyse potential fire effects on lake water quality of Lake Baikal (Russia), considering the role of runoff and sediment transport, a less studied pathway compared to fire emissions transport. The main objectives of this study were to analyse time series and investigate relationships among fires (i.e., burned area), meteo-climatic parameters and water quality variables (chlorophyll-a, turbidity) for the period 2003–2020. Because Lake Baikal is oligotrophic, we expected detectable changes in water quality variables at selected areas near the three mains tributaries (Upper Angara, Barguzin, Selenga) due to river transport of fire-derived burned material and nutrients. Time series analysis showed seasonal (from April to June) and inter-annual fire occurrence, precipitation patterns (high intensity in summer) and no significant temporal changes for water quality variables during the studied periods. The most severe wildfires occurred in 2003 with the highest burned area detected (36,767 km2). The three lake sub-basins investigated have shown to respond differently according to their morphology, land cover types and meteo-climatic conditions, indicating their importance in determining the response of water variables to the impact of fires. Overall, our finding suggests that Lake Baikal shows resilience in the medium-long term to potential effects of fires and climate change in the region.

Compounded weather events such as sequential heatwaves are likely to increasingly impact freshwater ecosystems in the future. Satellite-derived chlorophyll-a concentration estimates for 36 European lakes during a widespread double heatwave event in the summer of 2019 show that deep and medium depth lakes at higher latitudes displayed a synchronous chlorophyll-a increase with temperature, possibly as the result of an improved light climate resulting from increased stratification. Many deep or northern lakes had a notable response to the heatwaves. Warmer, southern shallow lakes had the most asynchronous response, tending to show a greater response to subsequent low pressure or storm events than to the heatwave itself. Chlorophyll-a peaks typically occurred five days after the peak of the heatwave for shallow lakes. For some shallow lakes, the sequential cycle of several heatwaves and low pressure events was found to punctuate the seasonal pattern of chlorophyll-a. Notably, in several of these nutrient-rich lakes the response to the heatwave was dwarfed by large algal blooms occurring later during the typical cyanobacterial bloom period in early autumn, underlining the importance of timing and phenology in response to heatwaves in addition to depth, latitude and trophic state.

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.

With the rapid development of economy, many lakes in Wuhan have been polluted to different degrees and suffer from eutrophication. The main objective of this study was to conduct the monthly trophic state assessments of waters in Wuhan from March 2019 to June 2020 using 111 Sentinel-2 images. The Forel-Ule index (FUI) and empirical Gaussian process regression (GPR) were, respectively, applied to obtain monthly area percentage (AP) of waters with each trophic state. Both FUI-derived and empirical GPR-retrieved results showed that majority of water bodies (>90%) in Wuhan were in mesotrophic and eutrophic states. The GPR-retrieved results based on FUI-derived water types were more reliable than the retrieval without classification, which reduced RMSE of trophic-level index (TLI) from 9.2 to 5.8, and MAPE from 14% to 9% (N = 213). Severe eutrophication occurred in the summer and early autumn (June–October). Stepwise multiple linear regression analysis indicated that temperature and wind speed were the two most important meteorological factors influencing eutrophication variability: the temperature accounted for 63% and 55% dynamic eutrophication from FUI-derived and GPR-retrieved results, respectively; the wind speed explained 44% and 52% variability of FUI-derived and GPR-retrieved results.

It is increasingly important to know the water quality of a reservoir, given the prospect of an environment poor in water reserves, which are based on intense and short-lived precipitation events. In this work, vegetation indices (NDVI, EVI) and bio-physical parameters of the vegetation (LAI, FC), meteorological variables, and hydrological data are considered as possible drivers of the spatial and temporal variability of water quality (WQ) of the Banja reservoir (Albania). Sentinel-2 and Landsat 8/9 images are analyzed to derive WQ parameters and vegetation properties, while the HYPE model provides hydrological variables. Timeseries of the considered variables are examined using graphical and statistical methods and correlations among the variables are computed for a five-year period (2016–2022). The added-value of integrating earth observation derived data is demonstrated in the analysis of specific time periods or precipitation events. Significant positive correlations are found between water turbidity and hydrological parameters such as river discharge or runoff (0.55 and 0.40, respectively), while negative correlations are found between water turbidity and vegetation descriptors (−0.48 to −0.56). The possibility of having easy-to-use tools (e.g., web portal) for the analysis of multi-source data in an interactive way, facilitates the planning of hydroelectric plants management operations.

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km2) freshwater dammed reservoirs. Located in Sardinia (Italy) and Crete (Greek), respectively, Mulargia and Aposelemis represent vital resources to supply drinking water in downstream valleys. A total of 400 cloud-free satellite images were turned into information on water quality by using an image processing chain implementing physically based methods for retrieving chlorophyll-a concentration (Chl-a), turbidity, Secchi disk depth (SDD) and surface water temperature. These estimates have been successfully validated (the lower Pearson correlation r was 0.88 for Chl-a) with 23 match-ups of in situ and satellite data. Results of the multi-temporal analyses showed a decrease of SDD due to the increase of Chl-a in Aposelemis or an increase of turbidity in Mulargia. For both freshwater reservoirs, the satellite-derived trophic state index assigned both lakes to mesotrophic conditions. The results finally suggested the effectiveness of S2 and Landsat in increasing, for the latest investigated years, the frequency of observations.

Freshwater ecosystems are challenged by cultural eutrophication across the globe, and it is a priority for water managers to implement water quality monitoring at different spatio-temporal scales to control and mitigate the eutrophication process. Phytoplankton abundance is a key indicator of the trophic and water quality status of lakes. Phytoplankton dynamics are characterized by high spatio-temporal variation, driven by physical, chemical and biological factors, that challenge the capacity of routine monitoring with conventional sampling techniques (i.e., boat based sampling) to characterise these complex relationships. In this study, high frequency in situ measurements and multispectral satellite data were used in a synergistic way to explore temporal (diurnal and seasonal) dynamics and spatial distribution of Chlorophyll-a (Chl-a) concentration, a proxy of phytoplankton abundance, together with physico-chemical water parameters in a shallow fluvial-lake system (Mantua Lakes). A good agreement was found between Chl-a retrieved by remote sensing data and Chl-a fluorescence data recorded by multi-parameters probes (R2 = 0.94). The Chl-a maps allowed a seasonal classification of the Mantua Lakes system as eutrophic or hypertrophic. Along the Mantua lakes system an increasing gradient in Chl-a concentration was recorded following the transition from a fluvial to lacustrine system. There was significant seasonal heterogeneity among the sub-basins, probably due to different hydrodynamics, influenced also by macrophyte stands. High-frequency data revealed the importance of rainfall events in the timing and growth dynamics of phytoplankton, particularly for spring and late summer blooms. Combining temporal and spatial data at high resolution improves the understanding of complex fluvial-lake systems. This technique can allow managers to target blooms in near-real time as they move through a system and guide them to localized hot spots enabling timely management action in ecosystems of high conservation and recreational value.

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2 = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption.

With the increase of evaporation projected for water bodies worldwide, there is a growing need for flexible and low data-demanding tools enabling the monitoring and management of water resources. This study presents a simple satellite-based tool named LakeVap specifically designed for mapping evaporation from lakes and reservoirs. LakeVap requires a small amount of potentially available data with a global coverage. The tool follows a Dalton-type approach and produces instantaneous (i.e., hourly) and daily evaporation maps from satellite-derived Lake Surface Water Temperature (LSWT) maps and single-point/gridded meteorological data. The model is tested on Lake Garda, Italy, by using a long time series of LSWT (ESA CCI-Lakes) and different sources of meteorological forcing. The accuracy of LakeVap evaporation outputs is checked by comparison with those from a hydro-thermodynamic model (Delft3D) specifically set up and validated for the case study. Results are consistent and sensitive to the representativeness of the meteorological forcing. In the test site, wind speed is found to be the most spatially variable parameter, and it is significantly underestimated by the ERA5 meteorological dataset (up to 100%). The potential application of LakeVap to other case studies and in operational contexts is discussed.

The interaction between microbial communities and benthic algae as nitrogen (N) regulators in poorly illuminated sediments is scarcely investigated in the literature. The role of sediments as sources or sinks of N was analyzed in spring and summer in sandy and muddy sediments in a turbid freshwater estuary, the Curonian Lagoon, Lithuania. Seasonality in this ecosystem is strongly marked by phytoplankton community succession with diatoms dominating in spring and cyanobacteria dominating in summer. Fluxes of dissolved gas and inorganic N and rates of denitrification of water column nitrate (Dw) and of nitrate produced by nitrification (Dn) and sedimentary features, including the macromolecular quality of organic matter (OM), were measured. Shallow/sandy sites had benthic diatoms, while at deep/muddy sites, settled pelagic microalgae were found. The OM in surface sediments was always higher at muddy than at sandy sites, and biochemical analyses revealed that at muddy sites the OM nutritional value changed seasonally. In spring, sandy sediments were net autotrophic and retained N, while muddy sediments were net heterotrophic and displayed higher rates of denitrification, mostly sustained by Dw. In summer, benthic oxygen demand increased dramatically, whereas denitrification, mostly sustained by Dn, decreased in muddy and remained unchanged in sandy sediments. The ratio between denitrification and oxygen demand was significantly lower in sandy compared with muddy sediments and in summer compared with spring. Muddy sediments displayed seasonally distinct biochemical composition with a larger fraction of lipids coinciding with cyanobacteria blooms and a seasonal switch from inorganic N sink to source. Sandy sediments had similar composition in both seasons and retained inorganic N also in summer. Nitrogen uptake by microphytobenthos at sandy sites always exceeded the amount loss via denitrification, and benthic diatoms appeared to inhibit denitrification, even in the dark and under conditions of elevated N availability. In spring, denitrification attenuated N delivery from the estuary to the coastal area by nearly 35%. In summer, denitrification was comparable (~100%) with the much lower N export from the watershed, but N loss was probably offset by large rates of N-fixation.

We investigate the effect of the Adige-Garda spillway opening on the 03/03/2020 on Lake Garda using numerical modelling and maps of Suspended Particulate Matter (SPM) concentration. SPM maps are obtained from Sentinel-2 images processed using the BOMBER bio-optical model. Three satellite images are examined: 28/02/2020, 04/03/2020 and 07/03/2020. Maps indicate a significant increase in SPM concentrations, especially in the northern part of the lake close to the hydraulic tunnel outlet. Results are consistent with the modelled flow field. Remote sensing effectively captures the event’s spatial and temporal variation, while numerical modelling explains and corroborates the observed patterns.

Macrophytes are of fundamental importance to the functioning of lake ecosystems. They provide structure, habitat, and a food source and are a required component in monitoring programs of lake ecological quality. The key aim of this study is to document the variation in spatial extent and density of macrophytes seasonally between 2015 and 2020 of the Sirmione Peninsula (Lake Garda, Italy), using Sentinel-2 imagery. In addition to this, our results were compared to previous data from imaging spectrometry; individual parameters affecting macrophyte communities were tested, and the possible effect of the COVID-19 lockdown on macrophyte colonization was evaluated. Satellite images allowed the mapping of the spatiotemporal dynamics of submerged rooted macrophytes in order to support monitoring of the shallow water ecosystem under study. Substantial changes were found in both spatial extent and density over the period from 2015 to 2020, particularly in 2019 when there was almost a complete absence of dense macrophytes. Variables found to influence the amount of macrophytes included transparency, chlorophyll–a, water level, winter wave height, and grazing by herbivores. A separate analysis focusing on areas associated with boat transit found a recovery in macrophyte coverage during the period of COVID-19 lockdown. The outcome of the study highlights a decline in the density of the macrophytes and a shift towards deeper areas compared to the situation in 1997. The area examined is part of an internationally important site containing the highest abundance and diversity of overwintering water birds in Italy. Exploiting satellite data at high frequency provided an insight to understand the dynamic changes and interactions with herbivorous birds, environmental factors, and anthropogenic pressures, revealing a delicately balanced and threatened ecosystem.

Aquatic vegetation loss caused substantial decrease of ecosystem processes and services during the last decades, particularly for the capacity of these ecosystems to sequester and store carbon from the atmosphere. This study investigated the extent of aquatic emergent vegetation loss for the period 1985–2018 and the consequent effects on carbon sequestration and storage capacity of Valle Santa wetland, a protected freshwater wetland dominated by Phragmites australis located in the Po river delta Park (Northern Italy), as a function of primary productivity and biomass decomposition, assessed by means of satellite images and experimental measures. The results showed an extended loss of aquatic vegetated habitats during the considered period, with 1989 being the year with higher productivity. The mean breakdown rates of P. australis were 0.00532 d−1 and 0.00228 d−1 for leaf and stem carbon content, respectively, leading to a predicted annual decomposition of 64.6% of the total biomass carbon. For 2018 the carbon sequestration capacity was estimated equal to 0.249 kg C m−2 yr−1, while the carbon storage of the whole wetland was 1.75 × 103 t C (0.70 kg C m−2). Nonetheless, despite the protection efforts over time, the vegetation loss occurred during the last decades significantly decreased carbon sequestration and storage by 51.6%, when comparing 2018 and 1989. No statistically significant effects were found for water descriptors. This study demonstrated that P. australis-dominated wetlands support important ecosystem processes and should be regarded as an important carbon sink under an ecosystem services perspective, with the aim to maximize their capacity to mitigate climate change.

The monitoring of water bio-physical parameters and the management of aquatic ecosystems are crucial to cope with the current state of inland water degradation. Not only does water quality monitoring support management decision making, it also provides vital insights to better understand changing structural and functional lake processes. Remote sensing has been widely recognized as an essential integrating technique for water quality monitoring, thanks to its capabilities to utilize both historical archive data for thousands of lakes as well as near-real time observations at multiple scales. To date, most of the applications developed for inland water have been based on multispectral and mid to coarse spatial resolution satellites, while a new generation of spaceborne imaging spectroscopy is now available, and future missions are under development. This review aims to present the exploitation of data gathered from two currently orbiting hyperspectral sensors (i.e., PRISMA and DESIS) to retrieve water quality parameters across different aquatic ecosystems, encompassing deep clear lakes and river dammed reservoirs.

The frequency of heatwave events in Europe is increasing as a result of climate change. This can have implications for the water quality and ecological functioning of aquatic systems. We deployed three spectroradiometer WISPstations at three sites in Europe (Italy, Estonia, and Lithuania/Russia) to measure chlorophyll-a at high frequency. A heatwave in July 2019 occurred with record daily maximum temperatures over 40 °C in parts of Europe. The effects of the resulting storm that ended the heatwave were more discernable than the heatwave itself. Following the storm, chlorophyll-a concentrations increased markedly in two of the lakes and remained high for the duration of the summer while at one site concentrations increased linearly. Heatwaves and subsequent storms appeared to play an important role in structuring the phenology of the primary producers, with wider implications for lake functioning. Chlorophyll-a peaked in early September, after which a wind event dissipated concentrations until calmer conditions returned. Synoptic coordinated high frequency monitoring needs to be advanced in Europe as part of water management policy and to improve knowledge on the implications of climate change. Lakes, as dynamic ecosystems with fast moving species-succession, provide a prism to observe the scale of future change.

Harmful cyanobacteria blooms (HCBs) are one of the main water quality threats affecting reservoirs. Guidelines suggest integrating laboratory, real-time in situ, and remote sensing data in the monitoring of HCBs. However, this approach is still little adopted in institutional measuring programs. We demonstrated that this integration improves frequency and spatial resolution of the data collection. Data were from an intense HCB (Planktothrix rubescens), which occurred in a south Italy multiple-uses reservoir (Lake Occhito) between 2008 and 2009 and regarded both the lake and the irrigation network. Laboratory and in situ fluorometric data were related to satellite imagery, using simple linear regression models, to produce surface lake-wide maps reporting the distribution of both P. rubescens and microcystins. In the first node of the distribution network, microcystin concentrations (4–10 µg L−1) reached values potentially able to damage the culture and to accumulate during cultivation. Nevertheless, our study shows a decrease in the microcystin content with the distance from the lake (0.05 µg L−1 km−1), with a reduction of about 80% of the microcystin concentrations at the furthest tanks. Recent improvements in the spatial resolution (i.e., tens of meters) of satellite imagery allow us to monitor the main tanks of large and complex irrigation systems.

The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 > 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.

This study applied remote sensing techniques to the study of water color in Himalayan glacial lakes as a proxy of suspended solid load. In situ measurements gathered in 5 lakes in October 2014 during satellite data acquisition enabled the characterization of water reflectance and clarity and supported image processing. Field data analysis led to a distinction between 3 water colors and a consequent lake water color classification on a regional scale from Landsat-8 data previously corrected for atmospheric and adjacency effects. Several morphometric parameters (lake size and shape, distance between lake and glacier) were also computed for the lakes thus classified. The results showed spatial and temporal variations in lake water color, suggestive of relationships between glacier shrinkage and the presence of brighter and more turbid water. A finer-scale analysis of the spatial variability of water reflectance on Chola Lake (based on GeoEye-1 data captured on 18 October 2014) showed the contribution of water component absorption from the inflow. Overall, the findings support further research to monitor Himalayan lakes using both Landsat-8 and Sentinel-2 (with its improved resolutions).

The relationship between invasive plant functional traits and their invasiveness is still the subject of scientific investigation, and the backgrounds of transition from non-native to invasive species in ecosystems are therefore poorly understood. Furthermore, our current knowledge on species invasiveness is heavily biased toward terrestrial species; we know much less about the influence of allochthonous plant traits on their invasiveness in aquatic ecosystems. In this paper, we present the results of a study on physiological and ecological traits of two introduced and three native macrophyte species in the Mantua lakes system (northern Italy). We compared their photophysiology, pigment content, leaf reflectance, and phenology in order to assess how the invasive Nelumbo nucifera and Ludwigia hexapetala perform compared to native species, Nuphar lutea, Nymphaea alba, and Trapa natans. We found L. hexapetala to have higher photosynthetic efficiency and to tolerate higher light intensities than N. nucifera and the native species especially at extreme weather conditions (prolonged exposure to high light and higher temperatures). Chlorophyll a and b, and carotenoids content of both allochthonous species were substantially higher than those of native plants, suggesting adaptive response to the ecosystem of Mantua lakes system. Higher variability of recorded data in invasive species was also observed. These observations suggest advanced photosynthetic efficiency of the invasive species, especially L. hexapetala, resulting in faster growth rates and higher productivity. This was supported by the evaluation of seasonal dynamics mapped from satellite remote sensing data. This study provides empirical evidence for the relationship between specific plant physiological traits and invasiveness of aquatic plant species, highlighting the importance of trait studies in predicting ecosystem-level impacts of invasive plant species.

The Curonian Lagoon is Europe’s largest lagoon and one of the most seriously impacted by harmful blooms of cyanobacteria. Intensive studies over the past 20 years have allowed us to identify the major drivers determining the composition and spatial extent of hyperblooms in this system. We summarize and discuss the main outcomes of these studies and provide an updated, conceptual scheme of the multiple interactions between climatic and hydrologic factors, and their influence on internal and external processes that promote cyanobacterial blooms. Retrospective analysis of remote sensed images demonstrated the variability of blooms in terms of timing, extension and intensity, suggesting that they occur only under specific circumstances. Monthly analysis of nutrient loads and stoichiometry from the principal tributary (Nemunas River) revealed large interannual differences in the delivery of key elements, but summer months were always characterized by a strong dissolved inorganic N (and Si) limitation, that depresses diatoms and favors the dominance of cyanobacteria. Cyanobacteria blooms occurred during high water temperatures, long water residence time and low-wind conditions. The blooms induce transient (night-time) hypoxia, which stimulates the release of iron-bound P, producing a positive feedback for blooms of N-fixing cyanobacteria. Consumer-mediated nutrient recycling by dreissenid mussels, chironomid larvae, cyprinids and large bird colonies, may also affect P availability, but their role as drivers of cyanobacteria blooms is understudied.