Plant diversity underpins wetland ecosystem stability and functional sustainability, and its reliable assessment is vital for effective conservation and management. Remote sensing provides an efficient mean of plant diversity monitoring, however the potential of remotely sensed functional traits (RS-traits) and spectral metrics for plant diversity estimation in wetland ecosystems has not been fully investigated. In this study, we integrated UAV hyperspectral and LiDAR data to extract spectral band metrics, Rao’s quadratic entropy and principal components, texture features, and retrieve physiological and morphological RS-traits. We then used these features to predict multi-dimensional (species, functional, and phylogenetic) plant diversity using multiple stepwise regression (MSR), generalized additive models (GAMs), and random forest regression (RF). The results demonstrated that the retrieved RS-traits were generally consistent with field measurements (R2 = 0.36–0.78 for physiological, R2 = 0.47–0.87 for morphological traits). Among the different models, MSR performed best for species diversity, GAMs for functional diversity, and RF for phylogenetic diversity. The highest predictive performance was achieved for species diversity (R2adj = 0.62–0.73), followed by functional (R2adj = 0.41–0.83) and phylogenetic diversity (R2adj = 0.55–0.64). Models based on RS-traits consistently performed better than those based on spectral metrics, while combining spectral metrics and RS-traits did not lead to statistically significant improvements. While our results provide preliminary evidence towards a unified RS-trait framework for the multi-dimensional monitoring of wetland plant diversity, further work is needed to generalise these findings to other sites and wetland types.

Intraspecific variation in plant phenotypes mediates responses to changing environmental conditions; however, a comprehensive analysis of factors influencing variation in plant species traits is still lacking in freshwaters. Here, we used Phragmites australis, a cosmopolitan foundation wetland species, to understand adaptation by plants to environmental conditions and spatial patterns in local adaptation by combining information on functional traits, including remote sensing-derived spectral traits and genetic diversity. We measured eleven leaf functional traits, including four spectral traits related to leaf structure and pigments content, plus two genetic diversity indices in 48 plots of P. australis from eight wetland systems in Italy. We used GAMMs to model trait responses to water and sediment conditions. We also produced continuous maps of trait variation using very-high-resolution maps obtained by remote sensing and used trait-environment relationships to analyse plant responses at fine and large scales. Ten out of eleven traits and genetic indices were significantly influenced by at least one environmental variable. Eutrophication and N/P ratio limitation led to a decrease in values of traits related to photosynthesis and productivity, especially for water phosphate concentration > 40 mu g L-1, and to increased genetic diversity. Extreme conditions at specific sites (like high conductivity above 3000 mu S cm(-1)) also determined a marked increase in traits related to high resource-use efficiency. Stressful conditions determined by P-limited eutrophication induced trait and genetic response of P. australis, shifting to more conservative strategies. Moreover, continuous maps of remotely sensed plant traits that respond strongly to environmental variables highlighted patterns of eutrophication and resource-use at the site scale, providing an innovative approach to monitoring sites densely covered by vegetation. This study demonstrates that coordinating functional, spectral and genetic data can robustly describe factors influencing diversity and responses in plants, and shows that consistent intraspecific variation of a dominant species can influence ecosystem processes at different scales.

Remote sensing data enables the accurate tracking of short- and long-term vegetation changes and provides valuable information for assessing plant phenology at different scales. However, investigating plant phenology using satellites and relating it to the environmental context still presents challenges in natural, complex ecosystems such as wetlands. Within these systems, both anthropogenic disturbance and climate change pose a threat to habitat equilibrium, for example by facilitating the establishment of invasive alien species.To characterise the phenology of two invasive aquatic plant species (Ludwigia hexapetala and Nelumbo nucifera), which have a notable impact on wetlands across Europe and North America, we utilised a medium-term Sentinel-2 time series collected across seven sites, representing diverse wetland types and biogeographical regions. We then investigated the environmental drivers (meteo-climatic factors and water quality parameters) influencing different phenometrics linked to the invasion success of these plants across sites and growing seasons, using non-linear parametric models.The models demonstrated different strengths of linkage between environmental variables and selected phenological metrics. The phenometrics that were modelled most efficiently were the growth and senescence rates, and peak biomass for L. hexapetala, and the green-up and senescence start timing for N. nucifera. Assessing the most important inputs for the phenometrics models showed that the two species respond to environmental forcing at different spatial scales, reflecting their contrasting ecological strategies. Key phenology drivers are linked to climatic constraints, mainly temperature and light resources, for N. nucifera, or to local habitat features, such as residence time and wind disturbance, for L. hexapetala.

High-resolution, 3-D water surface mapping in aquatic environments is critical for evaluating complex interactions between human activities and environmental dynamics. Despite the overall potential of LiDAR data to generate 3-D point clouds, providing the accurate and complete digital surface models (DSMs) at the interface between terrestrial and aquatic ecosystems is still a significantly challenging task. In fact, due to water's strong near-infrared absorption and its near specularity, LiDAR often results in weak or missing signal returns. In addition, direct linear interpolation for gap filling can introduce biases in the DSM reconstruction, especially near shorelines. This study proposes a four-step semiautomatic, open-source workflow for high-resolution DSM reconstruction in aquatic scenarios, using unsupervised machine learning for land-water classification based on optimized LiDAR-derived features. Mean water-level surface elevation, extracted from binary scene clustering, was used to fill DSM gaps over water via ad hoc gap filling. The accuracy of the resulting "water-filled" DSM (WFDSM) was evaluated across six diverse real-world aquatic scenarios with a range of challenging conditions (e.g., presence of aquatic vegetation, detached ponds, man-made structures, and land depressions) and compared against open-source products. Unsupervised clustering combining radiometric and geometric features achieved high classification accuracy (F-score > 0.97) for "water-level" targets, with negligible commission errors. Unlike standard products, WFDSM effectively handles variations in terrain and surface, maintaining low elevation biases (< 25 cm) even in areas with complex vegetation and fine-scale anthropogenic structures, thus demonstrating high suitability in both transitional and open-water areas.

As freshwater ecosystems are threatened globally, the conservation of aquatic plant diversity is becoming a priority. In the last decade, remote sensing has opened up new opportunities to measure biodiversity, especially across terrestrial biomes, and the combination of spectral features with additional information derived from community phylogeny can further advance the accurate characterisation of plant functional diversity across scales. In this study, we explored the use of spectral features extracted from centimetre resolution hyperspectral imagery collected by a drone and phylogenetic metrics derived from a fully resolved supertree to estimate functional diversity (richness, divergence, and evenness) using non-linear parametric and machine learning models within communities of floating hydrophytes and helophytes sampled from different sites. Our results show that all three functional diversity metrics can be estimated from spectral features using machine learning models (random forest; R2 = 0.90-0.92), while parametric models perform worse (generalised additive models; R2 = 0.40-0.79), especially for community evenness. Merging phylogenetic and spectral features improves modelling performance for functional richness and divergence (R2 = 0.95-0.96) using machine learning, but only significantly benefits community evenness estimation when parametric models are used. The combination of imaging spectroscopy and phylogenetic analysis can provide a quantitative way to capture variability in plant communities across scales and gradients, to the benefit of ecologists focused on the study and monitoring of biodiversity and related processes.

PROSPECT is the most widely used optical leaf model for a wide range of remote sensing applications on vegetation and has been developed and parameterised based on empirical data measured almost exclusively on terrestrial plant leaves. As aquatic plants differ substantially from terrestrial plants in leaf morphology and physiology, the validity of the relationships underlying PROSPECT in aquatic plants needs to be verified empirically. To this end, we compiled a comprehensive dataset of leaf spectra and biochemical-structural parameters sampled along a water affinity gradient, including floating and emergent hydrophytes, helophytes and riparian species, and terrestrial plants. In parallel, we designed a multidimensional experiment to explore the performance of PROSPECT across different groups and to characterise sources of modelling error, focusing on aquatic plants. Our results showed that estimates of most leaf parameters from PROSPECT inversions diverged increasingly from measured traits when moving from terrestrial to aquatic species. The suboptimal performance of PROSPECT on aquatic plants appears to be driven by three main factors: difficulties in disentangling leaf dry matter components (particularly proteins), unresolved issues related to the overlap of primary and secondary pigment mixtures and absorption, and the peculiarities of internal leaf structure (i.e. the presence of ‘aerenchyma’). These findings highlight the need for careful preliminary evaluation of the applicability and limitations of PROSPECT when applied to vegetation types that differ significantly from the typical terrestrial trees and grasses used for model calibration, including aquatic plants. Such evaluation should be preferably based on empirical data covering natural heterogeneity, so that future applications of remote sensing for mapping aquatic and wetland vegetation characteristics can be improved in terms of robustness and transferability.

Imaging spectroscopy and lightweight unmanned aerial vehicles (UAVs) have revolutionized remote sensing of vegetation, by providing high spectral and spatial resolution data. Comprehensive wetland monitoring should be based on reliable mapping of biophysical aquatic vegetation parameters, which in turn requires low bias in measured spectra, e.g. minimization of reflectance anisotropy. This study aims to quantitatively investigate how sensor scan direction and solar zenith angle (SZA) affect vegetation spectra over two dominant aquatic plants, Phragmites australis and Nuphar lutea, representing different functional types (i.e. emergent and floating) with divergent canopy structure and affinity with water, using data collected with a hyperspectral (400-1000 nm range) push-broom sensor mounted on a UAV. Anisotropy factor (ANIF), mean reflectance scatterplots and regression analysis were used for assessing the magnitude of spectral anisotropy at both pseudo-leaf and canopy scales. Our findings showed more accentuated anisotropic behaviour in the visible than near-infrared domain at SZA < 60 degrees as the scan direction approaches that of solar principal plane. The increase in reflectance in near-forward direction affects the spectra of floating-leaved N. lutea at both leaf and canopy scales, suggesting the presence of water (as a film over leaves or as canopy background) as a likely anisotropy driver. Backward hotspot is evident in canopy spectra of emergent P. australis, with driving mechanisms similar to terrestrial species (i.e. shadow-hiding). At SZA > 60 degrees, reflectance anisotropy appears to be negligible, independent of functional type and scan direction. This research underscores the importance of considering sun-target-sensor geometry in remote sensing studies on aquatic vegetation.

Understanding how environmental conditions and plant functional variation are mutually related is critical to improving our comprehension of plant adaptations. In this context, our knowledge of the interlinks between plant functional, spectral and genetic traits and environmental filters is still very limited, especially for wetland species. To gain new insights on this topic, a multidimensional dataset, centred on the widespread macrophyte species Nuphar lutea , was assembled by collecting data on functional traits (including spectral traits), genetic metrics and environmental determinants from 28 plots spanning north‐central Italy. A strong environmental filter acts on all traits (morphological, biochemical, spectral and the genetic diversity metrics) resulting in significant local control over trait patterns, exemplified by the discrimination value of water electrical conductivity. This is further reinforced by the key contribution of sediment variables in explaining traits variation. Site‐specific environmental conditions were reflected in different patterns of genetic diversity, suggesting a long‐term effect of environmental filters on genotypes as well. High water conductivity – in our study sites indicative of long‐term hydrogeological settings – is linked to more acquisitive behaviour in N. lutea and a progressive reduction in its genetic diversity, while high nutrients availability in sediments promotes higher leaf traits performance. This study better explores how high variability in leaf traits reinforces current genetic and mechanistic knowledge about competitive strategies in the key aquatic plant N. lutea , by testing the effectiveness of a novel integrative approach to assess multiple sources of plant functional variation.

Different perspectives use of machine learning (ML) algorithms have proven their performance depends on the quality of reference data. This is particularly true when targets are complex environments, such as wetlands, on which a vast majority of studies are site-specific and based on a single date. With this work, an extensive reference dataset of about 400,000 samples was collected, covering nine different sites and multiple seasons, to be considered representative of temperate wetland vegetation communities at continental scale. Starting from this dataset, the performance of selected ML classifiers was compared for detailed wetland vegetation type mapping, using spectral indices (SI) derived from multi-temporal composites of Sentinel-2 as input. Global and per-class accuracy metrics were computed based on four independent training and testing subsets, extracted from the overall dataset, and the impacts of input features variation in number and sites covered were assessed. Our results show a generally higher predictive power for ensemble methods, such as Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), compared to standalone ones, with the notable exception of Support Vector Machine (SVM); the latter in fact, in the algorithm that scored the highest overall accuracy (0.977 ± 0.001) and F-score for all the target classes. Decreasing the number of input features generally resulted in classification accuracy losses, less marked for RF than for SVM, while site-specific algorithms training showed more stability of SVM, thus indicating SVM stronger transferability than RF and XGBoost.

Over the last two decades, advancements in airborne imaging spectroscopy have prompted the exploitation of lightweight drones for detailed vegetation assessment at unprecedented resolutions. Yet, surface reflectance anisotropy and view-illumination effects may bias spectra extracted from push-broom scanners and derived spectral indices (SIs), particularly over aquatic vegetation, thus impacting the retrieval of biophysical and biochemical vegetation parameters. In this study, the impact of illumination conditions (overcast versus clear sky) and angular configurations (i.e., solar and viewing angles) on radiometric variability of centimetric resolution drone data was empirically investigated over four different aquatic plant species, representing different growth forms and canopy structures. Nadir-normalized reflectance spectra, broadband SIs, and the spectral angle distance to proximal leaf reflectance were used for characterizing and quantifying radiometric variability at canopy and leaf levels. Our findings demonstrated a decrement in reflectance under diffuse light conditions, especially in highly reflective domains within Green (520–580 nm) and Near-Infrared (700–850 nm) ranges, and a marked angular reflectance anisotropy in high absorption spectral regions (i.e., 450–500 nm and 630–700 nm) for aquatic vegetation. The normalized difference vegetation index (NDVI) showed overall lower sensitivity to incoming light variability and angular configurations compared to other tested SIs, whereas the water adjusted vegetation index (WAVI), suitably designed for aquatic vegetation, was less affected by angular anisotropy in floating plants. Indeed, radiometric variability exhibited a dependence on aquatic plant features, i.e., leaf orientation, canopy structure, and affinity with water (as canopy background).

Both genetic and phenotypic intraspecific diversity play a crucial role in the ecological and evolutionary dynamics of organisms. Several studies have compared phenotypic divergence (Pst) and differentiation of neutral loci (Fst) to infer the relative roles of genetic drift and natural selection in population differentiation (Pst - Fst comparison). For the first time, we assessed and compared the genetic variation and differentiation at the leaf trait level in two key macrophytes, Phragmites australis and Nuphar lutea. To this aim, we quantified and described the genetic structure and phenotypic diversity of both species in five lake systems in north-central Italy. We then investigated the relative roles of genetic drift and natural selection on leaf trait differentiation (Pst - Fst), assuming that Fst reflects divergence caused only by genetic drift while Pst also incorporates the effects of selective dynamics on the phenotype. In terms of genetic structure, the results for P. australis were in line with those observed for other Italian and European conspecific populations. Conversely, N. lutea showed a more complex genetic structure than expected at the site level, likely due to the combined effect of genetic isolation and its mixed mating system. Both species exhibited high variability in leaf functional traits within and among sites, highlighting a high degree of phenotypic plasticity. Pst - Fst comparisons showed a general tendency towards directional selection in P. australis and a more complex pattern in N. lutea. Indeed, the drivers of phenotypic differentiation in N. lutea showed a variable mix of stabilizing and directional selection or neutral divergence at most sites. The prevalence of vegetative over generative reproduction leads P. australis populations to be dominated by a few clones that are well adapted to local conditions, including phenotypes that respond plastically to the environment. In contrast, in N. lutea the interaction of a mixed mating system and geographical isolation among distant sites tends to reduce the effect of outbreeding depression and provides the genetic basis for adaptive capacity. The first joint analysis of the genetic structure of these two key macrophytes allowed a better understanding of the relative roles of genetic drift and natural selection in the diversification of phenotypic traits within habitats dominated by P. australis and N. lutea.

Wetlands, among the most valuable ecosystems, are increasingly threatened by anthropogenic impacts and climate change. Mapping wetland vegetation changes is crucial for conservation, management, and restoration of such sensitive environments. Machine Learning (ML) algorithms, such as Random Forest (RF), Support Vector Machine (SVM), kNearest Neighbour (kNN), and Artificial Neural Networks (ANN) are commonly applied for wetland mapping based on remote sensing data. However, scientific literature on this topic is often biased towards limited study areas, and lacking generalization testing over heterogeneous environmental conditions (e.g. latitude, ecoregion, wetland type). In this study, we compared eight ensemble and standalone ML methods, aiming at finding the best performing ones for aquatic vegetation mapping using Sentinel-2 over nine study areas and different seasons. The classifiers were tested to distinguish nine different classes - five aquatic vegetation classes and four background land cover classes – with seasonal monthly composites (April-November) of spectral indices as input. Results suggest that ensemble methods, such as RF, generally show higher predictive power with respect to most of common standalone classifiers (e.g. kNN or DT), which show the highest level of overall disagreement. SVM method overcame all the other classifiers, both standalone and ensemble, over our reference dataset, scoring an overall accuracy of 0.977 ± 0.001; In particular, SVM was the best over transitional aquatic vegetation classes (helophytes and submerged-floating association), which are the ones most frequently misclassified by other methods. Further developments of this research will focus on assessing the influence on classification performance of predictor variables and variations in input features.

As reflectance measured via remote sensing is connected to plant light use and morpho-structural features, it can be used to derive spectral proxies of functional traits, or spectro-functional traits. Focusing on disentangling intraspecific trait variability in nature, we evaluated the links between haplotype and spectro-functional traits in Phragmites australis populations. Haplotypes sequencing and multi-seasonal satellite data were used to evaluate the temporal dynamics of spectro-functional traits for reed stands sampled from seven wetlands in Central Italy, investigating meteo-climatic drivers, the differences across ecological statuses, sites, and haplotypes, and quantifying intraspecific variability due to haplotype or phenotypic plasticity. Five haplotypes were identified, including an unedited one, which explained a substantial portion of intraspecific variability in canopy traits, differing for aquatic and terrestrial stands. We found that meteo-climatic factors impact on aquatic reeds traits (not over terrestrial ones) and a dualism between most and less common haplotypes, pointing to different evolutionary strategies. Dynamics in reed canopy traits were linked to ecological status, site and haplotype, with signs of haplotype-variable effects of dieback on aquatic stands. Evaluating the spectro-functional variability over reed haplotypes may provide a straightforward approach for monitoring the genotype–phenotype relations across scales and assessing their ecological drivers.

Active microwaveremote sensing data at different frequencies can provide crucial information oncrop morphology and conditions, thus effectively supporting agronomicmanagement at different scales. In this work,we used variance-based global sensitivity analysis (GSA) as a quantitativeframework for investigating the sensitivity of X-band backscattering toagronomic and morphological features typical of two different crops, maize andrice. To this end, we jointly exploited empirical data on crop status andgrowth, high-resolution TerraSAR-X data, and microwave radiative transfer model(RTM) simulations. Phenology-informed simulations allowed us to quantify thecontributions of different scattering mechanisms for the two crops undervarying observation setups, to assess the sensitivity of X-band backscatteringto morpho-structural crop biophysical parameters (and their interactions), andto evaluate the effects of crop biomass on backscatter across growth stages.

This dataset includes leaf samples from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). Data were collected along three years (2016-2018) from three temperate shallow lakes surrounded by wetlands and hosting abundant macrophyte communities, located in central and southern Europe: Lake Hídvégi or Kis-Balaton (Hungary), Mantua lakes system (Italy), and Lake Varese (Italy). Leaf photophysiological parameters derived from chlorophyll fluorescence measured with a PAM-2500 chlorophyll fluorometer (Heinz Walz GmbH, Germany). Leaf pigments were derived from spectrophotometric readings of absorbance of leaf extracts in acetone 80%.

Operational monitoring of complex vegetation communities, such as the ones growing in coastal and wetland areas, can be effectively supported by satellite remote sensing, providing quantitative spatialized information on vegetation parameters, as well as on their temporal evolution. With this work, we explored and evaluated the potential of Sentinel-2 data for assessing the status and evolution of coastal vegetation as the primary indicator of ecosystem conditions, by mapping the different plant communities of Venice lagoon (Northeast Italy) via a rule-based classification approach exploiting synoptic seasonal features of spectral indices and multispectral reflectance. The results demonstrated that coastal and wetland vegetation community type maps derived for two different years scored a good overall accuracy around 80%, with some misclassification in the coastal areas and overestimation of salt marsh communities coverage, and that virtual collaborative environments can facilitate the use of Sentinel-2 data and products to multidisciplinary users.

An overgrowth of invasive floating macrophytes can occur in shallow eutrophic lakes as a result of significant anthropogenic pressures. This necessitates appropriate monitoring, followed by informed management, control and mitigation actions. In this study, we explored the long-term dynamics of macrophyte stands in a fluvial-wetland system and the influence of mechanical removal alongside environmental drivers. The Landsat imagery archive was used to analyze the areal coverage and canopy density of both autochthonous and allochthonous macrophytes in the Mantua lakes system (Northern Italy). Satellite derived data showed a substantial increase in the extent of the alien Nelumbo nucifera , and Ludwigia hexapetala , and the native Trapa natans over a timescale of decades, possibly caused by the temporary absence of macrophyte removal and altered hydrology. According to spectral proxies, N. nucifera recorded consistently the highest density in the system. T. natans density was found to respond to the maximum summer temperature and Eastern Atlantic climatic index, reflecting the role of regional climatic controls. This approach may be transferred to inland waters from regional to global scale exploiting satellite archives to obtain a time series on changing species dynamics essential for the conservation and management of aquatic habitats.

Abstract Background Macrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400–2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial–temporal macrophyte variability. Results Our key finding is that structural—leaf dry matter content, leaf mass per area—and biochemical—chlorophyll-a content and chlorophylls to carotenoids ratio—traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters. Conclusions Our main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.

The use of functional traits (FTs) can provide quantitative information to explain macrophyte ecology more effectively than traditional taxonomic-based methods. This research aims to elucidate the trait-based approaches used in recent macrophyte studies to outline their applications, shortcomings, and future challenges. A systematic literature review focused on macrophytes and FTs was carried out on Scopus database (last accessed May 2020). The latest 520 papers published from 2010 to 2020, which represent 70 % of the whole literature selected since 1969, were carefully screened. Reviewed studies mainly investigated: 1) the role of FTs in shaping communities; 2) the responses of macrophytes to environmental gradients; 3) the application of FTs in monitoring anthropic pressures; and 4) the reasons for success of invasive species. Studied areas were concentrated in Europe (41 %) and Asia (32 %), overlooking other important biodiversity hotspots, and only 6.2 % of the world macrophytes species were investigated in dedicated single species studies. The FTs most commonly used include leaf economic and morphological traits, and we noticed a lack of attention on root traits and in general on spatial traits patterns, as well as a relatively poor understanding of how FTs mediate biotic interactions. High-throughput techniques, such as remote sensing, allow to map fine-scale variability of selected traits within and across systems, helping to clarify multiple links of FTs with ecological drivers and processes. We advise to promote investigations on root traits, and to push forward the integration of multiple approaches to better clarify the role of macrophytes at multiple scales.