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Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [ G CC ]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVI C ) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVI C was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVI C values and recommend the use of site-specific NDVI from MODIS in order to scale NDVI C . We also compared G CC extracted from red-green-blue images to NDVI C and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVI C lags behind G CC in deciduous broad-leaf forests and grasslands, suggesting that G CC is more sensitive to changes in leaf color and NDVI C is more sensitive to changes in leaf area. In evergreen forests, NDVI C peaks later than G CC in spring, probably tracking the processes of shoot elongation and new needle formation. Both G CC and NDVI C can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVI C is more comparable than G CC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVI C is in excellent agreement with NDVI obtained from spectral measurements, and that NDVI C and G CC can complement each other in describing ecosystem phenology. Additionally, NDVI C allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery. [ABSTRACT FROM AUTHOR]

Climate extremes can have tremendous impacts on the terrestrial biosphere and their frequency is very likely going to increase in the coming years. In this study we examine the impact of the 2015 summer heat wave on a mountain grassland in the Western European Alps by jointly analyzing phenocam greenness (GCC) trajectories, proximal sensing, CO 2 flux data and structural canopy traits. Phenocam effectively tracked the impact of the heat wave, showing 39% of reduction in maximum canopy greenness and a senescence advance of 32 days compared to mean values. The same patterns ( i.e. reduction of maximum values and senescence advance) were observed for all considered canopy traits and photosynthetic ecosystem functional properties, in particular the maximum light-saturated rate of CO 2 uptake ( A max ), LAI and PRI. Pixel-level analysis of phenocam images allowed us to further highlight that forbs were more heavily impacted than grasses. Moreover the effect of the extreme event on greenness seasonal course was evaluated testing new formulations of the Growing Season Index (GSI) model. Results demonstrate that a combination of water and high temperature stress was responsible for the observed reduction of canopy greenness during the heat wave. [ABSTRACT FROM AUTHOR]

Net ecosystem carbon dioxide (CO 2 ) exchange (NEE) at FLUXNET sites is typically evaluated by means of the eddy covariance technique using a set of instruments on a single tower. However, in complex terrain, such as mountain areas, and during nighttime atmospheric conditions, with low turbulent mixing and stable stratification, this approach is known to underestimate the nighttime NEE and thus bias longer-term carbon balances. This study reports on the quantification of advection at a subalpine grassland site in Northern Italy (2160 m asl) situated in complex mountainous terrain. We show that different published methods for indirectly or directly accounting for advection resulted in a large divergence in the annual carbon balance. Advection, and in particular the horizontal term, reached non negligible values during nighttime and its inclusion in the CO 2 conservation equation increased NEE by a factor of two. NEE calculated by taking into account all terms (NEE acmb ), i.e. turbulent exchange, change in storage and advection of CO 2 , matched the approach based on the after-sunset maximum in the vertical turbulent flux and change in the storage of CO 2 and ecosystem respiration measured by automated chambers. Accounting for advection led to a 169% change in the annual carbon budget, turning the ecosystem from a sink (−108 gCm −2 y −1 ) to a source (75 gCm −2 y −1 ) of CO 2 . [ABSTRACT FROM AUTHOR]

In this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties. The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network ( http://phenocam.sr.unh.edu/ ), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package. [ABSTRACT FROM AUTHOR]

The increasingly important effect of climate change and extremes on alpine phenology highlights the need to establish accurate monitoring methods to track inter-annual variation (IAV) and long-term trends in plant phenology. We evaluated four different indices of phenological development (two for plant productivity, i.e., green biomass and leaf area index; two for plant greenness, i.e., greenness from visual inspection and from digital images) from a 5-year monitoring of ecosystem phenology, here defined as the seasonal development of the grassland canopy, in a subalpine grassland site (NW Alps). Our aim was to establish an effective observation strategy that enables the detection of shifts in grassland phenology in response to climate trends and meteorological extremes. The seasonal development of the vegetation at this site appears strongly controlled by snowmelt mostly in its first stages and to a lesser extent in the overall development trajectory. All indices were able to detect an anomalous beginning of the growing season in 2011 due to an exceptionally early snowmelt, whereas only some of them revealed a later beginning of the growing season in 2013 due to a late snowmelt. A method is developed to derive the number of samples that maximise the trade-off between sampling effort and accuracy in IAV detection in the context of long-term phenology monitoring programmes. Results show that spring phenology requires a smaller number of samples than autumn phenology to track a given target of IAV. Additionally, productivity indices (leaf area index and green biomass) have a higher sampling requirement than greenness derived from visual estimation and from the analysis of digital images. Of the latter two, the analysis of digital images stands out as the more effective, rapid and objective method to detect IAV in vegetation development. [ABSTRACT FROM AUTHOR]

Plant phenology is a commonly used and suitable indicator of the impact of climate change on vegetation. In mountainous areas, phenology is governed by environmental drivers such as air temperature, photoperiod and the presence of snow. In this study, digital images collected over 3 years (2009, 2010 and 2011) in a subalpine grassland site were used to investigate the relationship between the timing of snowmelt and the beginning of the growing season in both the spatial and the temporal dimension. The image analysis was conducted for a wide area corresponding to approximately 150 m 2 to characterize the spatial heterogeneity of grassland phenology. The investigated area was divided into 855 10 × 10 pixel cells, and for each cell annual time series of green chromatic coordinates ( gcc ) were computed from hourly images. To analyse the spatial pattern of phenology, the beginning of the season for each cell was extracted from the gcc time series. Based on the same grid dimension, three maps of yearly snowmelt date corresponding to the day of the year in which the snow in each cell disappeared from the ground were obtained. Although complete snowmelt in the area occurred rapidly, within a maximum of six days, several distinct spatial patterns were identified with snowmelt occurring earlier in convex compared to concave areas. Differences in snowmelt dates were quite unexpectedly negatively related to the beginning of the growing season. The negative correlation was explained considering that areas characterized by different microtopography have also a different species composition: the growing season began earlier in concave areas preferred by opportunistic species with a fast development after snowmelt while phenological development of grass typical of convex areas can take longer. This behaviour was especially evident in 2011 characterized by an extremely anticipated snowmelt. On the contrary, the analysis of the relationship between the timing of snowmelt and the beginning of the season between the three years analysed in this study, highlighted an advancement of the beginning of the growing season in 2011. However, this is valid only in areas characterized by the abundance of opportunistic species such as forbs for which the snow cover plays a major role in determining the beginning of phenological development. The results presented in this study support the possibility of using repeat digital photography to analyse the role of plant species composition on phenology in complex ecosystems such as subalpine and alpine grasslands. [ABSTRACT FROM AUTHOR]

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice. [ABSTRACT FROM AUTHOR]

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps. [ABSTRACT FROM AUTHOR]

The presence or absence of leaves within plant canopies exert a strong influence on the carbon, water and energy balance of ecosystems. Identifying key changes in the timing of leaf elongation and senescence during the year can help to understand the sensitivity of different plant functional types to changes in temperature. When recorded over many years these data can provide information on the response of ecosystems to long-term changes in climate. The installation of digital cameras that take images at regular intervals of plant canopies across the Integrated Carbon Observation System ecosystem stations will provide a reliable and important record of variations in canopy state, colour and the timing of key phenological events. Here, we detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products. These data will help us improve the representation of phenological responses to climatic variability across Integrated Carbon Observation System stations and the terrestrial biosphere through the improvement of model algorithms and the provision of validation datasets. [ABSTRACT FROM AUTHOR]

The presence or absence of leaves within plant canopies exert a strong influence on the carbon, water and energy balance of ecosystems. Identifying key changes in the timing of leaf elongation and senescence during the year can help to understand the sensitivity of different plant functional types to changes in temperature. When recorded over many years these data can provide information on the response of ecosystems to long-term changes in climate. The installation of digital cameras that take images at regular intervals of plant canopies across the Integrated Carbon Observation System ecosystem stations will provide a reliable and important record of variations in canopy state, colour and the timing of key phenological events. Here, we detail the procedure for the implementation of cameras on Integrated Carbon Observation System flux towers and how these images will help us understand the impact of leaf phenology and ecosystem function, distinguish changes in canopy structure from leaf physiology and at larger scales will assist in the validation of (future) remote sensing products. These data will help us improve the representation of phenological responses to climatic variability across Integrated Carbon Observation System stations and the terrestrial biosphere through the improvement of model algorithms and the provision of validation datasets. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the Δh parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

The long‐term increase in satellite‐based proxies of vegetation cover is a well‐documented response of seasonally snow‐covered ecosystems to climate warming. However, observed greening trends are far from uniform, and substantial uncertainty remains concerning the underlying causes of this spatial variability. Here, we processed surface reflectance of the moderate resolution imaging spectroradiometer (MODIS) to investigate trends and drivers of changes in the annual peak values of the Normalized Difference Vegetation Index (NDVI). Our study focuses on above‐treeline ecosystems in the European Alps. NDVI changes in these ecosystems are highly sensitive to land cover and biomass changes and are marginally affected by anthropogenic disturbances. We observed widespread greening for the 2000–2020 period, a pattern that is consistent with the overall increase in summer temperature. At the local scale, the spatial variability of greening was mainly due to the preferential response of north‐facing slopes between 1900 and 2400 m. Using high‐resolution imagery, we noticed that the presence of screes and outcrops locally magnified this response. At the regional scale, we identified hotspots of greening where vegetation cover is sparser than expected given the elevation and exposure. Most of these hotspots experienced delayed snow melt and green‐up dates in recent years. We conclude that the ongoing greening in the Alps primarily reflects the high responsiveness of sparsely vegetated ecosystems that are able to benefit the most from temperature and water‐related habitat amelioration above treeline. [ABSTRACT FROM AUTHOR]

Abstract: The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons. We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO2 fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e., LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products. We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e., MOD17 RUE) can be used to predict daily GPP. Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of phenological and RUE models, and thus point toward an extension of the proposed methodologies to the dataset collected within PhenoCam Network. [Copyright &y& Elsevier]

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this shifting mechanism to climate, as well as the role played by the cryosphere in the Earth energy budget, has motivated the development of a broad spectrum of predictive models. Such models rarely combine a high degree of physical realism in both the seasonal snow and glaciers, and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present S3M v5.1, a spatially explicit and hydrology-oriented cryospheric model that successfully reconstructs seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol. Comparatively novel aspects of S3M with respect to the existing literature are an explicit representation of the spatial patterns of snow liquid-water content, an hybrid approach to snowmelt that decouples the radiation- and temperature-driven contributions, the implementation of the ∆h parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the Italian north-western Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises an user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'. [ABSTRACT FROM AUTHOR]

In summer 2018, Europe experienced a record drought, but it remains unknown how the drought affected ecosystem carbon dynamics. Using observations from 34 eddy covariance sites in different biomes across Europe, we studied the sensitivity of gross primary productivity (GPP) to environmental drivers during the summer drought of 2018 versus the reference summer of 2016. We found a greater drought-induced decline of summer GPP in grasslands (−38%) than in forests (−10%), which coincided with reduced evapotranspiration and soil water content (SWC). As compared to the 'normal year' of 2016, GPP in different ecosystems exhibited more negative sensitivity to summer air temperature (Ta) but stronger positive sensitivity to SWC during summer drought in 2018, that is, a stronger reduction of GPP with soil moisture deficit. We found larger negative effects of Ta and vapour pressure deficit (VPD) but a lower positive effect of photosynthetic photon flux density on GPP in 2018 compared to 2016, which contributed to reduced summer GPP in 2018. Our results demonstrate that high temperature-induced increases in VPD and decreases in SWC aggravated drought impacts on GPP. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'. [ABSTRACT FROM AUTHOR]

Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPPmax). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree–grass ecosystem, we established three landscape‐level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green‐up and dry‐down), slopes during green‐up and dry‐down period, and seasonal amplitude, were extracted from time series of GPPmax and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPPmax was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green‐up period. Yet, both fertilized sites senesced earlier during the dry‐down period (17–19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPPmax during the green‐up period and a sharper decline in GPPmax during the dry‐down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient–water interaction in such water‐limited ecosystems. With the projected warming‐drying trend, the positive effects of N fertilization induced by N deposition on GPPmax may be counteracted by an earlier and faster dry‐down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water‐limited ecosystems. [ABSTRACT FROM AUTHOR]

A wide range of snow models with different degrees of complexity is currently available, and typically the higher is the model sophistication, the heavier are input data requirements and computational costs. The choice of an appropriate degree of complexity depends on the specific purpose. Multi-layer, physically-based snow models are typically used to reconstruct the vertical structure of the snowpack with a high level of detail and high accuracy, for example useful for avalanche risk forecasting, while simpler physical or empirical snow models are employed as one component of a modelling chain, or when a coarse estimate of snow cover characteristics is sufficient. Among the major challenges for cryospheric modelling research are i) the quantification of the snow model complexity which is needed to achieve accurate estimates of snow mass in different modelling frameworks and ii) the assessment of how the accuracy of the meteorological variables used to force snow models affects the quality of snow simulations. The latter aspect is particularly crucial in high mountain environments, where the variability of meteorological parameters is high both in space and time. In the present work five snow models of different complexity – the multi-layer models SNOWPACK and GeoTOP, the intermediate complexity models UTOPIA and SMASH, and the relatively simpler model S3M – are compared to assess their ability in reproducing the temporal evolution of snow water equivalent, snow density and snow depth, at the experimental site of Torgnon, 2160 m a.s.l. in the Western Italian Alps. High-quality meteorological forcing and detailed characterization of snowpack properties are available for this measurement site. First, we evaluate the models forced by the accurate Torgnon station measurements at 30 minute temporal resolution, to measure the model skills in case of "optimal" forcing. Second, we force the models with input data with gradually lowered frequency and/or accuracy, which are obtained by spatial interpolation of neighboring station measurements and from three global reanalyses (ERA-Interim, ERA5 and GLDAS) by extracting the meteorological time series at the gridpoint closest to the Torgnon station.This study provides information on how sensitive the snow models are to the accuracy of forcing data, exploring the feasibility of driving these models with coarser spatial and temporal resolution datasets, including interpolation of surface station measurements and reanalyses, typically the only data available in remote mountain areas. Guidelines on the trade-offs between model complexity and model performances are also provided, with the perspective of employing the best performing models to simulate past and future snowpack conditions at fine spatial scales. [ABSTRACT FROM AUTHOR]

The input of mineral dust from arid regions impacts snow optical properties. The induced albedo reduction generally alters the melting dynamics of the snowpack, resulting in earlier snowmelt. In this paper, we evaluate the impact of dust depositions on the melting dynamics of snowpack at a high-elevation site (2160 m) in the European Alps (Torgnon, Aosta Valley, Italy) during three hydrological years (2013–2016). These years were characterized by several Saharan dust events that deposited significant amounts of mineral dust in the European Alps. We quantify the shortening of the snow season due to dust deposition by comparing observed snow depths and those simulated with the Crocus model accounting, or not, for the impact of impurities. The model was run and tested using meteorological data from an automated weather station. We propose the use of repeated digital images for tracking dust deposition and resurfacing in the snowpack. The good agreement between model prediction and digital images allowed us to propose the use of an RGB index (i.e. snow darkening index – SDI) for monitoring dust on snow using images from a digital camera. We also present a geochemical characterization of dust reaching the Alpine chain during spring in 2014. Elements found in dust were classified as a function of their origin and compared with Saharan sources. A strong enrichment in Fe was observed in snow containing Saharan dust. In our case study, the comparison between modelling results and observations showed that impurities deposited in snow anticipated the disappearance of snow up to 38 d a out of a total 7 months of typical snow duration. This happened for the season 2015–2016 that was characterized by a strong dust deposition event. During the other seasons considered here (2013–2014 and 2014–2015), the snow melt-out date was 18 and 11 d earlier, respectively. We conclude that the effect of the Saharan dust is expected to reduce snow cover duration through the snow-albedo feedback. This process is known to have a series of further hydrological and phenological feedback effects that should be characterized in future research. [ABSTRACT FROM AUTHOR]

The Food and Agricultural Organization (FAO) method for potential evapotranspiration assessment is based on the crop coefficient, which allows one to relate the reference evapotranspiration of well irrigated grass to the potential evapotranspiration of specific crops. The method was originally developed for cultivated species based on lysimeter measurements of potential evapotranspiration. Not many applications to natural vegetated areas exist due to the lack of available data for these species. In this paper we investigate the potential of using evapotranspiration measurements acquired by micrometeorological stations for the definition of crop coefficient functions of natural vegetated areas and extrapolation to ungauged sites through remotely sensed data. Pastures, deciduous and evergreen forests have been considered and lower crop coefficient values are found with respect to FAO data. [ABSTRACT FROM AUTHOR]

In alpine environments, the growing season is severely constrained by low temperature and snow. Here, we aim at determining the climatic factors that best explain the interannual variation in spring growth onset of alpine plants, and at examining whether photoperiod might limit their phenological response during exceptionally warm springs and early snowmelts. We analysed 17 years of data (1998-2014) from 35 automatic weather stations located in subalpine and alpine zones ranging from 1560 to 2450 m asl in the Swiss Alps. These stations are equipped with ultrasonic sensors for snow depth measurements that are also able to detect plant growth in spring and summer, giving a unique opportunity to analyse snow and climate effects on alpine plant phenology. Our analysis showed high phenological variation among years, with one exceptionally early and late spring, namely 2011 and 2013. Overall, the timing of snowmelt and the beginning of plant growth were tightly linked irrespective of the elevation of the station. Snowmelt date was the best predictor of plant growth onset with air temperature after snowmelt modulating the plants' development rate. This multiple series of alpine plant phenology suggests that currently alpine plants are directly tracking climate change with no major photoperiod limitation. [ABSTRACT FROM AUTHOR]

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R2 CV and RMSE CV of 0.59 and 82 kg m−3, respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe. • Snow thermal inertia reflects the status of the snowpack evolution. • An empirical model is presented for mapping snow density from thermal inertia. • This study can pave new perspectives in remote sensing of the cryosphere. [ABSTRACT FROM AUTHOR]

This article presents the structure and results of a simplified model (VISTOCK) for simulating grass growth and water dynamics of grassland systems. The model, based on a process-based approach coupled with proximal (SKR 1800 2-Channel Light Sensor) and remote (Sentinel-2) NDVI-derived data for estimating LAI, simulates aboveground biomass (AGB), net primary production (NPP), evapotranspiration (ET), and the fraction of transpirable water in soil (FTSW). VISTOCK simulated a grassland system with few meteorological data (i.e., minimum and maximum daily temperatures, precipitation, global solar radiation), considering limitations to vegetation growth due to thermal and water stresses. It was calibrated for a natural alpine grassland in Italy (site T) during the most contrasting meteorological seasons of the dataset (2012, 2017, and 2018). It was then evaluated for the remaining years at site T (2013, 2014, 2015, and 2016) and for other two sites in Italy (sites B1, B2 and M) with different soil and climate conditions and diverse management strategies (2020 and 2021). VISTOCK accurately predicted AGB during the growing season (RMSE = 445, 240, 219, 365 kg DM ha-1 for T, M, B1, and B2, respectively) as well as for NPP, ET, and FSTW at site T. Simulation results suggest the ability of the model to simulate grassland in diverse environments with few inputs and parameters to be calibrated. The model's simplified structure, combined with easy-to obtain input data and easy applicability, encourages its wider use for out- and/or upscaling and decision making. • Using NDVI as a proxy for LAI simplified the model's structure. • AGB was predicted well under different environmental and management conditions. • The model showed the potential to reproduce water dynamics of grassland systems. • Simplifying the model's structure increased its applicability to different contexts. [ABSTRACT FROM AUTHOR]

Earth hummocks are small cryogenic mounds, covered by grass, closely spaced in grassland or wetlands. Hummock microtopography establishes specific microclimatic conditions, with small-scale variations in soil thermal properties and water regimes, which influence biogeochemical cycles. These properties, coupled with different litter decomposability, may cause variations on soil physical and chemical properties and pedogenesis, as well as a selective distribution of plant species. The work has been carried out at the LTER site of Tellinod (Torgnon, Aosta Valley, NW, Italy). The site is characterized by a Nardus stricta subalpine hummocky grassland located at 2100 masl, which shows the dominance of Nardus on hummocks and a prevalence of dicotyledons in interhummocks (i.e. the depressions between consecutive hummocks). Such distribution indicates that earth hummock pattern was reflected in soil properties. In order to confirm this hypothesis, we analyzed and compared soil pedogenesis and topsoil characteristics between hummocks and interhummocks. In addition, litter bags were incubated in hummock and interhummock positions to investigate litter decomposition rate as related to microtopography and plant species and its effects on topsoil edaphic properties. The results confirm that hummocky topography significantly influences topsoil properties, pedogenesis and vegetation distribution, with large differences between hummocks and interhummocks. The hummocky soil can be fully classified as Podzol, based on both the morphological and chemical diagnostic properties; however, morphological and chemical evidences indicate that the degree of podzolization differs significantly under hummocks and interhummocks. In addition, the results verify a faster decomposition of dicotyledons in the nutrient-richer interhummock topsoils compared to the podzolized hummocks positions, and an overall slower decomposition rate of Nardus litter. All these factors contribute to the creation and conservation of a unique pedo-environment in this subalpine grassland. [ABSTRACT FROM AUTHOR]

Subsurface storage changes (Δ S) represent a key modulator of drought propagation through the hydrological cycle, but their contribution to the annual water balance, and to drought propagation and recovery has rarely been explicitly assessed across catchments and climates. To expand on previous work on this matter, here we performed a large-sample analysis of precipitation, discharge, actual evapotranspiration (ET), and Δ S for 10 hydrological years and 102 catchments across various hydro-climatological regimes in Italy. We found that Δ S cannot be neglected in the annual water balance. Storage depletion leads to the attenuation of hydrological drought compared to meteorological drought, meaning that subsurface storage actively supports discharge during drought. We also found that storage generally recovers from precipitation deficits over time scales similar to the discharge recovery time, while recovery times for ET are longer. These findings show that subsurface storage drives drought propagation and recovery, regardless of climatic and catchment characteristics, and are thus relevant to properly inform water managers about surface- and ground-water availability in a changing climate. • Large-sample analysis of water balance components to study drought propagation and recovery. • Subsurface storage changes account on average for the 11% of long-term mean annual precipitation across the catchments. • Subsurface storage depletion sustains discharge during drought and the following recovery period. • A water balance approach is needed to better understand drought propagation and recovery. [ABSTRACT FROM AUTHOR]

• Greenness biological meaning and its relation with water fluxes are lacking. • New leaves are responsible for the rapid increase in spring canopy greenness. • Canopy greenness can be represented jointly using leaf spectra and leaf age. • Greenness is important to explain variation of canopy conductance. Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees' water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes. [ABSTRACT FROM AUTHOR]

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration ( RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error ( RMSE) and a 6% increase in the modeling efficiency ( EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy. [ABSTRACT FROM AUTHOR]

The quantification of rock glacier dynamics has gained increasing importance in recent years. In this study, the spatial and temporal flow patterns of perennially frozen debris in the active Gran Sometta rock glacier (Western Italian Alps) were investigated with repeated Unmanned Aerial Vehicle (UAV) surveys (2016–2019), Global Navigation Satellite System (GNSS) campaigns (2012−2020), geophysical prospections (2015) and ground surface temperature data (2014–2020). UAV data were used to generate maps of changes and elevation differences of the rock glacier surface by 3D point cloud comparison to evaluate surface lowering and accumulation processes. Horizontal velocities were quantified by an automatic image correlation technique and the results were then compared with horizontal surface velocities from GNSS measurements on selected points. The horizontal velocities estimated with the automatic method agree well with the GNSS velocities with an R2 = 0.99 and a RMSE lower than 0.07 m/y. Point cloud comparisons show surface lowering in the orographic left-hand side of the terminal part and in the central body of the rock glacier. The upper part exhibits almost absence of subsidence and any movement. This is explained by the lack of permafrost in this sector due to its overriding by the development of a small glacier during the Little Ice Age. As a result of the downslope movement, zones of surface rising occurred at the advancing front and at the moving ridge and furrow complexes. Surface velocity decreases from the orographic left to the right-hand side of the rock glacier tongue, where a thaw subsidence of up to 0.05 m/y was also observed. According to the GNSS measurements, the range of flow velocity of the rock glacier increased from 0.17–1.1 m/y in 2013 to 0.21–1.45 m/y in 2015 and then decreased until 2018 when the smallest surface velocity is detected. Since 2018, the creep velocities gradually started to increase again reaching values of 0.23 m/y up to a maximum of 1.9 m/y in the orographic left-hand side of the rock glacier tongue. This agrees with observations from other rock glaciers in the European Alps in recent decades. The complex Gran Sometta rock glacier dynamics can be explained by the heterogeneous distribution of permafrost and related subsurface perennially frozen ground which is thick enough (about 20–30 m) for permafrost creep to occur. Creep rates of the rock glacier permafrost depend also on the ground thermal regime: annual warmer surface conditions promote an acceleration of the creep rates within the rock glacier permafrost, whereas ground surface cooling causes a slight deceleration. • High-resolution point clouds and orthomosaics are generated from UAV surveys. • Rock glacier flow velocity increased from 2012 until 2015 and from 2018 to 2020. • Velocities estimated by feature-tracking algorithm and GNSS data are highly correlated. • Permafrost kinematics is influenced by thermal regime and internal structure. [ABSTRACT FROM AUTHOR]

Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grassland equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, including one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formulations. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PAR potential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors. [ABSTRACT FROM AUTHOR]

• Three different understory leaf area index (LAI u) retrieval methods are compared. • The three methods show different strengths/weaknesses depending on LAI scale. • Variation in LAI u is significantly related to vegetation diversity. Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAI u) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAI u over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAI u was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAI u > 2, while the simple ratio algorithm overestimates LAI u at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAI u values at both low and higher LAI ranges. Surprisingly, LAI u variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAI u in ecological modelling. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Luo, Y.P. et al., Using Near-Infrared Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree-Grass Ecosystems. Remote Sens. 2018, 10, 1293" which appeared on March 15, 2019 in this issue.

Climate change and human-induced stoichiometric imbalances are altering ecosystems across the globe. However, phenology, as the one of first-order control on productivity, has not been well studied with regard to its response to the above environmental changes. With co-located PhenoCams and eddy covariance (EC) towers in a large scale nutrients manipulation experiment (MANIP) in a Mediterranean tree-grass ecosystem, we intend to understand how the structural (i.e. greenness) and physiological phenology (i.e. max gross primary productivity – GPPmax) reacts to the imbalanced availability of nutrients and meteorological changes. Three experimental EC towers are located approximately 500 m apart from each other. Their footprints are manipulated with addition of nitrogen, nitrogen and phosphorus or not fertilized, serving as control. A PhenoCam was installed on the top of each EC tower. GPPmax was derived from EC flux tower measurements and green chromatic coordinates (GCC) was computed from PhenoCams. Then we extracted phenological metrics (i.e. transition dates; slopes and amplitudes of phenological profiles) and their uncertainties from time series of GCC and GPPmax. By comparing the extracted phenological metrics before and after nutrients manipulation, we showed that fertilization increases the speed of green-up and accelerates the senescence of dry-down, which is likely attributed to faster depletion of soil water at fertilized sites due to higher demand of transpiration at dry-down period. By applying statistical models and random forest method, we found meteorological factors like temperature and precipitation are the most important drivers for structural and physiological phenology. The relative importance of the availability of nutrients contributing to phenology is also quantified under different climate change scenarios. We expect that the joint use of proximal remote sensing and EC techniques, in conjunction with models, can help us to better reveal processes of environmental factors mediate the structural and physiological phenology under different nutrient availability. [ABSTRACT FROM AUTHOR]

The Alps are very sensitive to climate and land cover changes. In the past years, a growing interest towards understanding the water and carbon exchanges in the mountains led to an increasing number of studies. However, the complexity of these environments determines many uncertainties from a methodological and ecological point of view. Therefore, there is a need to compute and compare flux data collected at different, high-altitude and complex sites. Such studies will improve the knowledge about the impact of local morphology on measurements. In this study we compare different alpine sites regarding their momentum and scalar fluxes.Three eddy covariance sites in the Aosta Valley (Italy) are considered. The first is located on a 33° slope in a small lateral valley having a South-East aspect at 1730 m a.s.l. The land cover is characterised by an abandoned pasture. The second site is on an almost flat area along a milder slope. The area has a South-East aspect and it is located at 2610 m a.s.l. and the land cover is a grassland. The third (namely the ICOS associated ecosystem site IT-Tor) is located on an almost flat (less than 5°) area. The site has a South aspect and its altitude is 2160 m a.s.l. The area is characterised by an unmanaged subalpine grassland.Daytime measurements of sensible and latent heat and carbon dioxide fluxes collected at the three sites are compared during the growing season. Quality control procedure, energy balance closure, mean diurnal and cumulative fluxes are presented for each site. Potential reasons for differences among the investigated sites are discussed. [ABSTRACT FROM AUTHOR]

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP. [ABSTRACT FROM AUTHOR]