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In this study we examined ecosystem respiration (RECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of RECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of RECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of RECO. The maximum seasonal leaf area index (LAIMAX) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature Tref=15°C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, p70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands., JRC.H.2-Air and Climate

International audience; Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

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 150m2 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. © 2014 Elsevier B.V.

a b s t r a c t 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 grass- land 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, includ- ing 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 formula- tions. 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 PARpotential 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.

This paper presents a method for mapping water stress in a maize field using hyperspectral remote sensing imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an experimental farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to measure relative leaf water content (RWC), active chlorophyll fluorescence (ΔF/ F m ′ ), leaf temperature (Tl) and Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant physiological status (ΔF/ F m ′ , difference between Tl and air temperature (Tair), and RWC) but not the canopy structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band (PRI570) showed the strongest relationships with ΔF/ F m ′ (r2 = 0.76), Tl − Tair (r2 = 0.82) and RWC (r2 = 0.64) and the red-edge Chlorophyll Index (CIred-edge) with LAI (r2 = 0.64). Thus PRI has been proven to be related to water stress at early stages, before structural changes occurred. A method based on an ordinal logit regression model was proposed to map water stress classes based on airborne hyperspectral imagery. PRI570 showed the highest performances when fitted against water stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map water stress in the maize field. This study proves the feasibility of mapping stress classes using hyperspectral indices and demonstrates the potential applicability of remote sensing data in precision agriculture for optimizing irrigation management.

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., JRC.H.2-Climate change and air quality

This paper reports 3 years of eddy covariance measurements (2002-2004) on a poplar plantation (Populus; x cancidensis Moench, Clone I-214) in Northern Italy. We analyzed seasonal and interannual variability of the net ecosystem exchange (NEE), gross primary production (GPP), ecosystem respiration (R(ECO)) and evapotranspiration (ET) in relation to different meteorological and environmental conditions experienced by the plantation and to natural disturbances. In particular, during 2003 climatic conditions were exceptionally severe, with the highest mean air temperatures and the lowest precipitations observed in the study area in the last 50 years. In addition, during the late summer of 2004, the plantation was attacked by fall webworm larvae (Hyphantria cunea). The cumulated NEE during the three growing season (April-September) was -752.8, -626.7 and -702.7 g C m(-2) for 2002, 2003 and 2004, respectively. In the period June-August 2003, NEE was 35% and 29% higher than the NEE measured in the same period in 2002 and 2004, respectively, resulting in a lower net carbon uptake. As R(ECO) did not show large differences over the three growing seasons, the reduction in net carbon sequestration of June-August 2003 is mainly explained by a reduction of GPP (-18% and -17% in 2002 and 2004, respectively). The cumulated ET over the three growing seasons was 388, 471, 484 kg H(2)O m(-2) for 2002, 2003 and 2004, respectively. This behaviour was due to the fact that the position of the water table remained close to the roots, therefore supplying enough water to the plantation, even during the driest period. The analysis of light curve parameters, their residuals and canopy conductance suggests that the reduction Of CO(2) uptake during summer 2003 was mainly controlled by stomatal and non-stomatal limitations due to high temperatures, directly and in particular indirectly (i.e. through D) rather than by soil water stress. In conclusion, our study shows that in the presence of a heat-wave, a significant reduction of net productivity during summer may occur even in the absence of marked soil water stress. In addition to this sensitivity to high temperatures, which will likely increase in the future, the effects of natural disturbances may add further uncertainties, thus suggesting caution in the evaluation of the potential carbon sequestration of these ecosystems. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key ecophysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.

Vegetation phenology is strongly influenced by climatic factors. Climate changes may cause phenological variations, especially in the Alps which are considered to be extremely vulnerable to global warming. The main goal of our study is to analyze European larch (Larix decidua Mill.) phenology in alpine environments and the role of the ecological factors involved, using an integrated approach based on accurate field observations and modelling techniques. We present 2 years of field-collected larch phenological data, obtained following a specifically designed observation protocol. We observed that both spring and autumn larch phenology is strongly influenced by altitude. We propose an approach for the optimization of a spring warming model (SW) and the growing season index model (GSI) consisting of a model inversion technique, based on simulated look-up tables (LUTs), that provides robust parameter estimates. The optimized models showed excellent agreement between modelled and observed data: the SW model predicts the beginning of the growing season (B(GS)) with a mean RMSE of 4 days, while GSI gives a prediction of the growing season length (L(GS)) with a RMSE of 5 days. Moreover, we showed that the original GSI parameters led to consistent errors, while the optimized ones significantly increased model accuracy. Finally, we used GSI to investigate interactions of ecological factors during springtime development and autumn senescence. We found that temperature is the most effective factor during spring recovery while photoperiod plays an important role during autumn senescence: photoperiod shows a contrasting effect with altitude decreasing its influence with increasing altitude.

This manuscript presents a study aimed at characterizing the seasonal course of photosynthetic capacity of an alpine deciduous conifer, European larch (Larix decidua Mill.), based on chlorophyll fluorescence measurements and photosynthetic pigment analysis. The study focused on the characterization of autumn senescence events which (contrary to bud-burst) are still scarcely investigated. The study was conducted on two natural European larch stands in the northwestern Italian Alps during two consecutive years. The results show that photosynthetic efficiency as assessed by fluorescence measurements was controlled by variations in air and soil temperature. Photosynthesis responded to variations in maximum air and soil temperature in a delayed way, with a varying lag depending on the seasonal period considered. The analysis of photosynthetic efficiency and pigment decline at the end of the growing season identified two senescence phases. During early senescence, plants manifested only the beginning of needle decolouration, while during late senescence pigment degradation led to a loss in photosynthetic efficiency. This behavior indicates that the beginning of needle yellowing and the decline in photosynthetic efficiency can occur at different times-a finding that should be considered in order to improve models of ecosystem processes.

This study investigates the performances in a terrestrial ecosystem of gross primary production (GPP) estimation of a suite of spectral vegetation indexes (VIs) that can be computed from currently orbiting platforms. Vegetation indexes were computed from near-surface field spectroscopy measurements collected using an automatic system designed for high temporal frequency acquisition of spectral measurements in the visible near-infrared region. Spectral observations were collected for two consecutive years in Italy in a subalpine grassland equipped with an eddy covariance (EC) flux tower that provides continuous measurements of net ecosystem carbon dioxide (CO2) exchange (NEE) and the derived GPP. Different VIs were calculated based on ESA-MERIS and NASA-MODIS spectral bands and correlated with biophysical (Leaf area index, LAI; fraction of photosynthetically active radiation intercepted by green vegetation, f IPARg), biochemical (chlorophyll concentration) and ecophysiological (green light-use efficiency, LUEg) canopy variables. In this study, the normalized difference vegetation index (NDVI) was the index best correlated with LAI and f IPARg (r = 0.90 and 0.95, respectively), the MERIS terrestrial chlorophyll index (MTCI) with leaf chlorophyll content (r = 0.91) and the photochemical reflectance index (PRI551), computed as (R531 −R551)/(R531 +R551) with LUEg (r = 0.64). Subsequently, these VIs were used to estimate GPP using different modelling solutions based on Monteith’s lightuse efficiency model describing the GPP as driven by the photosynthetically active radiation absorbed by green vegetation (APARg) and by the efficiency (") with which plants use the absorbed radiation to fix carbon via photosynthesis. Results show that GPP can be successfully modelled with a combination of VIs and meteorological data or VIs only. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterised by a strong seasonal dynamic of GPP. Accuracy in GPP estimation slightly improves when taking into account high frequency modulations of GPP driven by incident PAR or modelling LUEg with the PRI in model formulation. Similar results were obtained for both measured daily VIs and VIs obtained as 16-day composite time series and then downscaled from the compositing period to daily scale (resampled data). However, the use of resampled data rather than measured daily input data decreases the accuracy of the total GPP estimation on an annual basis., JRC.H.4-Monitoring Agricultural Resources

Reliable time series of vegetation optical properties are needed to improve the modeling of the terrestrial carbon budget with remote sensing data. This paper describes the development of an automatic spectral system able to collect continuous long-term in-field spectral measurements of spectral down-welling and surface reflected irradiance. The paper addresses the development of the system, named hyperspectral irradiometer (HSI), describes its optical design, the acquisition, and processing operations. Measurements gathered on a vegetated surface by the HSI are shown, discussed and compared with experimental outcomes with independent instruments. © 2011 American Institute of Physics.

This study compares MODIS NDVI 16-day (250 m) time series, acquired by Terra and Aqua platforms, for monitoring the phenological cycle of larch and grasslands in an alpine environment. The accuracy of MODIS 250 m Terra and Aqua phenological metrics �as evaluated for larch forests through comparison �ith field data. At regional level it �as carried out a correlation analysis bet�een the mean dates of start and end of season detected from MODIS Terra and Aqua in different years. Regional maps of start and end of season �ere derived from MODIS data and the interannual phenological variability of both ecosystems �as evaluated. Annual anomalies of the beginning of the gro�ing season, obtained from satellite data, �ere related �ith air temperature anomalies, computed from meteorological stations, to evaluate the effects of recent climate variability on the vegetation phenological cycle. Comparison �ith field phenological observations sho�ed that the start and the end of phenological cycle can be accurately determined from MODIS Terra and Aqua data and that an increase/decrease of 1°C in spring temperature lead to about 10 days in advance/ delay of the start of the gro�ing season.

This research aims at developing a remote sensing technique for monitoring the interannual variability of the European larch phenological cycle in the Alpine region of Aosta Valley (Northern Italy) and to evaluate its relationships with climatic factors. Phenological field observations were conducted in eight test sites from 2005 to 2007 to determine the dates of completion of different phenological phases. MODerate Resolution Imaging Spectrometer (MODIS) 250 m 16-days normalized difference vegetation index (NDVI) time series were fitted with double logistic curves and the dates corresponding to different features of the curves were determined. Comparison with field data showed that the features of the fitted NDVI curve that allowed the best estimate of the start and end of the growing season were the zeroes of its third derivative (MAE of 6 and 4 days, respectively). The start and end of season were also estimated with the spring warming (SW) and growing season index (GSI) phenological models. MODIS start and end of season dates generally agreed with those obtained by the SW and GSI climate-driven phenological models. However, phenological models provided erroneous results when applied in years with anomalous meteorological conditions. The relationships between interannual variability of the larch phenological cycle and climate were investigated by comparing the mean start and end of season yearly anomalies with air temperature anomalies. A strong linear relationship (R2=0.91) was found between mean spring temperatures and mean start of season dates, with an increase of 1 °C in mean spring temperature leading to a 7-day anticipation of mean larch bud-burst date. Leaf coloring dates were found to be best related with mean September temperature (R2=0.77), but with higher spring temperatures appearing to lead to earlier leaf coloring. © 2010 Blackwell Publishing Ltd.

In this paper we present a methodology for the estimation of Gross Primary Production (GPP), Net Primary Production (NPP) and Net Ecosystem Production (NEP) for the main agricultural and forest ecosystems of the Lombardia Region (Italy). The MOD17 model was parameterized according to the different agro-forestry ecosystems and applied at regional scale by using satellite data with a spatial resolution of 250m. The high spatial resolution along with fine classification agro-forestry ecosystems has allowed to accurately analyze the carbon budget of an extremely fragmented and complex environment such as the Lombardia Region. Modeling results showed the role of the forests in the carbon budget at regional scale and represent important information layer for the spatial analysis and for inferring the inter-annual variability of carbon sequestration due to impacts of extreme events and recent climate change (e.g., drought, heat wave, flooding, fires).

In this study we analyze the spatial and temporal variability of European Larch phenological cycle in mountainous areas (Val d'Aosta, Italy) by using a combination of satellite data, primary topographic attributes (altitude and aspect) and phenological models driven by climatic variables. We apply a remote sensing technique based on a time series of MODIS TERRA 250m 16-days NDVI data for mapping the inter-annual variability of phenological cycle. Starting from the satellite green-up maps we spatially explored the response of larch phenology at different terrain conditions varying for altitude and aspect. A retrospective analysis based on climate-driven phenological models was finally conductedd to evaluate the magnitude of temporal variability of Larch phenology in the last century.

Changes in snow cover depth and duration predicted by climate change scenarios are expected to strongly affect high-altitude ecosystem processes. This study investigates the effect of an exceptionally short snow season on the phenology and carbon dioxide source/sink strength of a subalpine grassland. An earlier snowmelt of more than one month caused a considerable advancement (40 days) of the beginning of the carbon uptake period (CUP) and, together with a delayed establishment of the snow season in autumn, contributed to a two-month longer CUP. The combined effect of the shorter snow season and the extended CUP led to an increase of about 100% in annual carbon net uptake. Nevertheless, the unusual environmental conditions imposed by the early snowmelt led to changes in canopy structure and functioning, with a reduction of the carbon sequestration rate during the snow-free period.