[show abstract][hide abstract] ABSTRACT: 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 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.
International Journal of Applied Earth Observation and Geoinformation 01/2014; 29:1–10. · 2.18 Impact Factor
[show abstract][hide abstract] ABSTRACT: Plant phenology is a good indicator of the impact of climate change on
ecosystems. On mountain systems the main environmental constraints
governing phenological timing are air and soil temperature, photoperiod
and presence of snow. Recent studies showed the potentiality of using
automated repeat digital photography for monitoring vegetative
phenological events. In the present study, digital images collected with
a CC640 Campbell Scientific Camera over 3 years (2009, 2010, 2011) in a
subalpine grassland were used to analyse the spatial patterns of
phenological events and their relationship with the timing of snowmelt.
Yearly time series of green chromatic coordinates (gcc) were computed
from hourly images. In order to analyse the spatial pattern of
phenological metrics, gcc time series for each 10x10 pixel region of the
target ecosystem were computed and the start of the season for the 10x10
regions was extracted. Based on the same grid dimension a snowmelt date
map corresponding to the day of the year in which the snow disappears
from the ground was obtained. Our main result showed that despite the
snowmelt occurs rapidly, as maximum in seven days, several distinct
spatial patterns were identified. The comparison of spatial patterns of
snowmelt and phenological dynamics led to quite unexpected results. In
fact, a negative correlation was found between the two variables,
meaning that the growing season begins later in convex areas
characterized by an early snowmelt, and vice versa in concave areas. A
detailed field vegetational analysis revealed that these patterns were
related to different plant communities. In particular differences in
terms of species abundance seem to be related to convex and concave
areas, mainly covered by grasses and by forbs respectively suggesting
that different patterns of snow accumulation and of water availability
during the growing season due to micromorphology affect the vegetation
community and so indirectly phenology. These observations were
particularly clear during spring 2011, when an early disappearance of
snow, about 40 days earlier than the previous two years, occurred. These
results support the possibility of using digital repeat photography to
analyze the spatial variability of phenological timing of complex
ecosystems such as alpine grasslands.
[show abstract][hide abstract] ABSTRACT: In this study a method based on the analysis of MODerate-resolution
Imaging Spectroradiometer (MODIS) time series is proposed to estimate
the post-fire resilience of mountain vegetation (broadleaf forest and
prairies) in the Italian Alps. Resilience is defined herewith as the
ability of a dynamical system to counteract disturbances. It can be
quantified by the amount of time the disturbed system takes to resume,
in statistical terms, an ecological functionality comparable with its
undisturbed behavior. Satellite images of the Normalized Difference
Vegetation Index (NDVI) and of the Enhanced Vegetation Index (EVI) with
spatial resolution of 250m and temporal resolution of 16 days in the
2000-2012 time period were used. Wildfire affected areas in the
Lombardy region between the years 2000 and 2010 were analysed. Only
large fires (affected area >40ha) were selected. For each burned
area, an undisturbed adjacent control site was located. Data
pre-processing consisted in the smoothing of MODIS time series for noise
removal and then a double logistic function was fitted. Land surface
phenology descriptors (proxies for growing season start/end/length and
green biomass) were extracted in order to characterize the time
evolution of the vegetation. Descriptors from a burned area were
compared to those extracted from the respective control site by means of
the one-way analysis of variance. According to the number of subsequent
years which exhibit statistically meaningful difference between burned
and control site, five classes of resilience were identified and a set
of thematic maps was created for each descriptor. The same method was
applied to all 84 aggregated events and to events aggregated by main
land cover. EVI index results more sensitive to fire impact than NDVI
index. Analysis shows that fire causes both a reduction of the biomass
and a variation in the phenology of the Alpine vegetation. Results
suggest an average ecosystem resilience of 6-7 years. Moreover,
broadleaf forest and prairies show different post-fire behavior in terms
of land surface phenology descriptors. In addition to the above
analysis, another method is proposed, which derives from the qualitative
theory of dynamical systems. The (time dependent) spectral index of a
burned area over the period of one year was plotted against its
counterpart from the control site. Yearly plots (or scattergrams) before
and after the fire were obtained. Each plot is a sequence of points on
the plane, which are the vertices of a generally self-intersecting
polygonal chain. Some geometrical descriptors were obtained from the
yearly chains of each fire. Principal Components Analysis (PCA) of
geometrical descriptors was applied to a set of case studies and the
obtained results provide a system dynamics interpretation of the natural
[show abstract][hide abstract] ABSTRACT: Sun-induced chlorophyll fluorescence (Fs) is an electromagnetic signal emitted in the 650–800 nm spectral window by the chlorophyll-a of green leaves. Previous studies demonstrated the retrieval of Fs on a global scale using high spectral resolution measurements by the Fourier Transform Spectrometer (FTS) on board the greenhouse gases observing satellite (GOSAT). The retrieval of Fs from GOSAT-FTS data is based on the modeling of the in-filling of solar Fraunhofer lines by Fs. The first Fs retrieval methods for GOSAT-FTS measurements were based on physical formulations of the radiative transfer between the atmosphere, the surface and the instrument including the Fs emission. As an alternative, a statistical method was also successfully applied to GOSAT data. This method is based on a singular vector decomposition (SVD) technique producing a basis of spectral functions able to model the contribution of the reflected solar radiation to the top-of-atmosphere measurement in a linear way. The Fs signal is included in the forward model as an extra parameter adding to the reflected solar radiation. Here, we use field spectroscopy measurements to provide further experimental evidence on the retrieval of Fs with statistical approaches in both Fraunhofer lines and atmospheric oxygen and water vapor bands. The statistical retrieval method used with GOSAT-FTS data has been adapted to a set of ground-based spectro-radiometer measurements in the 717–780 nm range. Retrieval results in the 745–759 nm window, which contains only Fraunhofer lines, support the overall approach of estimating Fs from space measurements in that spectral window. Furthermore, the application of the method to broader fitting windows including both Fraunhofer lines and and (oxygen and water vapor) atmospheric bands atmospheric bands has been proven to be very effective to reduce the retrieval noise and has also shown a good comparison with reference O2A-based retrievals. This allows consideration of statistical methods as a powerful option for Fs retrieval from broad-band space-based measurements in the near-infrared.
Remote Sensing of Environment 01/2013; 133:52-61. · 5.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: 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.
Environmental Research Letters 01/2013; 8:025008. · 3.58 Impact Factor
[show abstract][hide abstract] ABSTRACT: 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/Fm′), 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/Fm′, 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/Fm′ (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.
ISPRS Journal of Photogrammetry and Remote Sensing 01/2013; 86:168–177. · 3.31 Impact Factor
[show abstract][hide abstract] ABSTRACT: 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.
International Journal of Biometeorology 12/2012; 57:871-880. · 2.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: Mountain regions are expected to be particularly influenced by future
climate change with increasing temperature, change in precipitation
patterns and duration of snow cover. In particular climate change is
foreseen to impact alpine ecosystems by increasing of weather extremes
(e.g. heat waves, droughts, exceptional anticipated snowmelt). Although
different studies attested the effect of climate change on vegetation
phenological shifts, uncertainties exist on the impacts of such shifts
on ecosystem processes and hence on the ecosystem-climate feedbacks.
High-altitude grasslands are snow-covered for most of the year and act
as a net carbon source throughout all the snow period. Little is still
known on the effects of spring warming and early snowmelt on annual
carbon budget of these alpine ecosystems. Being part of the PhenoAlp
project (www.phenoalp.eu) this study evaluated the effect of an
exceptional early snowmelt observed in 2011 on the relationship between
plant phenology and the ecosystem functioning of an unmanaged grassland
of northwestern Italian Alps located at 2160 m asl. The following main
questions were addressed: does an early snowmelt date increase the
length of the growing season? If so, what is the effect on the
productivity of the ecosystem? For this purpose continuous measurements
of CO2 exchange across the biosphere/atmosphere interface assessed by
means of eddy covariance since summer 2008 were evaluated. In order to
analyse the relationship between phenology and ecosystem productivity,
we extracted phenological indicators from CO2 flux time-series. Results
showed shifts in the phenological indicators considered and a clear
effect on the dynamics of the NEE (Net Ecosystem CO2 Exchange) and GPP
(Gross Primary Production) time-courses as a consequence of earlier
snowmelt. The grassland turned from a source to a sink more than one
month in advance compared to previous years. The earlier onset of
biological activity was also supported by evaluations of canopy
greening, LAI and vegetation indices. Beside this highly anticipated
beginning of the growing season, we found a slower general dynamics of
carbon flux components and lower summer peaks of NEE and GPP in 2011
compared to previous years, with different implications on the growing
season productivity and the annual carbon balance of the grassland.
[show abstract][hide abstract] ABSTRACT: The aim of this study was to evaluate the potential of MODIS normalized difference vegetation index hypertemporal data analysis for assessing Mediterranean pasture conditions in North Western Sardinia (Italy). During the seasons 2006 to 2007 and 2007 to 2008, field observations were carried out to classify 67 pasture sites in three condition classes based on expert knowledge. The local net primary productivity scaling (LNS) method was applied, and its potential for discriminating the pasture condition classes was evaluated by logistic regression models (LRM). Yearly and average LNS maps were generated for the period 2000 to 2008, and analyzed to identify areas that exhibited persistently low LNS values (hotspots). The LNS method proved useful to discriminate pastures in different conditions (LRM bootstrapped Nagelkerke pseudo R2=0.52). The analysis of persistence of low LNS values allow identifying regional hotspots of degradation. A qualitative evaluation of the main hotspots on aerial photographs revealed that approximately 62% of the hotspots were clearly characterized by pasture degradation patterns, whereas the remaining were associated to highly fragmented landscapes or to errors in the land cover map. This result emphasizes the importance of using multiscale approaches by integrating the LNS regional assessment with high spatial resolution remote sensing data analysis.
[show abstract][hide abstract] ABSTRACT: Canopy level chlorophyll fluorescence and reflectance of maize were retrieved simultaneously by using spectral
fitting (SF) techniques applied to canopy and reference upwelling radiances measured on the ground in
the O2–A atmospheric absorption band by means of a ground-measurements-based (GMB) method, using a white reference panel. This method was inspired by the Fluorescence Experiment (FLEX) mission concept, which is expected to provide the user community with a top-of-canopy radiance product, as well as sufficient data on atmospheric conditions to enable the simulation of a white reference panel radiance, after which the ground-based method can also be applied by the users of FLEX data. For the retrieval, a coupled surface– atmosphere radiative transfer model was also used to simulate the canopy radiance in specific atmospheric conditions and to quantify fluorescence and reflectance variables by using a second method based on the canopy radiance simulation (CRS), which uses the canopy radiance measurements only. The CRS method does not require any cross calibration of reference measurements, and is extremely useful when a reliable reference cannot be found. Part of the mathematical functions that modeled reflectance and fluorescence were recently used by the authors to perform simulations of observations from space. Simulations of the retrievals for both methods were performed at two different spectral band widths of 9 nm and 20 nm to evaluate the accuracy limits for a signal to noise ratio equal to 300:1. These simulations demonstrated an enhanced accuracy as compared to previously reported retrievals on the ground, and indicated that the CRS model can indeed be successfully applied for the retrieval of fluorescence. In the retrievals from measurements, the two intervals were compared to better evaluate the combined influence of the atmospheric conditions and forward modeling spectral accuracy on the CRS method. The 20 nm interval was also used to evaluate the possibility of retrieving the bi-directional and hemispherical–directional reflectances in the viewing direction of the canopy and surroundings. Lastly, the narrower 9 nm interval delivered the most accurate simulations and was chosen for comparing the retrievals obtained by means of the two different methods. From this comparison fluorescence retrieved by means of the CRS method resulted higher (about 5%) than that retrieved with the GMB method by means of the same mathematical functions, while the retrieved reflectances were very similar. The methods presented here demonstrate that fluorescence can be retrieved even when atmospheric and surface information is limited.
Remote Sensing of Environment 01/2012; 124:72-82. · 5.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: The aim of the present work is the development of ground-based
hyperspectral systems capable of collecting continuous and long-term
hyperspectral measurements of the Earth-surface. The development of such
instruments includes the optical design, the development of the data
acquisition (Auto3S) and processing software as well as the definition
of the calibration procedures. In particular an in-field calibration
methodologie based on the comparison between field spectra and data
modeled using Radiative Transfer (RT) approach has been proposed to
regularly upgrade instrument calibration coefficients. Two different
automatic spectrometric systems have been developed: the HyperSpectral
Irradiometer (HSI) [Meroni et al., 2011] and the Multiplexer Radiometer
Irradiometer (MRI) [Cogliati, 2011]. Both instruments are able to
continuously measure: sun incoming irradiance (ETOT) and irradiance (ES,
HSI)/radiance (LS, MRI) upwelling from the investigated surface. Both
instruments employ two Ocean Optics HR4000 spectrometers sharing the
same optical signal that allow to simultaneously collect "fine" (1 nm
Full Width at Half Maximum, FWHM) spectra in the 400-1000 nm rangeand
"ultra-fine" (0.1 nm FWHM) spectra within the 700-800 nm. The collected
optical data allow to estimate biochemical/structural properties of
vegetation (e.g. NDVI) as well as its photosynthetic efficiency through
the Photochemical Reflectance Index (PRI) and the analysis of
sun-induced chlorophyll Fluorescence in the O2-A Fraunhofer line
(F@760). The automatic instruments were operated in coordination with
eddy covariance flux tower measurements of carbon exchange in the
framework of several field campaigns: HSI was employed in a subalpine
pasture (2009-ongoing) (www.phenoalp.eu) while MRI was employed in 2009
in the Sen3Exp field survey promoted by the European Space Agency as
consolidation study to the future mission Sentinel-3. Results show that
the proposed instruments succeeded in collecting continuous and
long-term hyperspectral data in different measurement conditions. As a
demonstration of the potential of these instruments for monitoring plant
photosynthesis, the collected time series (NDVI, PRI and F@760) were
successfully used as inputs of Light Use Efficiency (LUE). However, HSI
and MRI would be also useful to routinely collect at ground observations
of other environmental compartments, for example for
calibration/validation of RS data and products.
[show abstract][hide abstract] ABSTRACT: 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.
The Review of scientific instruments 04/2011; 82(4):043106. · 1.52 Impact Factor
[show abstract][hide abstract] 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.
Agricultural and Forest Meteorology 01/2011; 151(10-10):1325–1337. · 3.42 Impact Factor
[show abstract][hide abstract] ABSTRACT: SpecCal software for the spectral calibration of high-resolution spectrometers is presented in this manuscript. The software, written in IDL 7.1, allows estimation of the channel central wavelength and the full width at half maximum of a selected spectrometer at several wavelengths across the VNIR range (350–1050 nm). This is achieved through comparison of the position and width of specific solar and terrestrial absorption features, as observed in the measured data, with those observed in simulated MODTRAN4 irradiance data. SpecCal is operated from a user-friendly graphical user interface that allows semiautomatic application of the spectral calibration algorithm at several wavelengths. The proposed software may be exploited as a useful in situ vicarious spectral calibration tool for field spectrometers operating in the VNIR range, which makes it possible to quickly analyze the spectral characteristics of the instruments and their possible variations with time.
[show abstract][hide abstract] ABSTRACT: 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, P<0.001, n=104) even within each PFT. Besides LAIMAX, we found that reference respiration may be explained partially by total soil carbon content (SoilC). For undisturbed temperate and boreal forests a negative control of total nitrogen deposition (Ndepo) on reference respiration was also identified. We developed a new semiempirical model incorporating abiotic factors (climate), recent productivity (daily GPP), general site productivity and canopy structure (LAIMAX) which performed well in predicting the spatio-temporal variability of RECO, explaining >70% 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.
[show abstract][hide abstract] ABSTRACT: Hyperspectral remote sensing can provide spatial and temporal variability of ecosystem parameters driving carbon fluxes which can be integrated with eddy covariance (EC) measurements to sale up carbon estimates to regional and global level. This contribution presents the development of two fully automated spectral data systems capable of collecting unattended, continuous, long-term measurements. Such systems have been installed to EC flux sites to improve our knowledge on the relationships between vegetation optical properties and photosynthesis.
3rd International Symposium on Recent Advances in Quantitative Remote Sensing, Valencia (Spain); 09/2010
[show abstract][hide abstract] ABSTRACT: 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.
[show abstract][hide abstract] ABSTRACT: The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval.