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Field spectroscopy is a powerful tool for understanding plant carbon uptake and for providing a link between local flux measurements and global satellite remote sensing. In fact, optical remote sensing techniques can capture valuable information on both phenology and physiology. Even if the concept is of interest for the scientific community, the integration of field spectroscopy techniques in flux networks is still challenging and largely unresolved. Further, mainly due to the lack of available instruments, obtained relationships between remote sensing measurements and gross primary productivity (GPP) are often site specific and so far poorly exploited.JB devices (FloX and RoX) are field spectrometer systems acquiring in situ radiometric measurements with standardized routines. They have been installed over the last five years across several ecosystems (e.g. croplands, forests, grasslands), often at eddy covariance stations. Recently, an effort was made to standardize the data processing and to align it to flux networks. The development of an open source processing chain was made and the definition of all the metadata needed to correctly interpret the measurements, including devices specification and setup, is ongoing.With this contribution, we aim to advance understanding on the relationship between optical proximal sensing information and GPP using a selection of eight diverse sites equipped with JB devices and belonging to flux networks (ICOS or FLUXNET). Selected sites represent different vegetation types, including broadleaf forest, needle leaf forests, croplands, and grasslands. The time series length varies from one season to 4 years, depending on the time the instrument was installed. From the hyperspectral data, a subset of vegetation indices (e.g. indices related to biomass, chlorophyll content, carotenoids, and sun induced chlorophyll fluorescence (SIF, NIRv), and was computed where available (i.e. only on six of the equipped FloX sites). The data output was qualitatively checked and aggregated to the same temporal resolution as for the flux data (30 mins). Several models were tested to investigate this relationship, exploited both on half-hourly interval and on daily aggregation. Overall, good results were found. In general, SIF and NIRv were found to be the best predictor for GPP at half-hourly scales (r2 =0.75 over croplands and broadleaf forests). When the analysis was computed on daily aggregation, the indices related to chlorophyll content showed the best agreement with GPP. Significant differences were found according to vegetation types, where needle leaf forests were giving the poorest results. Our analysis demonstrates the valuable information carried by field spectroscopy data in the context of understanding GPP dynamics, supporting the hypothesis that the link between optical sensing and fluxes can be better interpreted with a growing number of field spectrometer data available at flux sites.

Solar energy absorbed by chlorophyll molecules of plants is either used for the carboxylation of CO2 (i.e. photosynthesis), dissipated (in a regulated and unregulated fashion) as heat or re-emitted at higher wavelengths as fluorescence. Proximal or remote sensing measurements of the so-called sun-induced chlorophyll fluorescence (SIF) are thus thought to offer a non-obtrusive approach for quantifying a key ecosystem carbon cycle component and metric of plant health, gross primary productivity (GPP). In contrast to SIF, which is a relatively new approach, active measurements of chlorophyll fluorescence using the pulse amplitude modulation (PAM) technology represent a well-established standard approach. Up to date, few studies have combined active and passive (i.e. SIF) chlorophyll fluorescence measurements and the link between active chlorophyll fluorescence metrics and SIF is thus poorly understood, hampering progress in the physiological interpretation of the SIF signal.The overarching goal of this study is to improve our physiological understanding of the SIF signal. To this end we jointly quantified ecosystem-scale passive (i.e. SIF) and leaf-scale active chlorophyll fluorescence. Measurements were conducted during the vegetation period (March to November 2021) at a mountain Scots Pine (Pinus sylvestris L.) forest close to the village of Obermieming (Austria). Two sets of automated PAM fluorometers (MoniPAM, Walz, Germany) were installed from a walk-up tower to measure active leaf-scale chlorophyll fluorescence (ChlF) on branches in the sunlit top of the canopy and in the more shaded sub-canopy zone of the forest. Additionally, SIF at the canopy scale was measured via a high-resolution spectrometer system (FloX, JB Hyperspectral Devices, Germany) and the eddy covariance method was used to determine net ecosystem CO2 exchange.Preliminary results show an increase of SIF yield (SIF/aPAR) with warming temperature throughout summer followed by a steady decrease starting in autumn. A major heatwave event in June resulted in a decrease of the SIF and PSII yields, leading to an increase in NPQ. An overall decrease of PSII efficiency for shaded leaves compared to sun-exposed ones was detected, with significant reductions throughout decreasingly colder days in spring and autumn for both groups. Shaded leaves responded with an overall higher investment into NPQ mechanisms. In contrast, an increased yield of fluorescence and constitutive thermal energy dissipation (f,D) in sun-exposed leaves could indicate higher heat dissipation for the exposed group.Further analyses will use active chlorophyll fluorescence metrics for studying seasonal variability in SIF and SIF yield and their relationship to ecosystem-scale GPP.

Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

Data from thousands of past and present eddy covariance flux stations are available across the globe, while multiple hundreds actively operating as individual process-level studies, small flux networks dedicated to specific research goals, and larger national and continental networks with broad ecological and environmental foci.Many flux stations have weather and soil data to help clean, analyze and interpret the fluxes but most do not have optical proximal sensors, do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and cannot easily be used for validation of remotely sensed products, ecosystem modeling, or upscaling from field to regional levels. The flux source areas themselves (e.g., flux footprints) are typically not defined in the flux datasets, and the time stamps of the fluxes come in a large number of outdated non-trackable formats. Finally, the past ways of the flux data quality control, analysis and interpretation require a participation of micrometeorological expert (or an entire network) with their own custom codes or exceptional skills in using existing software such as MatLab or VB Tools in Excel. These are the key issues effectively preventing a larger environmental research community and remote sensing community from fully utilizing eddy covariance flux data.In 2016-2020, a set of new tools to collect, process, analyze, time- and space- allocate and share time-synchronized flux data from multiple flux stations were developed and deployed globally. These new tools can be effective in solving most or all of the key issues listed above. The fully automated FluxSuite system combines hardware, software and web services, and does not require an expert to run it. It can be incorporated into a new flux station or added to a present station, using a weatherized remotely-accessible microcomputer, SmartFlux3 which utilizes EddyPro software to calculate fully-processed fluxes in near-real-time, alongside biomet data and flux footprints. All data are merged into a single quality-controlled file timed using PTP time protocol. Remote sensing researchers and modelers without actual physical stations can form “virtual networks” of actual stations by collaborating with tower PIs from different physical networks and flux databases.The very latest development in this overall approach is the flux data analysis software, Tovi, designed to seamlessly ingest the data from the flux stations and to allow a non-micrometeorologist to quality control, analyze and interpret the flux data. It allows rapid execution of the QC/QA and data analysis steps which have been time-consuming and complicated in the past, and other data analysis steps virtually not doable in the past, all using interactive and intuitive GUI, including advanced footprint calculations and flux apportioning necessary for remote sensing community; NEE flux partitioning; automated generation specific lists of references for each workflow; etc.This presentation will show how combinations of these new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data for ground truthing, improve scientific interactions, and promote a better utilization of the eddy covariance flux data by a wider environmental research community.

On the 22nd of March 2019, PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission has been launched by the Italian Space Agency (ASI). Since then, the spacecraft has been collecting on demand hyperspectral data of the Earth surface. The imaging spectrometer features 239 bands covering the visible, near infrared and shortwave infrared wavelengths (400-2500 nm) with a spectral resolution The satellite mission is still in the initial commissioning phase. During this period, the acquisition of field spectroscopy data contemporary to satellite observation is fundamental. With the aim of calibrating and validating PRISMA observations on snow fields, we organized field campaigns at a high altitude (2160 m) experimental site (Torgnon, Aosta Valley) in the European Alps. During these campaigns, we measured spectral reflectance of snow with a Spectral Evolution spectrometer (350-2500 nm), snow grain size, and snow density. Among different instruments operating at the site (e.g. net radiometer, webcam, sensors for snow depth, snow water equivalent, snow surface temperature etc.), we recently installed an unattended spectrometer acquiring continuous measurements of snow reflectance. This instrument covers part of the visible and near infrared spectral range (400-900 nm) and it was used to analyze the daily evolution of snow reflectance during the snow season.In this contribution, we present a preliminary comparison between field and satellite hyperspectral reflectance data of snow. This comparison is fundamental for the future development of algorithms for the estimation of snow physical variables (snow grain size, snow albedo, and concentration of impurities) from satellite hyperspectral data.

Photogrammetry represents a non-destructive, cost-effective tool for coral reef monitoring, able to integrate traditional remote sensing techniques and support researchers’ work. However, its application to submerged habitats is still in early stage. We present new ways to employ Structure from Motion techniques to infer properties of reef habitats. In particular, we propose the use of Digital Surface Models and Digital Terrain Models for assessing coral colonies extension and height and discriminating between seabed and coral cover. Such information can be coupled with digital rugosity estimates to improve habitat characterization. DTM, DSM and orthophotos were derived and used to compute a series of metrics like coral morphologies, reef topography, coral cover and structural complexity. We show the potentialities offered by underwater photogrammetry and derived products to provide useful basic information for marine habitat mapping, opening the possibility to extend these methods for large-scale assessment and monitoring of coral reefs

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the SoilCanopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation. The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken, JPL, MM, and MR acknowledge the EnMAP project MoReDEHESHyReS “Modelling Responses of Dehesas with Hyperspectral Remote Sensing” (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy). DM, MM and MR received funding from the European Union’s Horizon 2020 research and innovation programme via the TRUSTEE project under the Marie Skłodowska-Curie grant agreement No. 721995. Authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein; SynerTGE “Landsat-8 + Sentinel-2: exploring sensor synergies for monitoring and modelling key vegetation biophysical variables in tree-grass ecosystems” (CGL2015-69095-R, Spanish Ministry of Science, Innovation and Universities); and FLUχPEC “Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ‘dehesa’ ecosystem” (CGL2012- 34383, Spanish Ministry of Economy and Competitiveness). Authors are very thankful to Dr. Karl Segl and Prof. Dr. Luis Guanter for their support with the EnMAP end-to-end simulator; as well as the MPI-BGC Freiland Group and especially Martin Hertel as well as Ramón LópezJiménez (CEAM) for technical assistance. We are grateful to all the colleagues from MPI-BGC, University of Extremadura, University of Milano-Bicocca, SpecLab-CSIC, INIA and CEAM which have collaborated in any of the field and laboratory works. We acknowledge the Majadas de Tiétar city council for its support.

The global outbreak of the Corona pandemic has led to a significant reduction of traffic and traffic-related urban air pollution. One important pollutant in this context is NO2. Sudden change in NO2 emissions related to reduction of urban traffic due to infection protection measures can be detected in Düsseldorf, Germany with continuous measurements of down-welling light with a RoX automated field-spectrometer. In comparison to a nearby reference instrument, a waveband around 590 nm was identified as significant for the retrieval in the VIS-NIR spectral range. A decision tree based on principal components which were decomposed from down-welling radiance spectra has been the most robust approach to retrieved NO2 values. Better differentiation of the NO2 value-range is achieved with a partial least square regression model. The results suggest that traffic-related changes of NOx pollution in urban air can be detected through continuous down-welling radiance measurements with inexpensive automated field-spectrometer systems., Graphical abstract Unlabelled Image, Highlights • A significant drop in urban NO2 emission was reported during the COVID-19 lock-down. • Simultaneous measurements of down-welling light model NO2 concentration • Inexpensive field spectrometer system reproduced the drop in NO2 levels. • Exploited wavebands in visible range model NO2 levels with promising accuracy • Monitoring NO2 as risk factor for COVID-19 may also include field spectrometers.

[EN] The interest of the scientific community on the remote observation of sun-induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) 6% and, This article is based upon work from COST Action ES1309 OPTIMISE, supported by COST (European Cooperation in Science and Technology; www.cost.eu).Cendrero-Mateo M.P. and Alonso L. are currently funded by AVANFLEX project (Advanced Products for the FLEX mission), no ESP2016-79503-C2-1-P, Ministry of Economy and Competitiveness, Spain. Wieneke S. is currently funded by the individual fellowship of the European Union's H2020 Marie Sklodowska-Curie actions under the grant agreement ReSPEc no [795299]. Goulas Y. is currently funded by the ECOFLUO project (In-situ Remote Sensing of Chlorophyll Fluorescence to Monitor Carbon Dynamics in Ecosystems in support of the FLEX mission), no 4500058116 and 4500058229, Centre National d'Etudes Spatiales (CNES), France. Mac Arthur, A. was funded by the UK's NERC/NCEO Field Spectroscopy Facility, at the University of Edinburgh. Pacheco-Labrador, J. was supported by the EnMAP project MoReDEHESHyReS "Modelling Responses of Dehesas with Hyperspectral Remote Sensing" (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy) and the Alexander von Humboldt Foundation via Max-Planck Prize to Markus Reichstein.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is of growing interest for the scientific community due to the inherent link of SIF with vegetation photosynthetic activity. An increasing number of in situ and airborne fluorescence spectrometers has been deployed worldwide to advance the understanding and usage of SIF for ecosystem studies. Particularly, a number of sites has been instrumented with the FloX (J&B Hyperspectral Devices, Germany), an automated instrument that houses two high resolution spectrometers covering the visible and near infrared spectral regions, one specifically optimized for fluorescence retrieval, the other for plant trait estimation.In this contribution we explore the feasibility to consistently retrieve plant traits and SIF from canopy level FloX measurements through the numerical inversion of a light version of the SCOPE model. The optimization approach was specifically adapted to work with the high- frequency time series produced by the FloX. In this context, a strategy for optimal retrieval of plant traits at daily scale is discussed, together with the implementation of an emulator of the radiative transfer model in the retrieval scheme. The retrieval strategy was applied to site measurements across Europe and the US that span a variety of natural and agricultural ecosystems.The full spectrum of canopy SIF, the fluorescence quantum efficiency, and main plant traits controlling light absorption and reabsorption were retrieved concurrently and evaluated over the growing season in comparison with site-specific ancillary data. Improvements and challenges of this method compared to other retrievals are discussed, together with the potential of applying a similar retrieval scheme to airborne datasets acquired with e.g. the HyPlant sensor, or the reconfigured “FLEX mode” data acquired with the recently launched Sentinel-3B during its commissioning phase.

Growing interest in the proximal sensing of sun-induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiometers. We developed a sensor simulator capable of reproducing biases and noises usually found in field spectroradiometers. First the sensor simulator was calibrated and characterized using synthetic datasets of known uncertainties defined from laboratory measurements and literature. Secondly, we used the sensor simulator and the characterized sensor models to simulate the acquisition of atmospheric and vegetation radiances from a synthetic dataset. Each of the sensor models predicted biases with propagated uncertainties that modified the simulated measurements as a function of different factors. Finally, the impact of each sensor model on SIF retrieval was analyzed. Results show that SIF retrieval can be significantly affected in situations where reflectance factors are barely modified. SIF errors were found to correlate with drivers of instrumental-induced biases which are as also drivers of plant physiology. This jeopardizes not only the retrieval of SIF, but also the understanding of its relationship with vegetation function, the study of diel and seasonal cycles and the validation of remote sensing SIF products. Further work is needed to determine the optimal requirements in terms of sensor design, characterization and signal correction for SIF retrieval by proximal sensing. In addition, evaluation/validation methods to characterize and correct instrumental responses should be developed and used to test sensors performance in operational conditions.

Reliable measurements of Sun Induced Fluorescence (SIF) require a good instrument characterization as well as a complex processing chain. In this paper, we summarize the state of the art SIF retrieval methods and measurements platforms for field phenotyping. Furthermore, we use HyScreen, hyperspectral-imaging system for top of canopy measurements of SIF, as an example of the instrument requirements, data process, and data validation needed to obtain reliable measurements of SIF.

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.

Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far-red Sun Induced chlorophyll Fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even-aged loblolly pine plantations in North Carolina (USA). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far-red peaks was computed. This quantifies the fluorescence emission efficiencies that is more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly 2-fold higher red fluorescence yield than mature forest plantations, while the far-red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intra-pixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long-term forest dynamics in response to future climatic conditions from remote sensing of SIF. This article is protected by copyright. All rights reserved

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.

Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production (GPP). However, the mechanistic link between GPP and SIF is not completely understood. We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F760) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen¿phosphorous (NP). Using the soil¿canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration (Cab) and maximum carboxylation capacity (Vcmax)) affected the observed linear relationship between F760 and GPP. We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F760. Changes in canopy structure mainly control the GPP¿F760 relationship, with a secondary effect of Cab and Vcmax. In order to exploit F760 data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered., The authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein, and the EUFAR TA project DEHESHyrE (EU FP7 Program). We acknowledge the Majadas de Tietar city council for its support. The authors acknowledge the Freiland Group and in particular Martin Hertel from MPI-Jena and Ramon Lopez-Jimenez (CEAM) for technical assistance. The authors thank the anonymous referee and the editor for their comments on the work, Alessandro Cescatti for discussions about the results and Silvana Schott for the graphics.

Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production (GPP). However, the mechanistic link between GPP and SIF is not completely understood. We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F

There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.

This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different Nitrogen (N) and Phosphorous (P) availability. Sun-induced chlorophyll Fluorescence yield computed at 760 nm (Fy760), scaled-photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and Normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy-chambers on the same locations sampled by the spectrometers. We hypothesized that light-use efficiency (LUE) models driven by remote sensing quantities (RSM) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as proxy for LUE and NDVI or MTCI as fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed significantly higher GPP in the N fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was tightly related to plant N content (r2 = 0.86, p < 0.01), it was poorly related to GPP (r2 = 0.45, p < 0.05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments but it is affected by N availability. Results from a cross validation analysis showed that MM (AICcv = 127, MEcv = 0.879) outperformed RSM (AICcv = 140, MEcv = 0.8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that MM is predictively inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results put forward that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

Remote estimation of Sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatiotemporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence. Key Points A novel high-resolution airborne sensor is flown Both red and far red Sun-induced fluorescence signals are accurately quantified Red and far red fluorescence tracks variations in photosynthetic efficiency

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress.

In this contribution we present activities and selected results obtained in recent studies and campaigns conducted in the context of the FLuorescence EXplorer (FLEX) mission. FLEX is a candidate mission for the ESA 8th Earth Explorer and large efforts are currently dedicated to the development of an implementation scheme for an accurate mapping of fluorescence from the selected spaceborne sensor and mission configuration. Field and airborne data collected in different experimental campaigns, together with simulated data, have been used to demonstrate the feasibility of fluorescence retrievals and the potential of exploiting high spatial resolution fluorescence maps for a better understanding of the environment from space.

We describe the results of an experiment designed to compare the radiometric performance of four different spectroradiometers in ideal field conditions. A carefully designed experiment where instruments were simultaneously triggered was used to measure the Hemispherical Conical Reflectance Factors (HCRF) of four targets of varying reflectance. The experiment was in two parts. Stage 1 covered a 2 hour period finishing at solar noon, where 50 measurements of the targets were collected in sequence. Stage 2 comprised 10 rapid sequential measurements over each target. We applied a method for normalising full width half maximum (FWHM) differences between the instruments, which was a source of variability in the raw data. The work allowed us to determine data reproducibility, and we found that lower-cost instruments (Ocean Optics and PP Systems) produced data of similar radiometric quality to those manufactured by Analytical Spectral Devices (ASD -here we used the ASD FieldSpec Pro) in the spectral range 400-850 nm, which is the most significant region for research communities interested in measuring vegetation dynamics. Over the longer time-series there were changes in HCRF caused by the structural and spectral characteristics of some targets. © 2013 Optical Society of America., The field experiment described in this paper was undertaken by participants in the EU funded COST Action (ES0903 “Eurospec”; http://cost-es0903.fem-environment.eu/) as part of a Summer School exercise in July 2011. Travel to the field site in Monte Bondone was part-funded by the EU COST action (PI: Loris Vescovo) through travel reimbursement to the Summer School instructors who are the authors of this paper. We are grateful to the participants of the Summer School for their help in setting up the experiment. We also wish to thank PP Systems, who lent Javier Pacheco-Labrador the 2m fibre optic cables used in the experiment and we are grateful to the BIOSPEC project (CGL2008-02301/CLI; http://www.lineas.cchs.csic.es/biospec) funded by the Spanish Ministry of Science and Innovation which has provided funding for Javier Pacheco-Labrador’s research

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.