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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.

The detection of solar induced chlorophyll fluorescence (SIF) in the field with spectrometers is based on the depth of the solar Fraunhofer or oxygen absorption lines in the upwelling radiance compared to that in the downwelling irradiance. This relative depth enables the differentiation of SIF from the reflected radiation. Recent studies have shown that if oxygen bands are used to retrieve SIF from tower-based measurements, then atmospheric correction is required. This study presents a band shape fitting (BSF) approach to retrieve both the relative optical path length (deepening) and SIF (infilling) from field measurements at the same time, using information in the measured spectral shape of the O2 feature. This approach is an alternative to using radiative transfer process models for estimating atmospheric transmittance. The method was applied to measurements taken from 100 m elevation above a forest, yielding plausible results for SIF in the O2A and O2B bands. The sensitivity to combined atmospheric and instrument characteristics prohibits application at much greater distances from the surface.

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.

The upcoming Fluorescence Explorer (FLEX) satellite mission aims to provide high quality radiometric measurements for subsequent retrieval of sun-induced chlorophyll fluorescence (SIF). The combination of SIF with other observations stemming from the FLEX/Sentinel-3 tandem mission holds the potential to assess complex ecosystem processes. The calibration and validation (cal/val) of these radiometric measurements and derived products are central but challenging components of the mission. This contribution outlines strategies for the assessment of in situ radiometric measurements and retrieved SIF. We demonstrate how in situ spectrometer measurements can be analysed in terms of radiometric, spectral and spatial uncertainties. The analysis of more than 200 k spectra yields an average bias between two radiometric measurements by two individual spectrometers of 8%, with a larger variability in measurements of downwelling radiance (25%) compared to upwelling radiance (6%). Spectral shifts in the spectrometer relevant for SIF retrievals are consistently below 1 spectral pixel (up to 0.75). Found spectral shifts appear to be mostly dependent on temperature (as measured by a temperature probe in the instrument). Retrieved SIF shows a low variability of 1.8% compared with a noise reduced SIF estimate based on APAR. A combination of airborne imaging and in situ non-imaging fluorescence spectroscopy highlights the importance of a homogenous sampling surface and holds the potential to further uncover SIF retrieval issues as here shown for early evening acquisitions. Our experiments clearly indicate the need for careful site selection, measurement protocols, as well as the need for harmonized processing. This work thus contributes to guiding cal/val activities for the upcoming FLEX mission.

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 increasing amount of continuous time series of solar-induced fluorescence (SIF) and vegetation indices (e.g. Photochemical Reflectance Index, PRI) acquired with high temporal (sub-minute) frequencies is foreseen to allow tracking of the structural and physiological changes of vegetation in a variety of ecosystems. Coupled with observations of CO2, water, and energy fluxes from eddy covariance flux towers, these measurements can bring new insights into the remote monitoring of ecosystem functioning. However, continuously changing solar-view geometry imposes directional effects on diurnal cycles of the fluorescence radiance in the observation direction (F) and PRI, controlled by structural and biochemical vegetation properties. An improved understanding of these variations can potentially help to disentangle directional responses of vegetation from physiological ones in the continuous long-term optical measurements and, therefore, allow to deconvolve the physiological information relevant to ecosystem functioning. Moreover, this will also be useful for better interpreting and validating F and PRI satellite products (e.g., from the upcoming ESA FLEX mission). Many previous studies focused on the characterization of reflectance directionality, but only a handful of studies investigated directional effects on F and vegetation indices related to plant physiology. The aim of this study is to contribute to the understanding of red (F687) and far-red (F760) fluorescence and PRI anisotropy based on field spectroscopy data and simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. We present an extensive dataset of multi-angular measurements of F and PRI collected at canopy level with a high-resolution instrument (FloX, JB Hyperspectral Devices UG, Germany) over different ecosystems: Mediterranean grassland, alfalfa, chickpea and rice. We found, that F760 and F687 directional responses of horizontally homogeneous canopies are characterized by higher values in the backscatter direction with a maximum in the hotspot and lower values in the forward scatter direction. The PRI exhibited similar response due to its sensitivity to sunlit-shaded canopy fractions. As confirmed by radiative transfer forward simulations, we show that in the field measurements leaf inclination distribution function controls the shape of F and PRI anisotropic response (bowl-like/dome-like shapes), while leaf area index and the ratio of leaf width to canopy height affect the magnitude and the width of the hotspot. Finally, we discuss the implications of off-nadir viewing geometry for continuous ground measurements. F observations under oblique viewing angles showed up to 67 % difference compared to nadir observations, therefore, we suggest maintaining nadir viewing geometry for continuous measurements of F and vegetation indices. Alternatively, a correction scheme should be developed and tested against multi-angular measurements to properly account for anisotropy of canopy F and PRI observations. The quantitative characterization of these effects in varying illu- mination geometries for different canopies that was performed in this study will also be useful for the validation of remote sensing F and PRI products at different spatial and temporal scales.

The increasing amount of continuous time series of solar-induced fluorescence (SIF) and vegetation indices (e.g. Photochemical Reflectance Index, PRI) acquired with high temporal (sub-minute) frequencies is foreseen to allow tracking of the structural and physiological changes of vegetation in a variety of ecosystems. Coupled with observations of CO2, water, and energy fluxes from eddy covariance flux towers, these measurements can bring new insights into the remote monitoring of ecosystem functioning. However, continuously changing solar-view geometry imposes directional effects on diurnal cycles of the fluorescence radiance in the observation direction (F) and PRI, controlled by structural and biochemical vegetation properties. An improved understanding of these variations can potentially help to disentangle directional responses of vegetation from physiological ones in the continuous long-term optical measurements and, therefore, allow to deconvolve the physiological information relevant to ecosystem functioning. Moreover, this will also be useful for better interpreting and validating F and PRI satellite products (e.g., from the upcoming ESA FLEX mission). Many previous studies focused on the characterization of reflectance directionality, but only a handful of studies investigated directional effects on F and vegetation indices related to plant physiology. The aim of this study is to contribute to the understanding of red (F687) and far-red (F760) fluorescence and PRI anisotropy based on field spectroscopy data and simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. We present an extensive dataset of multi-angular measurements of F and PRI collected at canopy level with a high-resolution instrument (FloX, JB Hyperspectral Devices UG, Germany) over different ecosystems: Mediterranean grassland, alfalfa, chickpea and rice. We found, that F760 and F687 directional responses of horizontally homogeneous canopies are characterized by higher values in the backscatter direction with a maximum in the hotspot and lower values in the forward scatter direction. The PRI exhibited similar response due to its sensitivity to sunlit-shaded canopy fractions. As confirmed by radiative transfer forward simulations, we show that in the field measurements leaf inclination distribution function controls the shape of F and PRI anisotropic response (bowl-like/dome-like shapes), while leaf area index and the ratio of leaf width to canopy height affect the magnitude and the width of the hotspot. Finally, we discuss the implications of off-nadir viewing geometry for continuous ground measurements. F observations under oblique viewing angles showed up to 67 % difference compared to nadir observations, therefore, we suggest maintaining nadir viewing geometry for continuous measurements of F and vegetation indices. Alternatively, a correction scheme should be developed and tested against multi-angular measurements to properly account for anisotropy of canopy F and PRI observations. The quantitative characterization of these effects in varying illumination geometries for different canopies that was performed in this study will also be useful for the validation of remote sensing F and PRI products at different spatial and temporal scales.

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.

Mangrove forests are among the most productive ecosystems on Earth and mainly grow at tropical and subtropical latitudes. They provide many important ecological and societal functions. However, rapid spatiotemporal variations in mangroves have been observed worldwide, especially in the coastal zones of developing areas, and the integrity of mangroves has been significantly affected by anthropogenic activities in recent decades. The goal of this study was to determine the spatiotemporal characteristics of mangrove distribution over the past 30 years in Guangdong Province. This goal was achieved by classifying multi-temporal Landsat images using a decision tree method based on Classification and Regression Tree (CART) algorithm. The driving forces resulting in these spatiotemporal variations of mangroves were then discussed. Our analysis revealed that the classification method used in this study yielded good accuracy, with an overall accuracy and kappa coefficient of higher than 90% and 0.8, respectively. In Guangdong province, the mangrove forests covered areas of 9305, 9556, 6793, and 9700 ha in 1985, 1995, 2005, and 2015, respectively, with remarkable inter-annual changes. Mangrove forests are mainly located in Western Guangdong, and few are located in Eastern Guangdong. The distribution of mangrove patches became more fragmented from 1985 to 2005 and less fragmented from 2005 to 2015, and the distribution pattern in 2015 showed stronger connectivity than that in 1985. Natural factors, such as temperature, sea level rise, extreme weather events, and the length of the coastline, have macroscopic effects on the distribution of mangrove forests. Anthropogenic activities, such as deforestation, urbanization, and aquaculture development, have negative effects on the distribution of mangroves. On the other hand, the establishment of nature reserves has positive effects on the distribution of mangroves. The findings of this study provide a reference for the management and protection of mangroves, which is of great practical significance.

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.

The complex aquatic ecosystems, such as lakes, are characterized by a high level of hourly and daily dynamic due to the rapid growth / decrease of phytoplankton, and to the variation of the total Suspended Solids due for example to the re-suspension of the bottom caused by winds. To this variability is added the variability also the spatial one. Satellite images have the advantage of being able to respond well to the understanding of spatial variability and temporal variability during the days but not able to explore the hourly temporal variability. For this reason, within the H2020 - EOMORES project we are integrating the use of water quality products obtained from Sentinel-2 and Sentinel-3 images with Remote Sensing Reflectance (Rrs) measurements obtained from spectroradiometers mounted on platforms and / or on floating buoys that allow continuous measurements during the day and for several consecutive days.

[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.

Sun induced fluorescence at 760 nm (F 760 ) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUE p and LUE f we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.

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.

Due to its close link to the photosynthetic process, sun-induced chlorophyll fluorescence (F) opens new possibilities to study dynamics of photosynthetic light reactions and to quantify CO2 assimilation rates. Although recent studies show that F is linearly related to gross primary production (GPP) on coarse spatial and temporal scales, it is argued that this relationship may be mainly driven by seasonal changes in absorbed photochemical active radiation (APAR) and less by the plant light use efficiency (LUE). In this work a high-resolution spectrometer was used to continuously measure red and far-red fluorescence and different reflectance indices within a sugar beet field during the growing season in 2015. Diurnal and seasonal developments were compared to eddy covariance derived GPP. Additionally, part of the time series coincided with a heatwave. The induced drought stress allowed us to observe F and its relationship to GPP under changing environmental conditions during the seasonal cycle. Across the season a strong linear relationship between GPP and F760 was found. This relationship however, was mainly driven by changes in APAR and was strongly reduced under drought conditions. We could show that far-red fluorescence yield can explain 59% of the diurnal and 79% of the seasonal variance in the light use efficiency. However, an even stronger relationship between FY760 and the structural vegetation index MTVI2 was found, implying that FY760 is affected by seasonal structural changes of the canopy. Nevertheless, the seasonally de-trended FY760 and PRI show that they share strong interdependencies with seasonal and diurnal LUE, in particular under drought stress conditions.

The SCOPE model embeds the state of art for coupling soil vegetation atmosphere transfer (SVAT) and radiative transfer models (RTM). For that reason the FLuorescence EXplorer (FLEX) mission selected this model to derive vegetation properties through inversion. However inverse problem is often ill-posed, providing equally likely solutions and hence inflating the uncertainty of the retrieved parameters. In this work we test the use of different priors based on ancillary measurements and literature to support multiple-constrain inversion of SCOPE. Results show that prior information on the relationships between variables such as leaf chlorophyll content $(C_{\text{ab}})$ , leaf carotenoids content $(C_{\text{ca}})$ , leaf water content $(C_{\mathrm{w}})$ and/or maximum carboxylation rate $(V_{\text{cmax}})$ reduce inversion uncertainties and overfitting, and should be sampled/estimated together with optical data.

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.

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

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.

Measurements of sun-induced chlorophyll fluorescence (SIF) over plant canopies provide a proxy for plant photosynthetic capacity and are of high interest for plant research. Together with spectral reflectance, SIF has the potential to act as a noninvasive approach to quantify photosynthetic plant traits from field to air and spaceborne scales. However, SIF is a small signal contribution to the reflected sunlight and often not distinguishable from sensor noise. SIF estimation is, therefore, affected by an unquantified uncertainty, making it difficult to estimate accurately how much SIF is truly emitted from the plant. To investigate and overcome this, we designed a device based on a spectrometer covering the visible range and equipped it with an LED emitting at the wavelength of SIF. Using this as a reference and applying thorough calibrations, we present consistent evidence of the instrument's capability of SIF retrieval and accuracy estimations. The LED's intensity was measured under sunlight with 1.27 ± 0.27 m W× sr-1 m}s nm-1 stable over the day. The large increase of SIF due to the Kautsky effect was measured spectrally and temporally proving the biophysical origin of the signal. We propose rigorous tests for instruments intended to measure SIF and show ways to further improve the presented methods.