Discerning Oriental from European beech by leaf spectroscopy: Operational and physiological implications

European beech (Fagus sylvatica L.) forests have recently experienced severe diebacks that are expected to increase in future. Oriental beech (Fagus sylvatica spp. orientalis (Lipsky) Greut. & Burd) is a potential candidate for assisted migration (AM) in European forests due to its greater genetic diversity and potentially higher drought resistance. Yet AM entails not only benefits, but also risks, and it is therefore important to monitor the progression of introduced (sub)species. Here, we demonstrate the potential of leaf spectroscopy to replace resourceintensive genetic analysis and field phenotyping for the discrimination and characterization of these two beech subspecies. We studied two European beech forests, one in France and one in Switzerland, where Oriental beech from the Greater Caucasus was introduced over 100 years ago. During two summers (2021, 2022), we measured leaf spectral reflectance, leaf morphological and biochemical traits from genotyped adult trees. Subspecies prediction models were developed separately for top-of-canopy leaves (amenable to remote sensing) and bottom-of-canopy leaves (easier to harvest) using partial least squares discriminant analysis (PLS-DA) and different sets of spectral predictors. Morphological, biochemical and spectra-derived leaf traits indicated that Oriental beech trees at the sites studied were characterized by higher lignin and nitrogen per unit leaf area than European beech, suggesting more protein-rich leaves on a per-area basis. The model based on top-of-canopy leaf reflectance spectra in the short-wave-infrared region (SWIR I: 1450–1750 nm) most accurately distinguished Oriental from European beech (BA = 0.86 ± 0.08, k = 0.72 ± 0.15), closely followed by models based on SWIR II, and on spectra-derived traits (BA ≥ 0.84, k ≥ 0.67). This study provides a proof-of-principle for the development of spectroscopy-based approaches when monitoring introduced species, subspecies or provenances. Our findings hold promise for upscaling to large forest areas using airborne remote sensing

Quantifying effects of cold acclimation and delayed springtime photosynthesis resumption in northern ecosystems.

Land carbon dynamics in temperate and boreal ecosystems are sensitive to environmental change. Accurately simulating gross primary productivity (GPP) and its seasonality is key for reliable carbon cycle projections. However, significant biases have been found in early spring GPP simulations of northern forests, where observations often suggest a later resumption of photosynthetic activity than predicted by models. Here, we used eddy covariance-based GPP estimates from 39 forest sites that differ by their climate and dominant plant functional types. We used a mechanistic and an empirical light use efficiency (LUE) model to investigate the magnitude and environmental controls of delayed springtime photosynthesis resumption (DSPR) across sites. We found DSPR reduced ecosystem LUE by 30-70% at many, but not all site-years during spring. A significant depression of LUE was found not only in coniferous but also at deciduous forests and was related to combined high radiation and low minimum temperatures. By embedding cold-acclimation effects on LUE that considers the delayed effects of minimum temperatures, initial model bias in simulated springtime GPP was effectively resolved. This provides an approach to improve GPP estimates by considering physiological acclimation and enables more reliable simulations of photosynthesis in northern forests and projections in a warming climate

Discerning Oriental from European beech by leaf spectroscopy : operational and physiological implications

European beech (Fagus sylvatica L.) forests have recently experienced severe diebacks that are expected to increase in future. Oriental beech (Fagus sylvatica spp. orientalis (Lipsky) Greut. & Burd) is a potential candidate for assisted migration (AM) in European forests due to its greater genetic diversity and potentially higher drought resistance. Yet AM entails not only benefits, but also risks, and it is therefore important to monitor the progression of introduced (sub)species. Here, we demonstrate the potential of leaf spectroscopy to replace resource-intensive genetic analysis and field phenotyping for the discrimination and characterization of these two beech subspecies. We studied two European beech forests, one in France and one in Switzerland, where Oriental beech from the Greater Caucasus was introduced over 100 years ago. During two summers (2021, 2022), we measured leaf spectral reflectance, leaf morphological and biochemical traits from genotyped adult trees. Subspecies prediction models were developed separately for top-of-canopy leaves (amenable to remote sensing) and bottom-of-canopy leaves (easier to harvest) using partial least squares discriminant analysis (PLS-DA) and different sets of spectral predictors. Morphological, biochemical and spectra-derived leaf traits indicated that Oriental beech trees at the sites studied were characterized by higher lignin and nitrogen per unit leaf area than European beech, suggesting more protein-rich leaves on a per-area basis. The model based on top-of-canopy leaf reflectance spectra in the short-wave-infrared region (SWIR I: 1450–1750 nm) most accurately distinguished Oriental from European beech (BA = 0.86 ± 0.08, k = 0.72 ± 0.15), closely followed by models based on SWIR II, and on spectra-derived traits (BA ≥ 0.84, k ≥ 0.67). This study provides a proof-of-principle for the development of spectroscopy-based approaches when monitoring introduced species, subspecies or provenances. Our findings hold promise for upscaling to large forest areas using airborne remote sensing

Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites:TL-LUE Parameterization and Validation

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR

Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.Peer reviewe

Monitoring the spectral performance of the APEX imaging spectrometer for inter-calibration of satellite missions

Die Fernerkundung ist heutzutage wahrscheinlich die wertvollste Methode um Parameter, die Prozesse unserer Umwelt definieren, quantitativ und global zu messen. Für eine korrekte Interpretation dieser Messungen ist das Verständnis aller Faktoren die den Messprozess beeinflussen entscheidend. Im Idealfall beinhaltet die Messung des reflektierten Sonnenlichts ausschließlich Informationen über das reflektierende Objekt oder Phänomen. Dies ist jedoch selten der Fall, da Interaktionen mit der Atmosphäre, eine Kontaminierung durch den Hintergrund und die Instrumenteigenschaften die Zusammensetzung und Ausbreitung der Sonnenstrahlung verändern. Für spektroskopiedaten-basierte Anwendungen sind die spektralen Eigenschaften des Instruments normalerweise die wichtigsten Parameter um eine korrekte Interpretation der Messung zu gewährleisten. Das spektrale Ansprechverhalten einzelner Detektorpixel wird über deren Zentrumswellenlänge sowie der Halbwertsbreite beschrieben und definiert damit die spektralen Eigenschaften des Instruments. Um die spektralen Eigenschaften des Instruments zu bestimmen, werden entsprechend Charakterisierung Messungen im Labor durchgeführt. Das Instrument ist jedoch an Bord einer luft- oder weltraumgestützten Plattform variierenden umweltbedingten Stressfaktoren ausgesetzt (z. B. Vibrationen, Temperatur und Druckkraft Schwankungen). Diese führen, zusammen mit dem natürlichen Alterungsprozess, zur Veränderung der spektralen Eigenschaften und Performance des Instruments. Werden solche Veränderungen nicht berücksichtigt, sondern weiterhin die im Labor charakterisierten spektralen Parameter zur Verarbeitung der Daten verwendet, kann das zu signifikanten Fehlern im Datensatz und der daraus abgeleiteten Produkte führen. Diese Dissertation untersucht die Eigenschaften und Ursachen für Änderungen der Charakteristik eines Spektrometers unter realen Bedingungen im Flugzeug. Dazu wurden neue Ansätze zum Monitoring der spektralen Eigenschaften eines flugzeuggestützten Sensors entwickelt und validiert. Die durch die Messungen beobachteten Abweichungen im Vergleich zu den Labormessungen werden genutzt um die Rohdaten vor einer Produktgenerierung entsprechend zu korrigieren. Das abbildende Spektrometer APEX (Airborne Prism EXperiment) steht im Fokus dieser Recherche. APEX ist ein dispersives, abbildendes pushbroom Spektrometer, das den Wellenlängenbereich zwischen 380 und 2500 nm abdeckt. APEX wurde entwickelt um gegenwärtige sowie zukünftige weltraumgestützte Missionen bei der Simulation, Kalibration und Validation zu unterstützen. Die Möglichkeit der in-flight Charakterisierung mittels eines auf APEX integrierten Charakterisierungs Equipments, bekannt als In-Flight Characterization (IFC) facility, ermöglicht die Messung der spektralen Eigenschaften des Sensors ausserhalb von Laborbedingungen. Gezielte Erfassung von IFC Messungen und Prozessierung mit Hilfe von ad-hoc entwickelten Algorithmen ermöglichte die Schätzung repräsentativer spektraler Parameter für ein luftgestütztes Instrument zu jedem Zeitpunkt. Ausserdem werden atmosphärische Absorptionsbanden aus Luftbilddaten verwendet, um die Schätzung der spektralen Parameter zusätzlich zu ergänzen und zu validieren. Dadurch konnte die Korrektur der Wellenlängenpositionen plausibel auf die APEX Datensätze angewendet werden. Die so kalibrierten APEX Daten werden erfolgreich für eine Simulation und Kalibration ausgewählter weltraumgestützter Missionen verwendet. In der Diskussion der Forschungsergebnisse werden die Vor- und Nachteile der entwickelten Ansätze besprochen und es wird auf mögliche Verbesserungen hingewiesen. Abschliessend wird ein Ausblick für weiterführende Arbeiten gegeben

Deriving land surface phenology indicators from CO₂ eddy covariance measurements

Recent progress of CO₂ eddy covariance (EC) technique and accumulation of measurements offer an unprecedented perspective to study the land surface phenology (LSP) in a more objective way than previously possible by allowing the actual photosynthesis measurement – gross primary productivity (GPP). Because of the spatial, temporal, and ecological complexity of processes controlling GPP time series, the extraction of important LSP dates from GPP has been elusive. Here, we present objective measures of several LSP metrics from GPP time series data. A case study based on long term GPP measurements over a mature boreal deciduous forest is provided together with LSP estimates from remote sensing data. Results show that most LSP metrics are interrelated within each season (spring and autumn) both from GPP and remote sensing based estimates. We provide simple mathematical derivatives of GPP time series to objectively estimate key LSP metrics such as: the start, end and length of growing season; end of greenup; start of browndown; length of canopy closure; start, end and length of peak; and peak of season. These key LSP metrics indicate the collective ecological responses to environmental changes over space and time. 

Experimental evaluation of Sentinel-2 spectral response functions for NDVI time-series continuity

Remote sensing of long-term vegetation monitoring relies on the analysis of multisensor and multitemporal time-series measurements. Cross-sensor calibration is therefore important to prevent artifacts in the temporal signal due to inherent differences in sensors configurations. Variations in spectral response functions (SRFs) are among the major causes of differences in multisensor reflectances and products. In this paper, we report on the SRF comparability of the upcoming Sentinel-2 Multispectral Instrument (MSI) sensor with a number of operational sensors National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR9), Land- sat 7 Enhanced Thematic Mapper Plus (ETM+), Satellite Pour l’Observation de la Terre VEGETATION1 (VGT1), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) relevant for vegetation monitoring. SRF cross-sensor calibration methods for the conversion of red and near infrared (NIR) reflectances and Normalized Difference Vegetation Index (NDVI) values of operational sensors in reference to the Sentinel-2 MSI sensor were evaluated. Calibration data sets obtained using the soil–leaf–canopy radiative transfer model; a state-of-the-art airborne imaging spectrometer Airborne Prism Experiment (APEX); and univariate and multi- variate regression models were considered for SRF cross-sensor calibration. For AVHRR9 and VGT1, reflectances in the red spectral region differed more than 30% from Sentinel-2 reflectances. These differences translated in NDVI deviations of up to 10%. The developed SRF cross-sensor calibration method reduced the differences by factors up to 6, 3, and 7 for red, NIR, and NDVI values, respectively. All but AVHRR9 have been found to be cross-calibrated to within 5% differences for reflectances and NDVI values. The present work is considered as part of a broader harmonization effort aimed at preparing for the integration of Sentinel-2 MSI data with existing historical data records and product time series