Assessing the impact of non-linear responses of field spectroradiometers on the estimation of biophysical parameters and light use efficiency

Tommaso Julitta’s Short Term Scientific Mission was funded by the Cost Action ES0903 – Eurospec. Javier Pacheco-Labrador’s stay was partially funded by the Biospec project “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (CGL2008-02301/CLI, Ministry of Science and Innovation).Peer reviewe

Remote sensing-based estimation of gross primary production in a subalpine grassland

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

The northernmost hyperspectral FLoX sensor dataset for monitoring of high-Arctic tundra vegetation phenology and Sun-Induced Fluorescence (SIF)

A hyperspectral field sensor (FloX) was installed in Adventdalen (Svalbard, Norway) in 2019 as part of the Svalbard Integrated Arctic Earth Observing System (SIOS) for monitoring vegetation phenology and Sun-Induced Chlorophyll Fluorescence (SIF) of high-Arctic tundra. This northernmost hyperspectral sensor is located within the footprint of a tower for long-term eddy covariance flux measurements and is an integral part of an automatic environmental monitoring system on Svalbard (AsMovEn), which is also a part of SIOS. One of the measurements that this hyperspectral instrument can capture is SIF, which serves as a proxy of gross primary production (GPP) and carbon flux rates. This paper presents an overview of the data collection and processing, and the 4-year (2019–2021) datasets in processed format are available at: https://thredds.met.no/thredds/catalog/arcticdata/infranor/NINA-FLOX/raw/catalog.html associated with https://doi.org/10.21343/ZDM7-JD72 under a CC-BY-4.0 license. Results obtained from the first three years in operation showed interannual variation in SIF and other spectral vegetation indices including MERIS Terrestrial Chlorophyll Index (MTCI), EVI and NDVI. Synergistic uses of the measurements from this northernmost hyperspectral FLoX sensor, in conjunction with other monitoring systems, will advance our understanding of how tundra vegetation responds to changing climate and the resulting implications on carbon and energy balance. Chlorophyll fluorescenceSolar Induced Fluorescence (SIF)ReflectancePhotosynthetic functionMERIS terrestrial chlorophyll index (MTCI)High-Arctic tundrapublishedVersio

Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales

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

Assessing across-scale optical diversity and productivity relationships in grasslands of the Italian alps

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation

Unmanned Aerial Systems (UAS)-Based Methods for Solar Induced Chlorophyll Fluorescence (SIF) Retrieval with Non-Imaging Spectrometers: State of the Art

Chlorophyll fluorescence (ChlF) information offers a deep insight into the plant physiological status by reason of the close relationship it has with the photosynthetic activity. The unmanned aerial systems (UAS)-based assessment of solar induced ChlF (SIF) using non-imaging spectrometers and radiance-based retrieval methods, has the potential to provide spatio-temporal photosynthetic performance information at field scale. The objective of this manuscript is to report the main advances in the development of UAS-based methods for SIF retrieval with non-imaging spectrometers through the latest scientific contributions, some of which are being developed within the frame of the Training on Remote Sensing for Ecosystem Modelling (TRuStEE) program. Investigations from the Universities of Edinburgh (School of Geosciences) and Tasmania (School of Technology, Environments and Design) are first presented, both sharing the principle of the spectroradiometer optical path bifurcation throughout, the so called ‘Piccolo-Doppio’ and ‘AirSIF’ systems, respectively. Furthermore, JB Hyperspectral Devices’ ongoing investigations towards the closest possible characterization of the atmospheric interference suffered by orbital platforms are outlined. The latest approach focuses on the observation of one single ground point across a multiple-kilometer atmosphere vertical column using the high altitude UAS named as AirFloX, mounted on a specifically designed and manufactured fixed wing platform: ‘FloXPlane’. We present technical details and preliminary results obtained from each instrument, a summary of their main characteristics, and finally the remaining challenges and open research questions are addressed. On the basis of the presented findings, the consensus is that SIF can be retrieved from low altitude spectroscopy. However, the UAS-based methods for SIF retrieval still present uncertainties associated with the current sensor characteristics and the spatio-temporal mismatching between aerial and ground measurements, which complicate robust validations. Complementary studies regarding the standardization of calibration methods and the characterization of spectroradiometers and data processing workflows are also required. Moreover, other open research questions such as those related to the implementation of atmospheric correction, bidirectional reflectance distribution function (BRDF) correction, and accurate surface elevation models remain to be addressed

Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

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

Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

Este estudio investiga las diferentes actuaciones de ópticas sobre los índices para estimar la producción primaria bruta (GPP) del estrato herbáceo de una sabana mediterránea con diferente disponibilidad de nitrógeno (N) y de fósforo (P). La fluorescencia de la clorofila inducida por el sol sobre el rendimiento calculado en 760 nm (FY760), escala de índice de reflectancia fotoquímica (sPRI), MERIS terrestre (índice de clorofila MTCI) y el índice de vegetación de diferencia normalizada (NDVI) fueron calculadas desde cerca de la superficie y las mediciones de espectroscopia de campo recolectados se hicieron utilizando espectrómetros de alta resolución espectral, que abarcan las regiones del infrarrojo cercano visible. La GPP fue medida utilizando cámaras de dosel en las mismas localidades muestreadas por los espectrómetros. Hemos probado si la eficiencia del uso de los modelos de luz (LUE) impulsados por cantidades de teledetección (RSMs) pueden hacer un mejor seguimiento de los cambios en la GPP causada por fuentes de nutrientes en comparación con aquellos impulsados exclusivamente por datos meteorológicos (MM). En particular, comparamos los espectáculos de diferentes formulaciones de RSM -basándose en la utilización de FY760 o sPRI como proxy para LUE y NDVI MTCI o como una fracción de la radiación fotosintéticamente activa absorbida (APAR f)- con las clásicas de MM. Los resultados mostraron mayor GPP en la N -parcelas experimentales fertilizadas durante el período de crecimiento. Estas diferencias en la GPP desaparecieron en el período de secado, cuando los efectos de la senescencia enmascarada contiene diferencias de potencial debido a la planta N. Por consiguiente, MTCI estaba estrechamente relacionada con la media de la planta N, contenida a través de tratamientos (r2 D 0:86, p < 0:01), porque estaba mal relacionados con GPP (r2 D 0:45, p < 0:05). Por el contrario sPRI y FY760 se correlacionaban bien con GPP durante todo el período de medición. Los resultados revelaron que la relación entre el GPP y FY760 no es única en los tratamientos, pero no se ve afectada por la disponibilidad de N. Los resultados de un análisis de validación cruzada mostró que el MM (AICcv D 127, MEcv D 0:879) superó a RSM (AICcv D 140, MEcv D 0:8737,) cuando la humedad del suelo fue utilizada para restringir la dinámica estacional de LUE. Sin embargo, el análisis residual demostró que las predicciones de GPP con MM son inexactas cuando no revela explícitamente unas variables climáticas en cambios relacionados con el parámetro de nutrientes LUE. Estos resultados sugieren que RSM es un medio valioso para diagnosticar los efectos inducidos por los nutrientes en la actividad fotosintética.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 tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) 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 a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (f APAR) – with those of classical MM. Results showed 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 closely related to the mean of plant N content across treatments (r2 D 0:86, p < 0:01), it was poorly related to GPP (r2 D 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 D 127, MEcv D 0:879) outperformed RSM (AICcv D 140, MEcv D 0:8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that GPP predictions with MM are inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results suggest that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.Trabajo financiado por: Alexander von Humboldt Foundation y la Max Planck Research AwardpeerReviewe

Nutrient induced changes in Sun-Induced Fluorescence emission in a Mediterranean grassland

Sun induced fluorescence (SIF), the radiation flux emitted by plant chlorophylls molecules in the 650-800 nm spectral window, is considered an indicator of photosynthetic performance. Recently it has been shown that SIF can track changes in light use efficiency (LUE), and therefore it is a good predictor of gross primary production (GPP) at various scales, from leaves and ecosystem to regional and global scale. Although SIF has been successfully used to predict GPP in various ecosystems, the mechanistic link between GPP and SIF remains not fully understood, and especially the effect of function and structure on SIF at the canopy scale remains an active area of research. SIF is emitted by the whole canopy, but only a fraction of the total emission is observed with remote sensing techniques. The escape probability of SIF (Fesc) controls the amount of SIF scattered by the canopy and is integral to separate the effect of canopy structure and function on the fluorescence signal. In this contribution we make use of data collected at the research site Majadas del Tietar, a Mediterranean grassland manipulated with Nitrogen and Phosphorus. Using the SCOPE model (Soil Canopy Observation Photochemistry and Energy fluxes) we obtain Fesc and we analyse how Top of canopy SIF and emitted SIF vary in response to the fertilization. With a combination of processes-based modelling and data driven analysis, such as relative importance analysis and structural equation modelling, we unravel the processes and causal relationship that are at the base of the GPP - SIF relationship. We show that the nutrient fertilization had an effect on plant composition, and therefore canopy structure, but also plant functioning. Nitrogen induced changes in biodiversity mainly affect leaf angle distribution of the canopy and therefore scattering properties such as Fesc. The nitrogen fertilization is also responsible for a change in plant functioning, with altered SIF emission. The simultaneous change of both canopy and structure causes the fertilization effect to be visible mainly at the emission level, but not at top of canopy, as the variation in canopy structure masks the change observed at leaf level. This contribution advances the knowledge of the highly complex dynamics involved in the GPP-SIF relationship. In depth understanding of the mechanistic processes is required to fully take advantage of the increasingly prevalent SIF data streams

Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production

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.Peer reviewe