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[EN] The first satellite-based global retrievals of terrestrial sun-induced chlorophyll fluorescence (SIF) were achieved in 2011. Since then, a number of global SIF datasets with different spectral, spatial, and temporal sampling characteristics have become available to the scientific community. These datasets have been useful to monitor the dynamics and productivity of a range of vegetated areas worldwide, but the coarse spatiotemporal sampling and low signal-to-noise ratio of the data hamper their application over small or fragmented ecosystems. The recent advent of the Copernicus Sentinel-5P TROPOMI mission and the high quality of its data products promise to alleviate this situation, as TROPOMI provides daily global measurements at a much denser spatial and temporal sampling than earlier satellite instruments. In this work, we present a global SIF dataset produced from TROPOMI measurements within the TROPOSIF project funded by the European Space Agency. The current version of the TROPOSIF dataset covers the time period between May 2018 and April 2021. Baseline SIF retrievals are derived from the 743¿758¿nm window. A secondary SIF dataset derived from an extended fitting window (735¿758¿nm window) is included. This provides an enhanced signal-to-noise ratio at the expense of a higher sensitivity to atmospheric effects. Spectral reflectance spectra at seven 3¿nm windows devoid of atmospheric absorption within the 665¿785¿nm range are also included in the TROPOSIF dataset as an important ancillary variable to be used in combination with SIF. The methodology to derive SIF and ancillary data as well as results from an initial data quality assessment are presented in this work. The TROPOSIF dataset is available through the following digital object identifier, This research has been supported by the European Space Agency (grant no. 4000127461/19/I-NS) and the National Aeronautics and Space Administration (grant no. NNX15AH95G)

The Geostationary Carbon Cycle Observatory (GeoCarb), to be launched in 2023, will be capable of measuring solar-induced chlorophyll fluorescence (SIF) and add to the current space-based record which started in 1995. GeoCarb will be unique as it will be the first geostationary satellite capable of sensing SIF over the American continents. Consequently, SIF measurements from GeoCarb can be performed much more flexibly compared to polar-orbiting platforms. With its scan mirror assembly, the instrument can point to any location on the Earth disc. This will allow measurements to be collected at various times of day, and measurement locations can be re-visited several times within a day. In expectation of the launch, we conduct an extensive, SIF-focused simulation study and explore the capability and limitations of the instrument and its particular sampling approach. Using cloud information from real measurements, as well as other observation- and model-based data, we produce over four million atmospheric simulations of GeoCarb measurements that the instrument would see throughout a full day. We then apply dedicated SIF retrieval algorithms on the simulated spectra and investigate the results along with cloud-screening performance and emergent retrieval biases and subsequent bias correction. Finally, we make comparisons with currently operating instruments where appropriate and show future science users of GeoCarb SIF what a typical day of measurements will yield.

Far‐red solar‐induced chlorophyll fluorescence (SIF) has been retrieved from multiple satellites with nearly continuous global coverage since 1996. Multiple official and research‐grade retrievals provide a means for cross validation across sensors and algorithms, but produces substantial variation across products due to differences in instrument characteristics and retrieval algorithm. The lack of a consistent, calibrated SIF data set hampers scientific interpretation of planetary photosynthesis. NASA's Orbiting Carbon Observatory 2 (OCO‐2) offers small sampling footprints, high data acquisition, and repeating spatially resolved targets at bioclimatically diverse locations, providing a unique benchmark for spaceborne sensors traceable to ground data. We leverage overlap between the longer running Global Ozone Monitoring Instrument version 2 (GOME‐2) SIF time series, and more recent state‐of‐the‐art OCO‐2 and TROPOspheric Monitoring Instrument (TROPOMI) data, in a first attempt to reconcile inconsistencies in the long‐term record. After screening and correcting for key instrument differences (time of day, wavelength, Sun‐sensor geometry, cloud effects, footprint area), we find that Global Ozone Monitoring Instrument version 2 and TROPOspheric Monitoring Instrument perform exceedingly well in capturing spatial, seasonal, and interannual variability across OCO‐2 targets. However, Global Ozone Monitoring Instrument version 2 retrieval methods differ by up to a factor of 2 in signal‐to‐noise and magnitude. Magnitude differences are largely attributed to retrieval window choice, with wider windows producing higher magnitudes. The assumed SIF spectral shape has negligible effect. Substantial research is needed to understand remaining sensitivities to atmospheric absorption and reflectance. We conclude that OCO‐2 and TROPOspheric Monitoring Instrument have opened up the possibility to produce a multidecadal SIF record with well‐characterized uncertainty and error quantification for overlapping instruments, enabling back‐calibration of previous instruments and production of a consistent, research‐grade, harmonized time series.

Traditional methods of carbon monitoring in mountainous regions are challenged by complex terrain. Recently, solar‐induced fluorescence (SIF) has been found to be an indicator of gross primary production (GPP), and the increased availability of remotely sensed SIF provides an opportunity to estimate GPP across the Western United States. Although the empirical linkage between SIF and GPP is strong, the current mechanistic understanding of this linkage is incomplete and depends upon changes in leaf biochemical processes in which absorbed sunlight leads to photochemistry, heat (via nonphotochemical quenching [NPQ]), fluorescence, or tissue damage. An improved mechanistic understanding is necessary to leverage SIF observations to improve representation of ecosystem processes within land surface models. Here we included an improved fluorescence model within the Community Land Model, Version 4.5 (CLM 4.5), to simulate seasonal changes in SIF at a subalpine forest in Colorado. We found that when the model accounted for sustained NPQ, this provided a larger seasonal change in fluorescence yield leading to simulated SIF that more closely resembled the observed seasonal pattern (Global Ozone Monitoring Experiment‐2 [GOME‐2] satellite platform and a tower‐mounted spectrometer system). We found that an acclimation model based on mean air temperature was a useful predictor for sustained NPQ. Although light intensity was not an important factor for this analysis, it should be considered before applying the sustained NPQ and SIF to other cold climate evergreen biomes. More leaf‐level fluorescence measurements are necessary to better understand the seasonal relationship between sustained and reversible components of NPQ and to what extent that influences SIF.

Novel satellite measurements of solar-induced chlorophyll fluorescence (SIF) can improve our understanding of global photosynthesis; however, little is known about how to interpret the controls on its spectral variability. To address this, we disentangle simultaneous drivers of fluorescence spectra by coupling active and passive fluorescence measurements with photosynthesis. We show empirical and mechanistic evidence for where, why, and to what extent leaf fluorescence spectra change. Three distinct components explain more than 95% of the variance in leaf fluorescence spectra under both steady-state and changing illumination conditions. A single spectral shape of fluorescence explains 84% of the variance across a wide range of species. The magnitude of this shape responds to absorbed light and photosynthetic up/down regulation; meanwhile, chlorophyll concentration and nonphotochemical quenching control 9% and 3% of the remaining spectral variance, respectively. The spectral shape of fluorescence is remarkably stable where most current satellite retrievals occur (far-red, >740nm), and dynamic downregulation of photosynthesis reduces fluorescence magnitude similarly across the 670- to 850-nm range. We conduct an exploratory analysis of hourly red and far-red canopy SIF in soybean, which shows a subtle change in red:far-red fluorescence coincident with photosynthetic downregulation but is overshadowed by longer-term changes in canopy chlorophyll and structure. Based on our leaf and canopy analysis, caution should be taken when attributing large changes in the spectral shape of remotely sensed SIF to plant stress, particularly if data acquisition is temporally sparse. Ultimately, changes in SIF magnitude at wavelengths greater than 740 nm alone may prove sufficient for tracking photosynthetic dynamics. Plain Language Summary Satellite remote sensing provides a global picture of photosynthetic activity-allowing us to see when, where, and how much CO2 plants are assimilating. To do this, satellites measure a small emission of energy from the plants called chlorophyll fluorescence. However, this measurement is typically made across a narrow wavelength range, while the emission spectrum (650-850 nm) is quite dynamic. We show where, why, and to what extent leaf fluorescence spectra change across a diverse range of species and conditions, ultimately informing canopy remote sensing measurements. Results suggest that wavelengths currently used by satellites are stable enough to track the downregulation of photosynthesis resulting from stress, while spectral shape changes respond more strongly to dynamics in canopy structure and chlorophyll concentration.

The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future.

Observations of solar‐induced chlorophyll a fluorescence (SIF) from spaceborne spectrometers can advance our understanding of terrestrial and aquatic carbon cycles. Here we present the first global retrievals of SIF at red wavelengths from the TROPOspheric Monitoring Instrument (TROPOMI). Despite the weak signal level, considerable uncertainties, and subtle measurement artifacts, spatial patterns and magnitudes agree with independent data sets. Over land, spatial patterns of our red SIF estimates covary with the far‐red SIF data. Red SIF over the ocean is highly consistent with the normalized fluorescence line height (nFLH) inferred from measurements of the MODerate resolution Imaging Spectroradiometer (MODIS), even when comparing single days and fine spatial scales. Major advantages of our Fraunhofer line‐based SIF retrievals include the capability to sense SIF through optically thin cloud/aerosol layers and an insensitivity to ocean color. This opens up new avenues for studying ocean biogeochemistry from space.

Remote sensing of solar‐induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top‐of‐canopy SIF using ground‐based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco‐physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal‐ and spectral resolution ground‐based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground‐based SIF retrieval. Analysis included field measurements from bi‐hemispherical and hemispherical‐conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer‐based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF‐PAR relationships and diurnal consistency across different sky conditions, while O₂A‐based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF‐PAR relationships across sky conditions. Such behavior holds across system configurations, though hemispherical‐conical systems diverge less across sky conditions. O₂A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF‐photosynthesis relationships interpreted by previous studies using O₂A‐based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer‐based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O₂A‐based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far‐red SIF retrievals across sky conditions.

Timely and accurate monitoring of crops is essential for food security. Here, we examine how well solar-induced chlorophyll fluorescence (SIF) can inform crop productivity across the United States. Based on tower-level observations and process-based modeling, we find highly linear gross primary production (GPP):SIF relationships for C4 crops, while C3 crops show some saturation of GPP at high light when SIF continues to increase. C4 crops yield higher GPP:SIF ratios (30�50) primarily because SIF is most sensitive to the light reactions (does not account for photorespiration). Scaling to the satellite, we compare SIF from the TROPOspheric Monitoring Instrument (TROPOMI) against tower-derived GPP and county-level crop statistics. Temporally, TROPOMI SIF strongly agrees with GPP observations upscaled across a corn and soybean dominated cropland (R2 = 0.89). Spatially, county-level TROPOMI SIF correlates with crop productivity (R2 = 0.72; 0.86 when accounting for planted area and C3/C4 contributions), highlighting the potential of SIF for reliable crop monitoring. ©2020. American Geophysical Union. All Rights Reserved.

The Chlorophyll Fluorescence Imaging Spectrometer (CFIS) is an airborne high resolution imaging spectrometer built at NASA's Jet Propulsion Laboratory (JPL) for evaluating solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2). OCO-2 is a NASA mission designed to measure atmospheric CO_2 but one of the novel data products is SIF, retrieved using reductions in the optical depth of Fraunhofer lines in OCO-2’s O_2 A-band, covering 757–775 nm at 0.042 nm spectral resolution. CFIS was specifically designed to retrieve SIF within the wavelength range of OCO-2, but extends further down to 737 nm, nearly maintaining the high spectral resolution of the OCO-2 instrument (0.07 vs. 0.042 nm). Here, we provide an overview of the instrument calibration and performance as well as the retrieval strategy based on non-linear weighted least-squares. To illustrate the retrieval performance using actual flight data, we focus on data acquired over agricultural fields in Mead, Nebraska from an unpressurized Twin Otter (DHC-6) aircraft at a flight altitude of 3000 m above ground level (AGL). Spectral residuals are consistent with expected detector noise, which enables us to compute realistic 1-σ precision errors of 0.5–0.7 W/m^2/sr/μm for typical SIF retrievals, which can be reduced to

On regional to global scales, few constraints exist on gross primary productivity (GPP) and ecosystem respiration (Re) fluxes. Yet constraints on these fluxes are critical for evaluating and improving terrestrial biosphere models. In this study, we evaluate the seasonal cycle of GPP, Re, and net ecosystem exchange (NEE) produced by four terrestrial biosphere models and FLUXCOM, a data‐driven model, over northern midlatitude ecosystems. We evaluate the seasonal cycle of GPP and NEE using solar‐induced fluorescence retrieved from the Global Ozone Monitoring Experiment‐2 and column‐averaged dry‐air mole fractions of CO2 (XCO2) from the Total Carbon Column Observing Network, respectively. We then infer Re by combining constraints on GPP with constraints on NEE from two flux inversions. An ensemble of optimized Re seasonal cycles is generated using five GPP estimates and two NEE estimates. The optimized Re curves generally show high consistency with each other, with the largest differences due to the magnitude of GPP. We find optimized Re exhibits a systematically broader summer maximum than modeled Re, with values lower during June–July and higher during the fall than Re. Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency (possibly due to an ecosystem‐scale Kok effect) and to seasonal variations in the leaf litter and fine root carbon pool. The results suggest that the inclusion of variable carbon use efficiency for autotrophic respiration and carbon pool dependence for heterotrophic respiration is important for accurately simulating Re.

Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.

International audience

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.

Several decade-long satellite retrievals of solar-induced chlorophyll fluorescence (SIF) have become available during the past few years, but understanding the long-term dynamics of SIF and elucidating its co-variation with historical gross primary production (GPP) remains a challenge. Part of the challenge is due to the lack of direct comparability among these SIF products as they are derived from various satellite platforms with different retrieval methods, instruments characteristics, overpass time, and viewing-illumination geometries. This study presents a framework that circumvents these discrepancies and allows the harmonization of SIF products from multiple instruments to achieve long-term coverage. We demonstrate this framework by fusing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Global Ozone Monitoring Experiment 2 (GOME-2) onboard MetOp-A developed at German Research Center for Geosciences (GFZ). We first downscale both original SIF datasets from their native resolutions to 0.05° ( SIF ¯ GOME2_005 and SIF ¯ SCIA_005 respectively) using machine learning (ML) algorithms imposed with regionalization constraints to account for the varying relationships between predictors and SIF in space and time. We then apply the cumulative distribution function (CDF) matching technique to correct the offset between SIF ¯ GOME2_005 and SIF ¯ SCIA_005 inherited from the original instrumental discrepancies to generate a harmonized SIF time series from 2002 to present ( SIF ¯ 005). Finally, we quantify the uncertainty of SIF ¯ 005. SIF ¯ 005 is validated with 1) the original retrievals to ensure the spatial and temporal variabilities are preserved, 2) airborne SIF derived from the Chlorophyll Fluorescence Imaging Spectrometer (CFIS, R2 = 0.73), and 3) ground-based SIF measurements at a subalpine coniferous forest (R2 = 0.91). The SIFyield derived from SIF ¯ 005 has high seasonal consistency with the ground measurements (R2 = 0.93), suggesting that the harmonized product SIF ¯ 005 carries physiological information beyond the absorbed photosynthetically active radiation. Additionally, SIF ¯ 005 has a good capability for large-scale stress monitoring as demonstrated with several major historical drought and heatwave events. The framework developed in this study sets the stage for future development of even more advanced SIF products from all SIF-capable satellite platforms once issues related to inter-sensor calibration are resolved and SIF physiology is better understood.

In recent years, solar-induced chlorophyll fluorescence (SIF) retrieved from space borne spectrometers has been extensively used as a proxy for terrestrial photosynthesis at relatively sparse temporal and spatial scales. The near-infrared band of the recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF in a spectral range devoid of atmospheric absorption features. We find that initial TROPOMI spectra meet high expectations for a substantially improved spatio-temporal resolution (up to 7 km × 3.5 km pixels with daily revisit), representing a step change in SIF remote sensing capabilities. However, interpretation requires caution, as the broad range of viewing-illumination geometries covered by TROPOMI’s 2600 km wide swath needs to be taken into account. A first inter-sensor comparison with OCO-2 (Orbiting Carbon Observatory-2) SIF shows excellent agreement, underscoring the high quality of TROPOMI’s SIF retrievals and the notable radiometric performance of the instrument. PLAIN LANGUAGE SUMMARY: Photosynthesis is the most essential process for life on Earth, but gradually changing environmental conditions such as increasing concentrations of atmospheric trace gases, rising temperatures or reduced water availability could adversely affect the photosynthetic productivity. The recently launched TROPOspheric Monitoring Instrument (TROPOMI) is designed to monitor atmospheric trace gases and air pollutants with an unprecedented resolution in space and time, while its radiometric performance also permits us to see a weak electromagnetic signal emitted by photosynthetically active vegetation - solar induced chlorophyll fluorescence (SIF). Mounting evidence suggests that SIF observations from satellite instruments augment our abilities to track the photosynthetic performance and carbon uptake of terrestrial vegetation. In this study, we present the first TROPOMI SIF retrievals, largely outperforming previous and existing capabilities for a spatial continuous monitoring of SIF from space.

Quantifying gross primary production (GPP), the largest flux of the terrestrial carbon cycle, remains difficult at the landscape scale. Evergreen needleleaf (coniferous) forests in the western United States constitute an important carbon reservoir whose annual GPP varies from year‐to‐year due to drought, mortality, and other ecosystem disturbances. Evergreen forest productivity is challenging to determine via traditional remote sensing indices (i.e., NDVI and EVI), because detecting environmental stress conditions is difficult. We investigated the utility of solar‐induced chlorophyll fluorescence (SIF) to detect year‐to‐year variation in GPP in four coniferous forests varying in species composition in the western United States (Sierra Nevada, Cascade, and Rocky Mountains). We show that annually averaged, satellite‐based observations of SIF (retrieved from GOME‐2) were significantly correlated with annual GPP observed at eddy covariance towers over several years. Further, SIF responded quantitatively to drought‐induced mortality, suggesting that SIF may be capable of detecting ecosystem disturbance in coniferous forests.

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Two new remote sensing vegetation parameters derived from spaceborne spectrometers and simulated with a three dimensional radiative transfer model have been evaluated in terms of their prospects and drawbacks for the monitoring of dense vegetation canopies: (i) sun-induced chlorophyll fluorescence (SIF), a unique signal emitted by photosynthetically active vegetation and (ii) the canopy scattering coefficient (CSC), a vegetation parameter derived along with the directional area scattering factor (DASF) and expected to be particularly sensitive to leaf optical properties. Here, we present the first global data set of DASF/CSC and examine the potential of CSC and SIF for providing complementary information on the controversially discussed vegetation seasonality in Amazon forests. A comparison between near-infrared SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument and the Orbiting Carbon Observatory-2 (OCO-2) (overpass time in the morning and noon, respectively) reveals the response of SIF to instantaneous photosynthetically active radiation (PAR). Large-scale seasonal swings of GOME-2 SIF amount up to 21% (regarding the annual maximum) and peak in October and around February, while OCO-2 SIF peaks in February. However, both time series agree very well if SIF is normalized by overpass time and wavelength. We further examine anistropic reflectance characteristics with the finding that the hot spot effect significantly impacts observed GOME-2 SIF values. On the contrary, our sensitivity analysis suggests that CSC is highly independent of sun-sensor geometry as well as atmospheric effects. The slight annual variability of CSC (3%) shows no clear seasonal cycle, while a relatively high spatial standard deviation points to a high degree of spatial heterogeneity in our study domain within the central Amazon Basin.

INTRODUCTION Reliable estimation of gross primary production (GPP) from landscape to global scales is pivotal to a wide range of ecological research areas, such as carbon-climate feedbacks, and agricultural applications, such as crop yield and drought monitoring. However, measuring GPP at these scales remains a major challenge. Solar-induced chlorophyll fluorescence (SIF) is a signal emitted directly from the core of photosynthetic machinery. SIF integrates complex plant physiological functions in vivo to reflect photosynthetic dynamics in real time. The advent of satellite SIF observation promises a new era in global photosynthesis research. The Orbiting Carbon Observatory-2 (OCO-2) SIF product is a serendipitous but critically complementary by-product of OCO-2’s primary mission target—atmospheric column CO 2 ( X CO 2 ). OCO-2 SIF removes some important roadblocks that prevent wide and in-depth applications of satellite SIF data sets and offers new opportunities for studying the SIF-GPP relationship and vegetation functional gradients at different spatiotemporal scales. RATIONALE Compared with earlier satellite missions with SIF capability, the OCO-2 SIF product has substantially improved spatial resolution, data acquisition, and retrieval precision. These improvements allow satellite SIF data to be validated, for the first time, directly against ground and airborne measurements and also used to investigate the SIF-GPP relationship and terrestrial ecosystem functional dynamics with considerably better spatiotemporal credibility. RESULTS Coordinated airborne measurements of SIF with the Chlorophyll Fluorescence Imaging Spectrometer (CFIS) were used to validate OCO-2 retrievals. The validation shows close agreement between OCO-2 and CFIS SIF, with a regression slope of 1.02 and R 2 of 0.71. Landscape gradients in SIF emission, corresponding to differences in vegetation types, were clearly delineated by OCO-2, a capability that was lacking in previous satellite missions. The SIF-GPP relationships at eddy covariance flux sites in the vicinity of OCO-2 orbital tracks were found to be more consistent across biomes than previously suggested. Finally, empirical orthogonal function (EOF) analyses on OCO-2 SIF and available GPP products show highly consistent spatiotemporal correspondence in their leading EOF modes across the globe, suggesting that SIF and GPP are governed by similar dynamics and controlled by similar environmental and biological conditions. CONCLUSION OCO-2 represents a major advance in satellite SIF remote sensing. Our analyses suggest that SIF is a powerful proxy for GPP at multiple spatiotemporal scales and that high-quality satellite SIF is of central importance to studying terrestrial ecosystems and the carbon cycle. Although the possibility of a universal SIF-GPP relationship across different biome types cannot be dismissed, in-depth process-based studies are needed to unravel the true nature of covariations between SIF and GPP. Of critical importance in such efforts are the potential coordinated dynamics between the light-use efficiencies of CO 2 assimilation and fluorescence emission in response to changes in climate and vegetation characteristics. Eventual synergistic uses of SIF with atmospheric CO 2 enabled by OCO-2 will lead to more reliable estimates of terrestrial carbon sources and sinks—when, where, why, and how carbon is exchanged between land and atmosphere—as well as a deeper understanding of carbon-climate feedbacks. The marked ecological gradients depicted by OCO-2’s high-resolution SIF measurements along a transect of temperate deciduous forests, crops, and urban area from Indiana to suburban Chicago, Illinois.

Sun-induced fluorescence (SIF) is an electromagnetic signal emitted by the chlorophyll of green leaves which has been shown to be a good proxy for plant photosynthetic activity. Recent developments in spaceborne spectrosopy have led to the derivation of the first global maps of SIF from a number of space-based spectrometers. This contribution will provide an overview on the state-of-the-art of terrestrial SIF monitoring in terms of existing and upcoming satellite missions and application fields.