Recent advances in remote sensing of solar‐induced chlorophyll fluorescence (SIF) have garnered wide interest from the biogeoscience and Earth system science communities, due to the observed ...linearity between SIF and gross primary productivity (GPP) at increasing spatiotemporal scales. Three recent studies, Maguire et al., (2020, https://doi.org/10.1029/2020GL087858), He et al. (2020, https://doi.org/10.1029/2020GL087474), and Marrs et al. (2020, https://doi.org/10.1029/2020GL087956) highlight a nonlinear relationship between fluorescence and photochemical yields and show empirical evidence for the decoupling of SIF, stomata, and the carbon reactions of photosynthesis. Such mechanistic studies help advance our understanding of what SIF is and what it is not. We argue that these findings are not necessarily contradictory to the linear SIF‐GPP relationship observed at the satellite scale and provide context for where, when, and why fluorescence and photosynthesis diverge at smaller spatiotemporal scales. Understanding scale dependencies of remote sensing data is crucial for interpreting SIF as a proxy for GPP.
Plain Language Summary
When exposed to light, plants re‐emit a small amount of light from chlorophyll molecules called fluorescence. Remote sensing instruments are now capable of measuring chlorophyll fluorescence (which is emitted between 650–850 nm) from canopies to the globe (solar‐induced chlorophyll fluorescence; SIF). A growing number of papers have highlighted an empirical linear relationship between SIF and whole‐ecosystem photosynthesis (gross primary productivity; GPP). These advances have excited the broader Earth science research community, but recent studies have pointed out that the linear SIF‐GPP relationship at coarse scales does not hold true at smaller spatiotemporal scales. In this commentary, we synthesize three recent studies that provide insight into the relationship between fluorescence and photosynthesis at leaf and canopy scales, under natural and controlled conditions. At fine spatiotemporal scales, fluorescence can be decoupled with photosynthetic carbon uptake, but we argue that satellite measurements are often too coarse in time and space to observe the SIF‐photosynthesis decoupling and that the integration of canopy processes explains the observed linearity. As such, SIF plays an important role as an estimate of GPP at spatial and temporal scales relevant for monitoring global terrestrial productivity, benchmarking terrestrial biosphere and earth system models, and managing ecosystems.
Key Points
Solar‐induced fluorescence (SIF) is widely used as a remote estimate of ecosystem gross primary productivity (GPP), but why does it work?
Three recent studies point to inherent nonlinearities in the fluorescence‐photosynthesis relationship at fine spatiotemporal scales
We synthesize mechanisms to suggest that these results are not contradictory to the increasingly linear SIF:GPP relationship across scales
In recent years, solar‐induced chlorophyll fluorescence (SIF) retrieved from spaceborne 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 spatiotemporal 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 2,600‐km‐wide swath needs to be taken into account. A first intersensor 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 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 TROPOspheric Monitoring Instrument SIF retrievals, largely outperforming previous and existing capabilities for a spatial continuous monitoring of SIF from space.
Key Points
We present the first solar‐induced chlorophyll fluorescence (SIF) observations from TROPOMI near‐infrared band measurements
TROPOMI enables unprecedented spatiotemporal resolution of global SIF maps
Intersensor comparison between TROPOMI and OCO‐2 SIF data shows excellent agreement
Solar-Induced Chlorophyll Fluorescence (SIF) is an emission of light in the 650–850 nm spectral range from the excited state of the chlorophyll-a pigment after absorption of photosynthetically active ...radiation (PAR). As this is directly linked to the electron transport chain in oxygenic photosynthesis, SIF is a powerful proxy for photosynthetic activity. SIF observations are relatively new and, while global scale measurements from satellites using high-resolution spectroscopy of Fraunhofer bands are becoming more available, observations at the intermediate canopy scale using these techniques are sparse.
We present a novel ground-based spectrometer system - PhotoSpec - for measuring SIF in the red (670–732 nm) and far-red (729–784 nm) wavelength range as well as canopy reflectance (400–900 nm) to calculate vegetation indices, such as the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the photochemical reflectance index (PRI). PhotoSpec includes a 2D scanning telescope unit which can be pointed to any location in a canopy with a narrow field of view (FOV = 0.7°). PhotoSpec has a high signal-to-noise ratio and spectral resolution, which allows high precision solar Fraunhofer line retrievals over the entire fluorescence wavelength range under all atmospheric conditions using a new two-step linearized least-squares retrieval procedure.
Initial PhotoSpec observations include the diurnal SIF cycle of single broad leaves, grass, and dark-light transitions. Results from the first tower-based measurements in Costa Rica show that the instrument can continuously monitor SIF of several tropical species throughout the day. The PhotoSpec instrument can be used to explore the relationship between SIF, photosynthetic efficiencies, Gross Primary Productivity (GPP), and the impact of canopy radiative transfer, viewing geometry, and stress conditions at the canopy scale.
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•PhotoSpec is a new instrument for sensitive red and far-red SIF measurements.•PhotoSpec measures canopy reflectance to calculate vegetation indices.•PhotoSpec allows spatially resolved SIF observations of leaves and canopies.
Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated ...whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO₂ eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI’s midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.
Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, ...additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1… λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.
•X, y, z, time, and laser return intensity constitute the 5-dimensions of LiDAR.•We review recent advances to highlight novel applications of LiDAR beyond 3D.•Beyond 3D LiDAR has and will enable a wide range of new research and applications.
Recent advances in the retrieval of Chl fluorescence from space using passive methods (solar-induced Chl fluorescence, SIF) promise improved mapping of plant photosynthesis globally. However, ...unresolved issues related to the spatial, spectral, and temporal dynamics of vegetation fluorescence complicate our ability to interpret SIF measurements.
We developed an instrument to measure leaf-level gas exchange simultaneously with pulse-amplitude modulation (PAM) and spectrally resolved fluorescence over the same field of view – allowing us to investigate the relationships between active and passive fluorescence with photosynthesis.
Strongly correlated, slope-dependent relationships were observed between measured spectra across all wavelengths (Fλ
, 670–850 nm) and PAM fluorescence parameters under a range of actinic light intensities (steady-state fluorescence yields, F
t) and saturation pulses (maximal fluorescence yields, F
m). Our results suggest that this method can accurately reproduce the full Chl emission spectra – capturing the spectral dynamics associated with changes in the yields of fluorescence, photochemical (ΦPSII), and nonphotochemical quenching (NPQ).
We discuss how this method may establish a link between photosynthetic capacity and the mechanistic drivers of wavelength-specific fluorescence emission during changes in environmental conditions (light, temperature, humidity). Our emphasis is on future research directions linking spectral fluorescence to photosynthesis, ΦPSII, and NPQ.
Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO₂ but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. ...Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R² = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.
Remotely sensed data that are sensitive to rapidly changing plant physiology can provide real-time information about crop responses to abiotic conditions. The Photochemical Reflectance Index (PRI) ...has shown promise when measured at short timesteps to remotely estimate dynamics in xanthophyll pigment interconversion — a plant photoprotective mechanism that results in lowered photosynthetic efficiency. To gain a better understanding of this dynamic spectral response to environmental conditions, we investigated PRI over two seasons (2013 and 2014) in rainfed soft white spring wheat (Triticum aestivum L.). Highly temporally resolved (measurement frequency=five minutes) in-situ radiometric measurements of PRI were collected at field plots of varying nitrogen (N) and soil water conditions (n=16). To represent the diurnal magnitude of xanthophyll pigment interconversion, we use a delta PRI (ΔPRI) derived from a midday PRI (xanthophyll de-epoxidation state) and an early morning PRI (xanthophyll epoxidation state). We hypothesize that ΔPRI can empirically deconvolve the diurnally changing (facultative) from the seasonally changing (constitutive) component of the PRI signal. In this study, ΔPRI demonstrated less sensitivity than an uncorrected PRI to leaf area index (LAI) and leaf chlorophyll content throughout the growing season. ΔPRI was correlated with continuous, unattended crop responses associated with vapor pressure deficit (0.50>R2>0.48), stomatal conductance (R2=0.47), and air temperature (0.42>R2>35). Further, the sensitivity with which ΔPRI responded to solar radiation under varying N treatments and periods of soil water availability (surplus, depletion, and deficit) suggests that crop growth may be inhibited by a xanthophyll cycle mediated stress response, detectable by ΔPRI. A major implication of these findings is that highly temporally and spatially resolved ΔPRI data could be used to track plant status in response to changing environmental conditions.
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•Highly time resolved crop spectral reflectance was investigated over two seasons.•Diurnal variation in wheat photosynthetic physiology was measured using ∆PRI.•∆PRI was sensitive to abiotic environmental conditions across 16 wheat canopies.•∆PRI response to solar radiation was highly correlated to biomass accumulation.
Summary
Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote ...sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated.
Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2 flux measurements, and a predictive model to simulate seasonal canopy color dynamics.
We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color.
These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.
•Daily NDVI best predicted harvesting metrics during the mid- and late season.•Phenological metrics were derived using smoothed, daily NDVI data.•NDVI derived phenological metrics improved ...predictions of harvest metrics.•Grain protein and N were primarily driven by phenology during reproductive stages.
Automated, low-cost and field-deployable remote sensing tools are well suited for continuously monitoring crop growth and providing growers with timely information about crop performance. Because automated sensors provide information about crop development and performance across time, we examined the hypothesis that ground-based canopy reflectance data might define crop phenology in new ways over the course of the season that can better forecast crop yield, protein, biomass, and grain nitrogen at harvest. This study examines the utility of daily Normalized Difference Vegetation Index (NDVI) data to monitor crop phenology over two complete growing seasons. Spectral reflectance data was collected at a total of sixteen plots under four different applied nitrogen (N) and soil water availability scenarios in rainfed soft white spring wheat (Triticum aestivum L.). Using NDVI at solar noon, four phenological periods were derived from the data using a non-parametric regression locally weighted smoothing parameter (loess) to account for day to day variability, and piecewise linear regression to determine inflection points in the seasonal NDVI curve. The NDVI derived phenological metrics (i.e. the change in NDVI per day, and duration (in days) of each phenological period) were compared against daily NDVI values throughout the season to predict harvest metrics. Daily NDVI data were generally poor predictors of harvest metrics early in the growing season, and reached maximum predictive power at the onset of heading, and the middle of ripening for biomass and yield (R2∼0.50 and ∼0.25 during heading, respectively, and R2∼0.50 during early ripening). Conversely, using both simple and multiple regression analysis, we found that harvest metrics were better explained using the rate and duration of NDVI derived phenological periods. Simple regressions between NDVI derived phenological metrics revealed several physiologically and management relevant correlations including strong, statistically significant (p<0.05) relationships between the rate of tillering and stem extension and total biomass (R2=0.63 and 0.54, respectively), the duration of heading and yield (R2=0.67), the rate of ripening and grain protein concentration (R2=0.45), and the duration of ripening and grain N content (R2=0.43), for example. Using multiple regression analysis, 83% of the variance in yield, 67% in protein concentration, 87% in total biomass, and 80% in grain N was explained by two to three NDVI derived phenological metrics. Further, multiple regression analysis using NDVI derived phenological metrics from the early season (tillering and stem extension) substantially improved early prediction of yield and biomass as compared to daily NDVI data, whereas protein and grain N were primarily driven by metrics associated with the reproductive development of the crop (heading and ripening). This work has implications for improving in-season management decisions and understanding of the phenological drivers of harvest metrics using daily NDVI data as an evaluation tool.
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