Solar-induced Chlorophyll Fluorescence Observations Research Articles

Simulations of solar-induced chlorophyll fluorescence over crop canopies using the integrated APSIM model

Agricultural production models predict crop yield by accounting for a variety of species, cultivar, farming management, and environmental impacts on crop photosynthesis. Without suitable constraints, however, large uncertainties may exist in simulations of crop photosynthesis. Recent advances in retrieving solar-induced chlorophyll fluorescence (SIF) at the top-of-canopy (TOC) have provided a promising measurement for crop photosynthesis. Within the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFtoc) which can be measured by remote sensing platforms. The new model (APSIM-SIF) first estimates the leaf-level chlorophyll fluorescence emitted over the full SIF spectrum (SIFtot_full) according to CO2 assimilation in crops. The model then mechanistically decomposes the conversion from SIFtot_full to SIFtoc into two factors: the SIF band conversion factor (ɛ) and the fluorescence escape ratio (fesc) that represent the impact of leaf physiological status and plant structure properties, respectively. ɛ can be estimated using leaf structural and biochemical parameters as inputs; fesc for near-infrared SIF can be expressed as a function of directional reflectance in the near-infrared region (RNIR), Normalized Difference Vegetation Index (NDVI), and the fraction of PAR absorbed by crops (fAPAR). The APSIM-SIF model determined more than 90% of the variation in gross primary productivity (GPP), aboveground biomass and leaf area index (LAI) measurements for maize (Zea mays L.) at two AmeriFlux sites in the U.S. Midwest and it also captured the seasonality of SIF (R2 = 0.84) and GPP (R2 = 0.81) well at an irrigated maize site in China. The APSIM-SIF model was also applied to the simulation of TOC SIF emission of maize and soybean (Glycine max L.) in the U.S. Midwest during the 2018 growing season. The simulated SIFtoc accounted for more than 75% of the variability of daily satellite SIF observations for grid squares with more than 70% crop area. The main contribution of this study lies in two aspects: (1) a physically-based framework is proposed to incorporate the SIF module to the APSIM-DCaPST model, and (2) the two important factors used in this framework (ɛ and fesc) remains largely constant during the peak growing season. These findings provide a theoretically robust and operational basis for linking SIF observations with crop growth.

Phenological and physiological responses of the terrestrial ecosystem to the 2019 drought event in Southwest China: Insights from satellite measurements and the SSiB2 model

• The drought in Southwest China severely suppressed plant phenology and physiology. • Chlorophyll fluorescence sensitively responds to changes in water conditions. • Satellite chlorophyll fluorescence contributes to model evaluation and improvement. Understanding plant phenological and physiological changes in response to drought will provide key insight into the response of terrestrial ecosystems to climate change, but is still limited due to the increased drought severity and frequency in recent decades. Here, we combine solar-induced chlorophyll fluorescence (SIF) along with SSiB2 (Simplified Simple Biosphere Model) simulations to investigate the plant phenological and physiological responses to the 2019 drought in southwestern China. Our results show that this 2019 drought had substantial impacts on vegetation phenology and photosynthesis due to the soil moisture deficit in spring, while the rewatering process in July alleviated the water deficit and reduced drought damage to plants. Moreover, SIF observations provide a physiology-related vegetation response, as the recovery of plant photosynthesis indicated by fluorescence yield ( S I F yield ) is much stronger than the recovery of greenness described by vegetation indices during the rewatering in July. The SSiB2 simulations captured the physiological response of plants to moisture deficit during drought period, while the lack of realistic energy dissipation mechanisms under stressed conditions may lead to discrepancies in the timing of peak response to drought. Our findings highlight the prospective application of remote sensing SIF measurements in monitoring the timely response of plant physiology to changes in water conditions and to provide important information for model evaluation and improvement.

Sun-induced chlorophyll fluorescence is more strongly related to photosynthesis with hemispherical than nadir measurements: Evidence from field observations and model simulations

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a novel proxy for terrestrial gross primary production (GPP). A growing number of ground-based automatic SIF observation systems equipped with hemispherical-conical and bi-hemispherical observation configurations have been developed in synergy with EC flux measurements across different ecosystems. However, the difference in the canopy SIF observed by these two types of configurations has not been well studied, which poses challenges in evaluating their performance in tracking GPP. In this study, we investigated SIF from both hemispherical-conical and bi-hemispherical observation configurations for their ability to track GPP in a maize field during the 2020 growth season. We found that bi-hemispherical SIF observations (SIFHemis) showed higher correlations with GPP at both diurnal and seasonal scales, and the superiority of SIFHemis for GPP estimation was also supported by Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulations. In addition, we found that the SIFHemis-GPP model established at a satellite overpass time (e.g., 09:30) outperformed the corresponding SIFNadir-GPP model in estimating both the half-hourly and daily GPP. The underlying mechanism for the advantage of this SIFHemis-GPP relationship was elucidated by a simplified geometrical optical model, which showed that the diurnal patterns of the observed sunlit and shaded leaves for the SIFHemis were consistent with those of the canopy GPP. Our study recommends a bi-hemispherical configuration setup for its superiority in monitoring GPP dynamics.

Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning

Accurately partitioning net ecosystem exchange (NEE) into ecosystem respiration (ER) and gross primary productivity (GPP) is critical for understanding the terrestrial carbon cycle. The standard partitioning methods rely on simplified empirical models, which have inherent structural errors. These structural errors lead to biased GPP and ER estimation, especially during extreme events (e.g., drought) and human disturbances (e.g., crop harvest). Recently, solar-induced chlorophyll fluorescence (SIF) has been shown to be well correlated to GPP, thus offering a path to improve the NEE partitioning by constraining GPP. However, the ecosystem-scale relationship between GPP and SIF remains limited. Here, we show that neural networks informed by SIF observations (NNSIF) can be successfully used to partition NEE, while simultaneously learning the ecosystem-scale GPP-SIF relationship. NNSIF was compared against standard partitioning methods and NN without SIF constraint (NNnoSIF), using field data from different ecosystems and synthetic data generated by a coupled fluorescence-photosynthesis model (SCOPE). NNSIF showed superior performance as: (1) it effectively improves the ER estimation, especially at high temperature, (2) it better captures the moisture limitation on ER, (3) it more accurately estimates LUE variations to stress, and (4) it uniquely captures the rapid GPP drop after land management (harvest). Furthermore, NNSIF can retrieve the GPP-SIF relationship at the ecosystem scale, and elucidate how this relationship responds to environmental conditions. Overall, our algorithm provides the first direct and non-empirical estimate of the ecosystem-scale GPP-SIF relationship, without relying on any prior empirical assumptions on the relationships between CO2 fluxes, climatic drivers, and SIF. The new knowledge learned by NNSIF can help better estimate global-scale GPP using satellite SIF, especially during extreme events and in the presence of land management.

A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet)

Abstract. Gross primary productivity (GPP) is the sum of leaf photosynthesis and represents a crucial component of the global carbon cycle. Space-borne estimates of GPP typically rely on observable quantities that co-vary with GPP such as vegetation indices using reflectance measurements (e.g., normalized difference vegetation index, NDVI, near-infrared reflectance of terrestrial vegetation, NIRv, and kernel normalized difference vegetation index, kNDVI). Recent work has also utilized measurements of solar-induced chlorophyll fluorescence (SIF) as a proxy for GPP. However, these SIF measurements are typically coarse resolution, while many processes influencing GPP occur at fine spatial scales. Here, we develop a convolutional neural network (CNN), named SIFnet, that increases the resolution of SIF from the TROPOspheric Monitoring Instrument (TROPOMI) on board of the satellite Sentinel-5P by a factor of 10 to a spatial resolution of 500 m. SIFnet utilizes coarse SIF observations together with high-resolution auxiliary data. The auxiliary data used here may carry information related to GPP and SIF. We use training data from non-US regions between April 2018 until March 2021 and evaluate our CNN over the conterminous United States (CONUS). We show that SIFnet is able to increase the resolution of TROPOMI SIF by a factor of 10 with a r2 and RMSE metrics of 0.92 and 0.17 mW m−2 sr−1 nm−1, respectively. We further compare SIFnet against a recently developed downscaling approach and evaluate both methods against independent SIF measurements from Orbiting Carbon Observatory 2 and 3 (together OCO-2/3). SIFnet performs systematically better than the downscaling approach (r=0.78 for SIFnet, r=0.72 for downscaling), indicating that it is picking up on key features related to SIF and GPP. Examination of the feature importance in the neural network indicates a few key parameters and the spatial regions in which these parameters matter. Namely, the CNN finds low-resolution SIF data to be the most significant parameter with the NIRv vegetation index as the second most important parameter. NIRv consistently outperforms the recently proposed kNDVI vegetation index. Advantages and limitations of SIFnet are investigated and presented through a series of case studies across the United States. SIFnet represents a robust method to infer continuous, high-spatial-resolution SIF data.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5.

Recent advances in satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf‐level to canopy‐level is usually not well‐represented. Here, we incorporate the simulation of far‐red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf‐level fluorescence yield was simulated by a parametric simplification of the Soil Canopy‐Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf‐level to top‐of‐canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R 2 > 0.91, root‐mean‐square error <0.19 W⋅m−2⋅sr−1⋅μm−1), and captured the day‐to‐day variation of tower‐measured SIF at temperate forest sites (R 2 > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite‐observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Detecting mangrove photosynthesis with solar-induced chlorophyll fluorescence

ABSTRACT Accurately understanding the photosynthesis of mangroves is essential for inferring how climate change influences the terrestrial and marine carbon cycle. However, photosynthesis of mangroves remains poorly understood due to the difficulty of in situ flux data acquisition. Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a direct proxy for photosynthesis which would be an effective tool for detecting mangrove photosynthesis. We presented the first SIF observations from both ground and unmanned aerial vehicle (UAV) platforms in a mangrove forest and provided an exploratory attempt to downscale SIF from canopy level to leaf level to better characterize photosynthesis. We found that relationships between mangrove photosynthesis and SIF vary by light condition, which exhibited a negative correlation under low-light condition but positive correlation under high-light condition. Moreover, we found that inter-species differences affect the relationship between photosynthesis and SIF in mangroves. Relationship exhibited more clear-cut correlation by distinguishing mangrove species than considering them as a whole. Our results would help to better understand the photosynthesis mechanism of the mangrove forests and fills the gap of the relationship between SIF and photosynthesis in subtropical and tropical wetland ecosystems.

Direct estimation of photosynthetic CO2 assimilation from solar-induced chlorophyll fluorescence (SIF)

Much progress has been made in predicting terrestrial gross primary productivity (GPP) from solar-induced chlorophyll fluorescence (SIF). However, SIF-GPP relationships are mostly built by statistically relating top-of-canopy (TOC) SIF observations (SIFTOC) to eddy covariance flux-tower GPP estimates. We developed a process-based model, based on the mechanistic light response (MLR) model, to mechanistically link SIFTOC with the photosynthetic activity of vegetation. To apply this mechanistic model at the canopy scale, we 1) reformulate the equations by replacing the fraction of open PSII reaction centers (qL) and the maximum quantum yield of photosystem II (ФPmax) with nonphotochemical quenching (NPQ) and the quantum yield of photosystem II (ΦP); 2) reconstruct hemispherical broadband SIF fluxes at photosystem II (PSII) from the directional observed SIFTOC that is contributed from photosystem I and II; 3) estimate other key parameters including KDF (ratio between the rate constants for constitutive heat loss and fluorescence), Cc (chloroplastic CO2 partial pressure), and Γ* (chloroplastic compensation point of CO2) at the canopy scale based on assumptions and in-situ measurements. A comparison against flux-tower based GPP at a winter-wheat study site, demonstrates that the modeled GPP, driven by SIFTOC at 760 nm, air temperature, incoming photosynthetically active radiation (PAR), and directional reflectance in the red and near-infrared region, is able to quantify canopy photosynthesis with good accuracy at both half-hourly (R2 = 0.85, RMSE = 5.62 μmol m−2 s−1, rRMSE = 9.10%) and daily (R2 > 0.90, RMSE = 3.25 μmol m−2 s−1 and rRMSE = 8.69%) scales. The present model enhances our ability to mechanistically estimate GPP with SIF at the canopy scale, an essential step to model carbon uptake using satellite SIF at regional and global scales.

Retrieval of Red Solar-Induced Chlorophyll Fluorescence With TROPOMI on the Sentinel-5 Precursor Mission

Solar-Induced chlorophyll Fluorescence (SIF) retrieved from spaceborne hyperspectral measurements can provide a synoptic perspective on the functioning of photosynthetic active vegetation and algae. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 precursor mission provides data for the retrieval of SIF with an unprecedented resolution in the spatial and temporal dimensions. In this study, we propose an approach for the retrieval of the red SIF peak value (at around 685 nm) over both land and ocean from TROPOMI measurements. This approach exploits the solar Fraunhofer lines located in the spectral window of 669–686 nm to disentangle SIF from the solar radiation reflected by the surface–atmosphere system. First, through simulated TROPOMI-like datasets, we determine the proper parameter settings and demonstrate the feasibility of the approach for the red SIF retrieval from TROPOMI data. Then, the approach is applied to real TROPOMI measurements. The red SIF retrieved with the proposed approach for two months’ TROPOMI data is compared with another TROPOMI red SIF dataset and the marine normalized fluorescence line height (nFLH) product of the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons display similar spatial distributions over both land and ocean for the weekly global composites. Especially, the comparison of the two red SIF datasets demonstrates good agreement overall, indicating consistency of the two retrieval approaches. The retrieval uncertainty for the weekly global composite is about 13% and 10% of the peak SIF value over land/ocean, respectively, which can be considered as satisfactory error thresholds for global SIF observations.

Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.

Far-Red Chlorophyll Fluorescence Radiance Tracks Photosynthetic Carbon Assimilation Efficiency of Dark Reactions

Solar-induced chlorophyll fluorescence (SIF) observations from space have shown close relationships with terrestrial photosynthesis rates. SIF originates from the light reactions of photosynthesis, whereas carbon fixation takes place during the dark reactions of photosynthesis. Questions remain regarding whether SIF is able to track changes in the efficiency of the dark reactions in photosynthesis. Using concurrent measurements of leaf-scale gas exchange, pulse amplitude-modulated (PAM) fluorescence, and fluorescence spectral radiances, we found that both far-red fluorescence radiances and PAM fluorescence yields responded rapidly to changes in photosynthetic carbon assimilation due to changes in environmental factors or induced stomatal closure under constant light conditions. Uncertainties in outgoing and incoming irradiance mismatch for SIF measurements may very likely obscure the contributions of the dark reactions, thereby causing the inconsistent findings previously reported, which were no change in far-red SIF and PAM fluorescence yields after clear reductions in the photosynthetic carbon assimilation efficiency of dark reactions. Our results confirm that high-quality SIF measurements have the potential to provide insights into the dark reactions of photosynthesis. This study is particularly relevant for better interpreting satellite SIF observations that are obtained under roughly constant overpass times and relatively stable light intensities.

TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production

Solar-induced chlorophyll fluorescence (SIF) observed by satellites has advanced the monitoring of terrestrial photosynthesis regionally and globally. The relationship between SIF and gross primary production (GPP) at leaf, canopy, and ecosystem scales has received tremendous attention in recent years. It remains controversial whether the SIF-GPP relationship at the ecosystem scale is universal or dependent upon vegetation type. New SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with unprecedented high spatial and temporal resolution provide a new opportunity to elucidate the SIF-GPP relationship. Here, we examine the SIF-GPP relationship for seven major vegetation types across the U.S. with TROPOMI SIF and in-situ GPP data for 83 eddy covariance flux sites. We find that TROPOMI SIF shows a strong and consistent relationship with tower-based GPP at both satellite footprint and grid-cell levels. The slope of the SIF-GPP relationship is similar among all the vegetation types except croplands, demonstrating a nearly universal (converging to ~13.5 g C m−2 d−1/W m−2 μm−1 sr−1) rather than vegetation type-specific SIF-GPP relationship. This confirms that TROPOMI SIF can be used as a proxy for GPP across a wide variety of vegetation types, and can also be used to quantify GPP by avoiding uncertainty associated with land cover maps. The C4 crops have a much higher slope than the C3 crops, and therefore croplands tend to have a higher slope than C3-dominated vegetation types (e.g., forests, shrublands, savannas). We also find that the TROPOMI SIF is well correlated with GPP under normal or wetter conditions, while their relationship becomes weaker under water stress. Our TROPOMI-based study could improve our understanding of the SIF-GPP relationship at the ecosystem scale and advance the mapping of GPP globally with SIF observations from space.

Assessing impacts of canopy 3D structure on chlorophyll fluorescence radiance and radiative budget of deciduous forest stands using DART

Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition (e.g., proportion of direct and diffuse irradiance) and observational geometric configurations (e.g., sun and viewing directions). Radiative transfer (RT) models have the potential to provide a better understanding of the canopy structural effects on the SIF emission and RS signals. Here, we used the DART model to assess the daily influence, from morning to evening, of forest 3D architecture on SIF nadir radiance, emission, escape factor and nadir yield of eight 100 m × 100 m forest study plots established in a temperate deciduous forest of the Smithsonian Environmental Research Center (Edgewater, MD, USA). The 3D architecture of each plot was derived from airborne LiDAR. DART simulations of these 3D forest plots and their 1D (i.e., vertical profile of sun-adapted and shade-adapted leaves) and 0D (i.e., homogeneous layer of sun-adapted leaves above an homogeneous layer of shade-adapted leaves) abstractions were compared to assess the relative errors (ε1D−3D and ε0D−3D) associated with horizontal and vertical structural heterogeneity, respectively. Forest 3D structure, especially horizontal heterogeneity, had a great influence on forest nadir SIF radiance, resulting in ε1D−3D up to 55% at 8:00 and 18:00 (i.e., for oblique sun directions). The key indicators of this impact, in the descending order of importance, were the SIF escape factor (ε1D−3D up to 40%), the attenuation of incident photosynthetically active radiation (ε1D−3D less than 5%), and the SIF emission yield (ε1D−3D less than 2%). The influence of forest architecture on the nadir SIF escape factor and SIF yield (ε1D−3D up to 40%) varied over time, with differences in forest stand structure, and per spectral domain, being always larger between 640 and 700 nm than between 700 and 850 nm. In addition, woody elements demonstrated a large influence on forest SIF radiance due to their “shading” effect (ε up to 17%) and their “blocking” effect (ε ≈ 10%), both of them higher for far-red than for red SIF. These results underline the importance of 3D forest canopy architecture, especially 2D heterogeneity, and inclusion of woody elements in RT modeling used for interpretation of the RS SIF signal, and subsequently for the estimation of gross primary production and detection of vegetation stress.

Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

AbstractThe interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.

Solar-induced chlorophyll fluorescence is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during the fall transition

Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUEp), chlorophyll fluorescence yield (ΦF), and the fraction of emitted SIF photons escaping from the canopy (fesc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUEp and ΦF to air temperature. While LUEp reached saturation at high air temperatures, ΦF did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level ΦF (R2 = 0.84) implying that ΦF could be more closely related to pigment pool changes rather than LUEp. In addition, we found that the fesc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUEp to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest.

Improving the Capability of the SCOPE Model for Simulating Solar-Induced Fluorescence and Gross Primary Production Using Data from OCO-2 and Flux Towers

Solar-induced chlorophyll fluorescence (SIF) measured from space has shed light on the diagnosis of gross primary production (GPP) and has emerged as a promising way to quantify plant photosynthesis. The SCOPE model can explicitly simulate SIF and GPP, while the uncertainty in key model parameters can lead to significant uncertainty in simulations. Previous work has constrained uncertain parameters in the SCOPE model using coarse-resolution SIF observations from satellites, while few studies have used finer resolution SIF measured from the Orbiting Carbon Observatory-2 (OCO-2) to improve the model. Here, we identified the sensitive parameters to SIF and GPP estimation, and improved the performance of SCOPE in simulating SIF and GPP for temperate forests by constraining the physiological parameters relating to SIF and GPP by combining satellite-based SIF measurements (e.g., OCO-2) with flux tower GPP data. Our study showed that SIF had weak capability in constraining maximum carboxylation capacity (Vcmax), while GPP could constrain this parameter well. The OCO-2 SIF data constrained fluorescence quantum efficiency (fqe) well and improved the performance of SCOPE in SIF simulation. However, the use of the OCO-2 SIF alone cannot significantly improve the GPP simulation. The use of both satellite SIF and flux tower GPP data as constraints improved the performance of the model for simulating SIF and GPP simultaneously. This analysis is useful for improving the capability of the SCOPE model, understanding the relationships between GPP and SIF, and improving the estimation of both SIIF and GPP by incorporating satellite SIF products and flux tower data.

Practical approaches for normalizing directional solar-induced fluorescence to a standard viewing geometry

Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) have improved the capabilities of monitoring large-scale Gross Primary Productivity (GPP). However, SIF observations are subject to directional effects which can lead to considerable uncertainties in various applications. Practical approaches for normalizing directional SIF observations to nadir viewing, to minimize the directional effects, have not been well studied. Here we developed two practical and physically-solid approaches for removing the directional effects of anisotropic SIF observations: one is based on near-infrared or red reflectance of vegetation (NIRv and Redv), and the other is based on the kernel-driven model with multi-angular SIF measurements. The first approach uses surface reflectance while the second approach directly leverages multi-angular SIF measurements. The performance of the two approaches was evaluated using a dataset of multi-angular measurements of SIF and reflectance collected with a high-resolution field spectrometer over different plant canopies. Results show that the relative mean absolute errors between the normalized nadir SIF and the observed SIF at nadir decrease by 3–6% (far-red) and 6–8% (red) for the first approach, and by 7–13% and 6–11% for the second approach, compared to the original data, respectively. The effectiveness and simplicity of our proposed approaches provide great potential to generate long-term and consistent SIF data records with minimized directional effects.

A model for estimating transpiration from remotely sensed solar-induced chlorophyll fluorescence

Terrestrial evapotranspiration (ET) is an important flux that links global cycles of carbon, water and energy and is largely driven by transpiration (T) through leaf stomata in vegetated areas during the growing season. ET, however, remains one of the most uncertain hydrological variables at the global scale. In this study, we proposed a semi-mechanistic model for estimating terrestrial T by deriving an analytical solution between solar-induced chlorophyll fluorescence (SIF) and stomatal conductance (gc) as well as vapor pressure deficit (VPD), combining theories on the photosynthetic pathway and optimal stomatal behavior. The relationships of SIF-ETR and ETR-gc·VPD0.5 was calibrated by the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model. This model was validated by hourly canopy SIF and concurrent eddy covariance flux observations at both forest and cropland ecosystems. Results showed that the SIF combined with VPD can better predict gc than using SIF alone with a more consistent seasonal trends found in both SIF and gc·VPD0.5. The correlation between gc·VPD0.5 and SIF was stronger than those between gc and SIF and between gc and VIs. Canopy T was accurately predicted from SIF at both hourly (R2 > 0.65) and daily (R2 > 0.76) scales and was also successfully estimated using SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) at cropland ecosystems. In comparison with empirical relationships of directly linking gc with SIF or VIs, the proposed model produced latent heat flux (λE) estimation in best agreement with measured values at all three sites. Our model could be a step forward in understanding the coupling of carbon and water cycles and may be used in ecosystem models for improving ET estimation over large areas.

Elucidating space based observations of solar induced chlorophyll fluorescence over terrestrial vegetation of India

Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics. Space based observations of solar-induced chlorophyll fluorescence (SIF) provides an alternative non-invasive approach to investigate gross primary production (GPP), a key element of ecological research. Orbiting carbon observatory (OCO-2) is providing continuous global measurements of SIF at 757 nm and 771 nm wavelengths with nadir and glint mode observations. Using one of the three spectrometer instruments, measurements of SIF complements OCO-2’s Carbon dioxide data with information on when and where plants are drawing carbon from the atmosphere. Here, we present spatio-temporal patterns of space borne observations of SIF and its correlation with GPP and vegetation indices over diverse terrestrial vegetation of India. The results indicate a spatial variability in SIF ranging from 0.001 to 0.6 (± 0.16) Wm−2 µm−1 sr−1 and 0.001 to 0.7 (± 0.2) Wm−2 µm−1 sr−1 in nadir and glint modes, respectively. OCO-2 derived SIF at finer scale shows a similar trend with MODIS derived GPP during September–October–November and June–July–August seasons. A linear correlation of satellite derived SIF with GPP (Pearson’s coefficient, r = 0.84), EVI (r = 0.72) and NDVI (r = 0.63) at 757 nm for 0.05° × 0.05° grid over terrestrial India is reported, besides seasonal variations. Amongst the diverse vegetation types, SIF and GPP are well correlated over Evergreen broadleaf forests. The study provides new insights and useful inputs to GPP modelling and improved understanding of terrestrial carbon cycle research in India.

Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence

Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C3 or C4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C3 and one for C4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 ± 6.54 PgC/year for the 2015–2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected ‘hotspots’ of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.