Solar-induced Chlorophyll Fluorescence Observations Research Articles

Understanding Terrestrial Water and Carbon Cycles and Their Interactions Using Integrated SMAP Soil Moisture and OCO‐2 SIF Observations and Land Surface Models

AbstractRecently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

The reconstructed solar-induced chlorophyll fluorescence dataset reveals the almost ubiquitous close relationship between vegetation transpiration and solar-induced chlorophyll fluorescence

The precise estimation of vegetation transpiration (T) holds significant implications for enhancing our understanding of global carbon, water, and energy cycles. Nonetheless, vegetation transpiration stands out as one of the most challenging hydrological variables to capture on a global scale. In recent years, the newly introduced solar-induced chlorophyll fluorescence (SIF) has gained widespread application in the monitoring of photosynthesis within terrestrial ecosystems. It holds promise as a potential parameter for the precise estimation of vegetation transpiration. At various scales (leaf, canopy, and ecosystem), the relationship between SIF and vegetation transpiration has garnered widespread attention. Previous investigations predominantly relied on tower-based SIF or coarse-resolution satellite observations of SIF to assess the association between SIF and vegetation transpiration. The reconstituted SIF data (continuous SIF, CSIF) offer heightened spatial resolution, presenting novel opportunities for elucidating the SIF-T relationship. Nevertheless, it remains uncertain whether the relationship between SIF and vegetation transpiration is universally applicable or contingent upon specific land cover types. This study globally analyzes the relationship between SIF and vegetation transpiration utilizing reconstituted SIF data (CSIF) in conjunction with vegetation transpiration data acquired from 109 flux tower sites worldwide, encompassing 11 distinct land cover types. The results indicate that: (1) CSIF exhibits strong correlations with site T on both 8-day and monthly temporal scales, with the correlation between CSIF and site T surpassing that of traditional MODIS vegetation indices and site T. (2) Across the 11 land cover types, the majority of the slopes in the CSIF-T linear relationships are essentially consistent, indicating that the relationship between SIF and T is nearly universal rather than specific to particular land cover types. (3) The slope of the CSIF-T linear relationship in C4 vegetation is higher than that in C3 vegetation, indicating that the SIF-T relationship is contingent upon the photosynthetic pathway. (4) A robust linear relationship between CSIF and T emerges under favorable environmental conditions, while under extreme conditions such as water stress, the linear correlation between SIF and T diminishes. Our research findings enhance our understanding of the SIF-T relationship, offering a scientific basis for reliable T estimation on a global scale.

Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests.

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle-scale chlorophyll fluorescence measurements with tower-based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar-induced chlorophyll fluorescence (SIF) and solar intensity-normalized SIF (SIFrelative) under snow-covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near-infrared vegetation index [NIRv] under 50% snow cover). Needle-scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower-based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data-driven and process-based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems.

A practical approach for extracting the photosystem II (PSII) contribution to near-infrared solar-induced chlorophyll fluorescence

Recent studies have indicated a good potential for using solar-induced chlorophyll fluorescence (SIF) to estimate photosynthetic CO2 assimilation. SIF can be emitted by both Photosystem I (PSI) and Photosystem II (PSII), but it is the SIF signals from PSII which are related to photosynthetic carbon fixation. However, since top-of-canopy SIF observations (SIFTOC) always contain contributions from both photosystems, to mechanistically estimate gross primary productivity (GPP) from SIF, it is essential to extract PSII SIF from SIFTOC. Based on the differences in the relative contribution of PSII across different wavelengths, we propose a practical approach for extracting PSII contribution to SIFTOC at the near-infrared (NIR) band (fPSII_760) using measurements of SIFTOC in the red and NIR spectral regions. A leaf-scale concurrent instrument was developed to assess the response of fPSII_760 under varying environments. For winter-wheat leaves, as light intensity increased from 0 to 400 μmol m−2 s−1, fPSII_760 rose from 0.6 to 0.8; with further increase in light intensity to 1800 μmol m−2 s−1, fPSII_760 consistently decreased to 0.65. There was a slight decreasing trend in fPSII_760 with rising temperatures, with values dropping from 0.65 at 15 °C to 0.61 at 40 °C. We found that variations in fPSII_760 are due to changes in the fluorescence yield of PSII, with the two having a positively proportional relationship. We also estimated canopy-scale fPSII_760 for a winter-wheat study site: fPSII_760 varied from 0.61 to 0.83, with a mean value of 0.78 during the peak growing season. A comparison with eddy covariance-derived GPP reveals that GPP estimated with dynamic fPSII_760 was more accurate than that calculated using fixed fPSII_760, with R2 increasing from 0.6 to 0.84. This study contributes to a deeper understanding of the link between SIF and photosynthetic CO2 assimilation, paving the way for more effective use of SIF to estimate GPP.

Improved estimation of gross primary productivity (GPP) using solar-induced chlorophyll fluorescence (SIF) from photosystem II

Solar-induced chlorophyll fluorescence (SIF) contains contributions from both photosystem I (PSI) and photosystem II (PSII). In theory, SIF emitted from PSII (SIFPSII) should be extracted from at-sensor SIF to quantify photosynthetic CO2 assimilation, as PSI fluorescence yield is nearly insensitive to changes in photochemical yield. In many SIF-related studies, the fraction of chlorophyll-absorbed energy allocated to PSII (β2), a key factor controlling the flux of excitation energy for SIFPSII, is simply assigned a fixed value. However, β2 is regulated in response to variations in environmental conditions to avoid an energy imbalance between PSI and PSII. By quantifying the regulating effect of the cytochrome b6f complex (Cyt b6f) on the electron transport from PSII to PSI, and its interaction with energy dissipation in both photosystems, we develop a framework to estimate β2 and PSII fluorescence yield (ΦF2), two key determinants for SIFPSII, from SIF emission, PAR, air temperature, the maximum carboxylase activity of Rubisco (Vcmax), and the maximum activity of Cyt b6f (JCB6F_max). The framework is equipped with a two-leaf scheme, enabling us to estimate SIFPSII for sunlit and shaded leaves from top-of-canopy SIF observations (SIFTOC). Our simulation results showed that β2 and ΦF2 tend to change in opposite directions with varying PAR, making the contribution of PSII to SIFTOC relatively constant. We compare gross primary productivity (GPP) mechanistically estimated from SIFPSII obtained with fixed (GPPSIF_Fβ) and dynamic β2 (GPPSIF_Dβ) against the eddy-tower-derived GPP (GPPEC) at a winter-wheat experiment site. At a half-hourly time scale, GPPSIF_Dβ is better than GPPSIF_Fβ, showing higher correlations with GPPEC (R2 = 0.74 versus R2 = 0.64 and RMSE = 6.61 μmol m−2 s−1 versus RMSE = 7.67 μmol m−2 s−1). The study provides a practical way to estimate the contribution of SIFPSII to SIFTOC, giving a better theoretical basis to SIF-based GPP estimation models.

Estimation of global ecosystem isohydricity from solar-induced chlorophyll fluorescence and meteorological datasets

Plants exhibit varying strategies for optimizing the trade-off between CO2 uptake and water loss through transpiration in response to increasing air or soil dryness. Anisohydric plants generally keep their stomata open to maintain or enhance carbon uptake, but this exposes them to a greater risk of hydraulic failure. In contrast, isohydric plants tend to maintain hydraulic integrity by enforcing stricter stomatal and xylem regulation, albeit at the expense of reduced carbon assimilation. Information on the degree of ecosystem isohydricity (σ) is important for predicting plant mortality during drought. However, the current σ estimation method lacks a physiological basis by not explicitly accounting for the photosynthesis process. Recent advances in observing solar-induced chlorophyll fluorescence (SIF), an effective proxy for photosynthetic rate, provides new potential to estimate σ at regional and global scales. Based on the revised mechanistic light response (rMLR) model, we developed a mechanistic, SIF-based model of ecosystem-scale isohydricity forced by satellite SIF observations and meteorological datasets. This model was used to estimate daily global ecosystem isohydricity from 2019 to 2020 at a spatial resolution of 0.25°. During the study period, the mean ecosystem isohydricity of global ecosystems was 0.75, suggesting that, on the whole, global terrestrial ecosystems tend to exhibit more anisohydric behavior. Specifically, herbaceous vegetation mostly displaying highly anisohydric behavior, while woody vegetation tended to be more isohydric. Ecosystem isohydricity exhibits clear seasonality, becoming more isohydric in summer. A random-forest analysis revealed that the three most important factors influencing global ecosystem isohydricity are net photosynthesis rate (Anet), canopy height (h), and vapor pressure deficit (VPD), which collectively accounted for 86.1% of the importance. We also found that ecosystem isohydricity reflects a plant-environment interaction, with the contribution of intrinsic hydraulic traits likely exceeding 50%. The proposed model enables us to incorporate plant physiological controls into the estimation of ecosystem-scale σ, thereby enhancing our ability to track plant water-use strategies in response to rising water stress in a changing climate.

CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations

Solar-induced chlorophyll fluorescence (SIF) has shown promise in estimating gross primary production (GPP); however, there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis. This study introduces a new global 0.05° SIF-based GPP dataset (CMLR GPP, based on Canopy-scale Mechanistic Light Reaction model) using TROPOMI observations. A modified mechanistic light response model was employed at the canopy scale to generate this dataset. The canopy q L (opened fraction of photosynthesis II reaction centers), required by the CMLR model, was parameterized using a random forest model. The CMLR GPP estimates showed a strong correlation with tower-based GPP ( R 2 = 0.72) in the validation dataset, and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO-2-based SIF product) GPP at a global scale. The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, as well as different plant functional types and most months of the year. In conclusion, CMLR GPP is a novel global GPP dataset based on mechanistic frameworks, whose availability is expected to contribute to future research in ecological and geobiological regions.

Global retrieval of the spectrum of terrestrial chlorophyll fluorescence: First results with TROPOMI

Solar-Induced chlorophyll Fluorescence (SIF) could be used as an indicator of photosynthetic status due to the close relationship between SIF and the photosynthetic apparatus. Terrestrial SIF is emitted throughout the red and near-infrared spectrum and is characterized by two peaks centered around 685 nm and 740 nm, respectively. In this study, we present a data-driven approach to reconstruct the terrestrial SIF spectrum from measurements by TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 precursor mission. This approach makes use of solar Fraunhofer lines in the combined spectral windows devoid of strong atmospheric absorption features to retrieve SIF signal from the solar radiation reflected by the surface and atmosphere system. Information contents are mainly from the two windows close to the red and far-red SIF peaks, 663–686 nm and 743–758 nm. A linear forward model represented as an addition of the SIF-free radiance spectrum and the SIF component is proposed with a proper selection of its parameter settings. The SIF component was simulated as linear combinations of 2 basis SIF spectra. Through inverting the linear forward model, the SIF spectrum was retrieved from the solar radiation reflected by the surface and atmosphere system. The evaluation of the retrieval results is performed by inter-comparison with other SIF datasets. The comparisons display similar spatial distributions for the weekly global SIF composites for the first two weeks in June and December of 2019 and July and December of 2021. Especially the comparison of the far-red SIF datasets with other dedicated far-red SIF retrievals demonstrates close agreement, indicating consistency among the retrieval approaches. The reconstructed TROPOMI red SIF shows improved and more reasonable spatiotemporal distributions. The retrieval uncertainty for the weekly global composite is about 12% and 2% of the peak red and far-red SIF values, respectively, which can be considered as satisfactory error thresholds for global composites of SIF observations. Different spectral features for several typical biomes from reconstructed SIF spectra suggest that red and far-red SIF may carry complementary information on photosynthetic function and biophysical properties of the plant. For the first time, the reconstruction of the SIF spectrum is achieved for spaceborne measurements with the potential to open new applications for better understanding of the ecosystem function.

Improving the Estimation of Canopy Fluorescence Escape Probability in the Near-Infrared Band by Accounting for Soil Reflectance

Solar-induced chlorophyll fluorescence (SIF) has been found to be a useful indicator of vegetation’s gross primary productivity (GPP). However, the directional SIF observations obtained from a canopy only represent a portion of the total fluorescence emitted by all the leaf photosystems because of scattering and reabsorption effects inside the leaves and canopy. Hence, it is crucial to downscale the SIF from canopy level to leaf level by modeling fluorescence escape probability (fesc) for improved comprehension of the relationship between SIF and GPP. Most methods for estimating fesc rely on the assumption of a “black soil background,” ignoring soil reflectance and the effect of scattering between soils and leaves, which creates significant uncertainties for sparse canopies. In this study, we added a correction factor considering soil reflectance, which was modeled using the Gaussian process regression algorithm, to the semi-empirical NIRv/FAPAR model and obtained the improved fesc model accounting for soil reflectance (called the fesc_GPR-SR model), which is suitable for near-infrared SIF downscaling. The evaluation results using two simulation datasets from the Soil–Canopy–Observation of Photosynthesis and the Energy Balance (SCOPE) model and the Discrete Anisotropic Radiative Transfer (DART) model showed that the fesc_GPR-SR model outperformed the NIRv/FAPAR model, especially for sparse vegetation, with higher accuracy for estimating fesc (R2 = 0.954 and RMSE = 0.012 for SCOPE simulations; R2 = 0.982 and RMSE = 0.026 for DART simulations) compared with the NIRv/FAPAR model (R2 = 0.866 and RMSE = 0.100 for SCOPE simulations; R2 = 0.984 and RMSE = 0.070 for DART simulations). The evaluation results using in situ observation data from multi-species canopies also suggested that the leaf-level SIF calculated by the fesc_GPR-SR model tracked better with photosynthetic active radiation absorbed by green components (APARgreen) for sparse vegetation (R2 = 0.937, RMSE = 0.656 mW/m2/nm) compared with the NIRv/FAPAR model (R2 = 0.921, RMSE = 0.904 mW/m2/nm). The leaf-level SIF calculated by the fesc_GPR-SR model was less sensitive to observation angles and differences in canopy structure among multiple species. These results emphasize the significance of accounting for soil reflectance in the estimation of fesc and demonstrate that the fesc_GPR-SR model can contribute to further exploring the physiological mechanism between SIF and GPP.

Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data

Solar-induced chlorophyll fluorescence, or SIF, is a part of the natural process of photosynthesis. SIF can be measured from space by instruments such as the Orbiting Carbon Observatory-2 (OCO-2), making it a useful proxy for monitoring gross primary production (GPP), which is a critical component of Earth’s carbon cycle. The complex physical relationship between SIF and GPP is frequently studied using OCO-2 observations of SIF since they offer the finest spatial resolution available. However, measurement error (noise) and large gaps in spatial coverage limit the use of OCO-2 SIF to highly aggregated scales. To study the relationship between SIF and GPP across varying spatial scales, de-noised and gap-filled (i.e., Level 3) SIF data products are needed. Using a geostatistical methodology called cokriging, which includes kriging as a special case, we develop coSIF: a Level 3 SIF data product at a 0.05-degree resolution. As a natural secondary variable for cokriging, OCO-2 observes column-averaged atmospheric carbon dioxide concentrations (XCO2) simultaneously with SIF. There is a suggested lagged spatio-temporal dependence between SIF and XCO2, which we characterize through spatial covariance and cross-covariance functions. Our approach is highly parallelizable and accounts for non-stationary measurement errors in the observations. Importantly, each datum in the resulting coSIF data product is accompanied by a measure of uncertainty. Extant approaches do not provide formal uncertainty quantification, nor do they leverage the cross-dependence with XCO2.

Accounting for Changes in Radiation Improves the Ability of SIF to Track Water Stress‐Induced Losses in Summer GPP in a Temperate Deciduous Forest

AbstractGlobal observations of solar‐induced chlorophyll fluorescence (SIF) are available from multiple satellite platforms, and SIF is increasingly used as a proxy for photosynthetic activity and ecosystem productivity. Because the relationship between SIF and gross primary productivity (GPP) depends on a variety of factors including ecosystem type and environmental conditions, it is necessary to study SIF observations across various spatiotemporal scales and ecosystems. To explore how SIF signals relate to productivity over a temperate deciduous forest, we deployed a PhotoSpec spectrometer system at the University of Michigan Biological Station AmeriFlux site (US‐UMB) in the northern Lower Peninsula of Michigan during the 2018 and 2019 growing seasons. We found that SIF correlated with GPP across diurnal and seasonal cycles (R2 = 0.61 and 0.64 for 90‐min‐ and daily‐averaged data), but that SIF signals were more strongly related to downwelling radiation than GPP (R2 = 0.91 for daily‐averaged data). The dependence of SIF on radiation obscured the impact of intraseasonal drought in the SIF timeseries, but drought stress was apparent as a decrease in relative SIF, which exhibited a stronger correlation with GPP (R2 = 0.56) than other remotely sensed data over the drought period. These results highlight the potential of SIF for detecting stress‐induced losses in forest productivity. Additionally, we found that the red:far‐red SIF ratio did not exhibit a response to water stress‐induced losses in productivity, but was largely driven by seasonal and interannual changes in canopy structure, as well as by synoptic changes in downwelling radiation.

Synchronous Changes of GPP and Solar-Induced Chlorophyll Fluorescence in a Subtropical Evergreen Coniferous Forest.

Using in situ near-surface observations of solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) of a subtropical evergreen coniferous forest in southern China, this study analyzed the dynamics of SIF, GPP and their environmental responses, and explored the potential of SIF in characterizing the variation of GPP. The results showed that SIF and GPP have similar diurnal and seasonal variation and both reach the highest value in summer, indicating that the SIF can be applied to indicate the seasonal variation of GPP for the subtropical evergreen co-niferous. With the increase in temporal scale, the correlation between SIF and GPP becomes more linear. The diurnal variations of both SIF and GPP were characterized by photosynthetically active radiation (PAR), the seasonal variations of SIF and GPP were influenced by air temperature (Ta) and PAR. Probably due to the absent of drought stress during the study period, no significant correlation was detected between soil water content (SWC) and either SIF or GPP. With the in-crease in Ta, PAR or SWC, the linear correlation between the SIF and GPP gradually decreased, and when Ta or PAR was relatively higher, the correlation between SIF and GPP become weakly. Further research is still needed to illustrate the relationship between SIF and GPP under drought condition which occurred frequently in this region based on longer observation.

Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIFU, which were approximately equivalent. 7.8 ± 1.7% of the SIFU was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIFTOC) during the year. Furthermore, our findings suggest that substituting the observed SIFTOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density.

Analysing far-red SIF directional anisotropy of three structurally contrasting forest canopies towards improved GPP estimation

Solar-induced chlorophyll fluorescence (SIF) is used to estimate terrestrial gross primary production (GPP) due to its physiological link to photosynthesis. However, strong angular variations in satellite-observed SIF (SIFobs), especially for forest canopies with a high structural heterogeneity, hampers robust estimation of GPP. Here, we use directional SIFobs datasets from OCO-2 and TROPOMI satellite sensors and the 3D discrete anisotropic radiative transfer model to investigate the directional properties of far-red SIFobs and their relationships with two GPP products obtained from MODIS and FluxCom datasets for three European forest types with contrasting canopy structures, i.e., boreal, temperate and Mediterranean forests. We found bowl-like angular distribution patterns of SIFobs in both observed and simulated boreal and temperate forests, but no generic distribution pattern of SIFobs was found for the Mediterranean forest. In our GPP estimation of boreal forest, oblique SIFobs performed better than the widely used nadir SIFobs. However, we did not find an optimal view angle suitable for all three forest types. Further, the angular dependencies and forest structure impacts present in SIFobs were found to still propagate into the estimation of total emitted SIF (SIFtotal), which generally regarded as free of these confounding effects. Finally, our results demonstrate that two commonly used variables, i.e., SIFobs with a constant viewing angle and the approximated SIFtotal, both fail to produce a robust relationship with GPP for the Mediterranean forest. These findings highlight the importance of forest canopy structural heterogeneity, including forest floor, on interpretation of directional satellite SIF observations, even when corrected and expressed as a total canopy SIF emission.

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m − 2s − 1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

AbstractThe parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP‐SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP‐SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM‐simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML‐based GPP‐SIF relationship. The ELM model when fed with the ML GPP‐SIF models also can well predict the spatial‐temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball‐Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote‐sensing SIF, which can be further improved in the future with more ground‐ and satellite‐based observations.

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought.

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data.

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Dynamic Monitoring of Oxygen Supply Capacity of Urban Green Space Based on Satellite-Based Chlorophyll Fluorescence

Green plants provide food, energy and oxygen sources for human beings and animals on Earth through photosynthesis, which is essential to maintain regional ecological balance. However, few studies have focused on the natural oxygen supply capacity of urban green spaces. As a companion to photosynthesis in leaves, solar-induced chlorophyll fluorescence (SIF) contains abundant photosynthetic information. Currently, satellite-based SIF observations are considered to be a rapid and nondestructive ‘indicator’ of plant photosynthesis, which provides an alternative way to quantitatively assess the spatio-temporal dynamics of oxygen supply capacity in urban green spaces. This study examined the spatial patterns, long-term trends, and environmental control factors of SIF in the nine central cities in China from 2001 to 2020 based on the time-series of the global reconstructed GOSIF-v2 SIF dataset. The results were as follows: (1) There was a contrasting spatial difference between southern and northern cities in China, and multi-year mean SIF values of the southern cities were generally higher than those of the northern cities; (2) The interannual dynamics of SIF in each city generally showed an upward trend, with fluctuations, and the intraannual seasonal differences were more significant in northern cities than those in the southern cities; (3) The spatial trend analysis showed that Beijing, Guangzhou, and Chongqing have had the most significant improvements, followed by Xi’an, Wuhan, Chengdu, and Zhengzhou, while Tianjin and Shanghai have had the least improvements; and (4) The expansion of construction land has exerted significant impacts on the dynamics of the SIF trend in several cities, but it is not the only factor. All analyses indicated that the improvement of vegetation structure and function in the area can offset its negative effect.

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.