Toward More Accurate Modeling of Canopy Radiative Transfer and Leaf Electron Transport in Land Surface Modeling

Modeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes.


Introduction
The terrestrial biosphere plays a significant role in mitigating the impact of anthropogenic CO 2 emissions, sequestering approximately 25% of these emissions through photosynthesis (Friedlingstein et al., 2022).Furthermore, it contributes to atmospheric moisture dynamics by returning around 40% of total land precipitation to the atmosphere through transpiration (Schlesinger & Jasechko, 2014).Thus, accurate modeling of leaf gas exchange is imperative for comprehensively predicting global carbon, water, and energy fluxes.This pursuit is essential not only for assessing the land system's capacity to counteract climate change but also for predicting the Earth's future climate trajectory.Achieving this level of accuracy necessitates vegetation models that skillfully represent both plant responses to environmental factors (such as drought stress and CO 2 fertilization) and the environment itself (such as the light environment).
On one hand, there has been a remarkable surge in depicting stomatal responses to the environment over the past few decades.These efforts span from fitting parameter-based empirical models to more intricate process-and trait-based optimality models (Ball et al., 1987;Leuning, 1995;Medlyn et al., 2011;Sperry et al., 2017;Wang, Anderegg, et al., 2021;Wang et al., 2020;Wolf et al., 2016).Concurrently, the realm of plant hydraulics has undergone extensive exploration (Tyree & Zimmermann, 2002;Venturas et al., 2017), empowering vegetation models to more accurately capture plants' reactions to drought stress and, consequently, the tree mortality caused by drought (Anderegg et al., 2016;McDowell & Allen, 2015;McDowell et al., 2008).These improvements are crucial to improve the modeling of canopy water dynamics, which further impact the canopy light environment.On the other hand, canopy radiative transfer (RT; see Table 1 for the full list of symbols) modeling is also transitioning from traditional broadband approaches to the more complex hyperspectral schemes (Campbell & Norman, 1998;Féret et al., 2017;Jacquemoud & Baret, 1990;Norman, 1982;van der Tol et al., 2009;Yang et al., 2017).This advancement not only elevates the accuracy of the modeling process but also facilitates remote monitoring of vegetation, for example, the models that simulate leaf and canopy optical properties can be used for trait estimation using spaceborne remote sensing data (Croft et al., 2020;Simard et al., 2011;Wei et al., 2019).
Despite the notable advancements and improved predictive precision they offer (Anderegg et al., 2016;Féret et al., 2021;Sabot et al., 2022;Sperry et al., 2019;Wang et al., 2020), the integration of these progressions into land surface models (LSMs) and Earth system models (ESMs) has encountered significant obstacles.Among the foremost hurdles lies the challenge of data accessibility (Wang et al., 2023).Evidently, the acquisition of laborintensive plant traits remains an enduring obstacle.Further, extra impediments stem from the intricate nature of the model processes, rendering them computationally intensive for LSMs and ESMs.For example, in contrast to the conventional approach of canopy RT modeling, which utilizes two broadband channels of photosynthetically active radiation (PAR) and near-infrared radiation (NIR) (Campbell & Norman, 1998;Norman, 1982), the adoption of the hyperspectral RT scheme can introduce a slowdown of over 100-fold depending on the number of wavelength bins and canopy layering (Wang & Frankenberg, 2022).This extended computational demand ripples through subsequent photosynthesis and stomatal modeling processes.Notably, these more advanced model schemes allow researchers to more accurately represent the biophysical and physiological vegetation processes, and to learn more from the increasing number of data.The central focus of this study is to investigate the impact of canopy RT modeling on leaf photosynthesis and chlorophyll fluorescence.
In addition to its capacity for modeling canopy optical attributes such as reflectance and fluorescence spectra (van der Tol et al., 2009;Wang, Köhler, et al., 2021;Yang et al., 2017), the hyperspectral canopy RT scheme presents several distinct advantages when compared to the conventional broadband RT scheme.Foremost, hyperspectral RT leverages leaf traits, including chlorophyll content and leaf biomass components, to deduce leaf absorption features (Féret et al., 2017(Féret et al., , 2021;;Jacquemoud & Baret, 1990).This empowers the model with a more accurate and nuanced representation of vegetation characteristics.Second, the partitioning of absorbed PAR within the leaf is no longer constrained by a constant assumption.Instead, hyperspectral RT computes this partitioning based on intrinsic leaf biophysical properties.This departure from the rigid assumption enables a finer-tuned portrayal of electron transport and partitioning between photosystems (Hogewoning et al., 2012;Laisk et al., 2014).Furthermore, hyperspectral RT is equipped to address the variations in the light source spectrum across the canopy (van der Tol et al., 2009).As an illustration, green light is disproportionately abundant in the lower canopy, yet leaves exhibit a diminished capacity to absorb green light compared to red and blue wavelengths.Unlike the conventional broadband RT scheme, which treats green light equivalently to red and blue light, hyperspectral RT is primed to account for this differential absorption.This critical adjustment holds significant implications for the accurate modeling of canopy energy balance, photosynthesis, and transpiration processes.The leaf's net photosynthetic rate (A net ) is typically represented as the minimum value between the rates limited by carboxylation regeneration (A c ) and electron transport (A j ) in photosynthesis models (Collatz et al., 1992;Farquhar et al., 1980;Johnson & Berry, 2021).In the extensively adopted C3 photosynthesis model by Farquhar et al. (1980), the computation of A j is expressed as: where J is the potential electron transport rate, C i is the internal CO 2 partial pressure, Γ* is the CO 2 compensation point with the absence of dark respiration (R d ), J max is the maximum electron transport rate at leaf temperature, J PSII is the potential electron transport in photosystem II (PSII) from total absorbed radiation, and θ (0 < θ ≤ 1) is a curvature parameter to smooth J max and J PSII .
The magnitude of J PSII is contingent upon not only the incident PAR but also on key leaf biophysical parameters including leaf pigments, carbon concentration, and water content (Féret et al., 2017;Jacquemoud & Baret, 1990).Mathematically, the expression for J PSII is given by: where f APAR is the ratio between total absorbed PAR (APAR) and PAR, f PPAR is the ratio between photosystem antenna absorbed PAR (PPAR) and APAR, f PSII is the fraction of PPAR that goes to PSII (typically assumed to be 0.5), and Φ PSII,max is the maximum PSII yield (often assumed to be constant, e.g., 0.85 as in Quebbeman and Ramirez (2016)).Although both f APAR and f PPAR are influenced by radiation and trait-related factors (Féret et al., 2021;Grassi et al., 2002), they are seldom directly quantified in leaf gas exchange measurements.In practice, they are often assumed to remain constant, primarily due to the absence of leaf hyperspectral absorption feature data within conventional photosynthesis measurements.Typically in vegetation models, f APAR is equivalent to leaf absorptance in the PAR range whereas f PPAR is assumed to be 1 (namely all APAR goes to the photosystems).For example, in studies such as Medlyn et al. (2002) and Sperry et al. (2017), a value of  The inability to precisely incorporate the varying factors of f APAR , f PPAR , or Φ PSII,max has the potential to introduce biases into the modeled A net , particularly evident for shaded leaves within the lower canopy that are inherently light-limited.Amongst various contributors, chlorophyll, carotenoid, and leaf biomass prominently contribute to light absorption within the 400-700 nm range (refer to Figure 1a).For instance, as chlorophyll content increases, the product f APAR • f PPAR should also rise and eventually reach a saturation point (depicted in Figure 1b).Notably, different leaf optical models might hold distinct assumptions regarding photosynthetic photon absorption (PPAR), as illustrated by CliMA Land (Wang et al., 2023;Wang, Köhler, et al., 2021).In this case, both chlorophyll and carotenoid absorption are considered a PPAR, recognizing that carotenoid also plays a role within the antenna system.Neglecting the carotenoid's PPAR contribution could lead to an underestimation of f APAR • f PPAR .Consequently, being aware of the underlying model assumptions is imperative when utilizing models to compute PPAR.
Due to the non-uniform spectral absorption of leaves in the PAR range, it is crucial to make adjustments to f APAR and f PPAR based on the spectrum of the light source to achieve accurate photosynthesis modeling.Specifically, the highest f APAR • f PPAR values are observed for pure red light (centered around 655-665 nm, as depicted in Figure 1b), whereas pure green light (ranging from 530 to 540 nm, as shown in Figure 1b) leads to the lowest values.Consequently, a cautious approach is necessary when employing empirical correlations between chlorophyll content and f APAR as well as f PPAR (Evans, 1996;Quebbeman & Ramirez, 2016).For instance, the Licor portable photosynthesis system predominantly emits red light peaking at 660 nm (comprising over 90% of the spectrum).In contrast, natural PAR encompasses a more substantial contribution from green light in both direct and diffuse forms.A study by Evans (1996) observed f APAR • f PPAR exceeding 0.9 for leaves possessing high chlorophyll content when subjected to artificial red light (with less than 5% blue light, as indicated by the red curve in Figure 1b).Applying this correlation to leaves exposed to natural light conditions introduces a discrepancy of more than 0.2 in f APAR • f PPAR .The magnitude of this difference will increase notably for lower canopy leaves where green light surpasses red and blue light in abundance (as depicted in Figure 1b).
In addition to potential errors introduced by constant assumptions of f APAR and f PPAR , it's important to recognize that the assumption of constant f PSII and Φ PSII,max can also lead to inaccuracies in computed J PSII .While f PSII is seldom directly measured, it is often approximated as 0.5, reflecting the presumption that plants evenly distribute energy between photosystem I and II for optimal photon absorption under non-stressed conditions.However, it's crucial to note that the partitioning of photons between these photosystems varies with the wavelength of light.Specifically, shorter wavelength photons (below 680 nm) tend to predominantly excite photosystem II, whereas longer wavelength photons tend to overstimulate photosystem I (Hogewoning (coefficient unit: cm 2 μg 1 ), car (coefficient unit: cm 2 μg 1 ), water (coefficient unit: cm 1 ), and LMA (coefficient unit: cm 2 mg 1 ).(b) The f APAR • f PPAR at different chlorophyll contents and light sources.See Table 1 for the list of symbols.The simulations are done using the PROSPECT leaf radiative transfer model (Féret et al., 2017;Jacquemoud & Baret, 1990) embedded in CliMA Land (Wang et al., 2023;Wang, Köhler, et al., 2021).The f APAR • f PPAR is computed for a leaf with a structure equivalent to 1.4 mesophyll layers (Jacquemoud & Baret, 1990), leaf mass per area of 0.012 g cm 2 , and leaf carotenoid content of 1/7 the chlorophyll content (Croft et al., 2020).The natural radiation spectra shapes are from the American Society for Testing and Materials (ASTM) G-173 look-up table, blue light with flat flux density from 448 to 458 nm, green light with flat flux density from 530 to 540 nm, and red light with flat flux density from 655 to 665 nm.et al., 2012;Laisk et al., 2014).Moreover, the light harvest complex on photosystem II can detach and reattach to photosystem I to prevent excessive light from entering photosystem II (Allen et al., 1981).This intricate dynamic justifies the assumption of f APAR = 0.5 when shorter wavelength light (below 680 nm) predominates, as it aligns with the avoidance of over-excitation of photosystem II.Nevertheless, when incoming radiation predominantly consists of longer wavelength light (above 680 nm), the f APAR = 0.5 assumption and photosynthesis models that disregard photosystem I electron transport could yield problematic outcomes (Porcar-Castell et al., 2021).
When not held as a constant, the computation of Φ PSII,max typically involves deriving it from the rate coefficients associated with photochemical yield (K P ), fluorescence (K F ), and heat dissipation (K D ).Moreover, recent advancements have incorporated the rate coefficient of sustained non-photochemical quenching (K S ) at lower temperatures into the calculation (Magney et al., 2019;Porcar-Castell, 2011;Raczka et al., 2019): Nonetheless, it's important to recognize that these rate coefficients may exhibit a temperature dependence.For example, van der Tol et al. ( 2014) advocated the inclusion of temperature dependency within K D , a step taken to explain the temperature-induced variations in minimum fluorescence after dark adaptation and maximum fluorescence after dark adaptation.Furthermore, Raczka et al. (2019) demonstrated the effectiveness of incorporating a dynamic K S term at lower temperatures, leading to an enhanced representation of solar-induced chlorophyll fluorescence (SIF) in a sub-alpine forest during winter.Consequently, a more rational approach involves treating Φ PSII,max as a variable, achieved through the incorporation of temperature-dependent terms within K D and K S : where T leaf is leaf temperature in °C, [K S,max , b, T s ] are fitting parameters, and S is a dynamic acclimation state based on temperature (see Raczka et al. (2019) for more details).
However, traditional LSMs face challenges in accurately incorporating the interplay of leaf traits, light sources, and temperature-dependent electron transport within their radiation and photosynthesis models, as they cannot perform canopy RT at hyperspectral resolution.For instance, the state-of-the-art Community Land Model (CLM; version 5) employs a constant coefficient to convert photosynthetically active radiation (PAR) from energy to photons.Moreover, it adopts a f APAR • f PPAR value ranging from 0.84 to 0.88 based on plant functional types (PFTs; f PPAR = 1), rather than leveraging leaf biophysical traits (D.M. Lawrence et al., 2019).The implications of such photosynthesis modeling extend to other vital vegetation processes, including stomatal opening and fluorescence emission.Progress in hyperspectral leaf and canopy RT modeling (Féret et al., 2021;Jacquemoud & Baret, 1990;van der Tol et al., 2009;Yang et al., 2017), coupled with advancements in global plant trait estimation and canopy structure characterization (Croft et al., 2020;Wei et al., 2019), has ushered in the ability to assess the significance of addressing these complexities within LSMs.
In the present study, we ask (a) how f APAR • f PPAR varies vertically in a canopy and spatially across the globe, and how much it may be biased by traditional LSMs, and (b) how global scale photosynthesis modeling is biased by imperfect RT and electron transport modeling.To answer the questions, we conduct the research in the following steps.(a) We compute leaf-level f APAR • f PPAR in a vertically resolved canopy at both site level and global scale utilizing a next-generation LSM-CliMA Land.(b) We conduct global-scale simulations with CliMA Land under four scenarios, incorporating varying settings of f APAR • f PPAR and Φ PSII,max .Subsequently, we compare the modeled gross primary productivity (GPP) and SIF among these scenarios.(c) We explore potential strategies aimed at refining the representation of electron transport within traditional LSMs, thereby seeking to enhance their accuracy in RT and thus photosynthesis modeling.

CliMA Land
CliMA Land, available at https://github.com/CliMA/Land,stands as a next-generation, highly modular LSM coded in Julia.Its primary objective is to leverage the growing wealth of remote sensing data beyond carbon and water fluxes (Wang, Köhler, et al., 2021).The remarkable modularity of CliMA Land empowers its deployment in diverse research contexts spanning multiple scales, from leaf to organ, plant, and ecosystem levels.Central to its design philosophy is the separation of the core model from configuration schemes (Wang et al., 2023), thereby affording the flexibility to execute the model under various setups, including emulating other LSMs.CliMA Land operates by encapsulating vegetation processes within fundamental units known as soil-plant-air continua (SPACs) within each grid.Notably, the customization of SPACs allows accommodation of a spectrum of complexities (Braghiere et al., 2023;Wang & Frankenberg, 2022).One of the hallmark features of CliMA Land is its departure from the conventional reliance on PFT-based lookup tables.Instead, each SPAC element incorporates site-specific plant traits, thus better accounting for the spatial and temporal variations of these traits (Wang et al., 2023).
We used CliMA Land v0.1 to perform global simulations in the present study, and the exact version is tagged as "a11," which differs from "a6" used in Wang et al. (2023) in that we (a) used a smoothing factor θ of 0.7 when computing the potential leaf electron transport rate from the maximum electron transport and radiation, (b) fixed a bug in the sunlit leaf APAR calculation, (c) added the option to change SIF excitation wavelength, (d) added an on/off switch to allow Φ PSII,max to vary with leaf temperature (default is on), (e) extended shortwave radiation wavelength coverage to 300 nm to account for ultraviolet radiation, (f) fixed a bug in updating wind speed's impact leaf boundary layer conductance, (g) added a new feature to prescribe broadband leaf reflectance and transmittance and soil albedo, and (h) added a flexible way to include or exclude carotenoid contribution to PPAR (default is to exclude).Among the various supported model schemes within CliMA Land, we used the following configurations for our global simulations: • multiple layered canopy with hyperspectral RT (Wang & Frankenberg, 2022;Yang et al., 2017); • clumping index (CI) implementation (Braghiere et al., 2021); • hyperspectral soil albedo (Braghiere et al., 2023;Jiang & Fang, 2019); • leaf level PROSPECT-PRO model that partitioned leaf biomass to carbon-based constituents and proteins (Féret et al., 2021); • carotenoid absorption defined as PPAR (Wang, Köhler, et al., 2021); • absorbed far-red light with 700-750 nm wavelength used as APAR (Zhen & Bugbee, 2020); • empirical stomatal model (Medlyn et al., 2011) along with a tuning factor based on soil moisture (Wang et al., 2023); • weather drivers prescribed from ERA5 single levels reanalysis data (Hersbach et al., 2020); • viewing zenith angle at 0°.We configured CliMA Land using the globally gridded data sets from GriddingMachine, which shares data sets via FTP/HTTP, Julia, Matlab, Octave, Python, and R (Wang et al., 2022).The data sets we used in the present study (labeled as "gm3") included soil color (P.J. Lawrence & Chase, 2007), soil hydraulic parameters (Dai et al., 2019), canopy height (Simard et al., 2011), chlorophyll content (Croft et al., 2020), CI (Wei et al., 2019), leaf area index (LAI) (Yuan et al., 2011), specific leaf area (Butler et al., 2017), photosynthetic capacity (Luo et al., 2021), elevation (Yamazaki et al., 2017), land mask (Hersbach et al., 2020), and PFT map used to derive stomatal parameters (P.J. Lawrence & Chase, 2007).The "gm3" data sets we used in the present study differed from the "gm1" used in Wang et al. (2023) only in the CI map as Wei et al. (2019) data set is more updated and has a better temporal resolution (Li et al., 2023).We refer the readers to https://silicormosia.github.io/blogs/emerald/emerald.htmlfor the benchmarks of CliMA Land global simulations of each version from "a6" to "a11."

Vertical f APAR and f PPAR Profiles
As red and blue light fractions decrease in the lower canopy (because of upper canopy absorption), it is expected to see the spectrally integrated f APAR and f PPAR varying in a vertically resolved canopy and the profiles should vary with canopy structure and leaf traits.To understand how f APAR and f PPAR vary with canopy structure and leaf traits, we ran CliMA Land at the plant level and performed a sensitivity analysis to three leaf traits and two canopy Journal of Advances in Modeling Earth Systems 10.1029/2023MS003992 structural parameters: chlorophyll content (Chl), carotenoid content (Car), and leaf mass per area (LMA), leaf area index (LAI), and CI (a measure of the canopy's horizontal heterogeneity, lower value means more heterogeneous canopy) (Braghiere et al., 2021).
We first ran the model at our default setting: Chl = 40 μg cm 2 , Car = 5.7 μg cm 2 , LMA = 0.012 g cm 2 , LAI = 3, and CI = 1.Note that because of the limited knowledge of how leaf traits vary vertically in the canopy, we used constant leaf trait profiles in our model.We ran the canopy RT at hyperspectral mode and obtained the incident light spectrum per canopy layer (sum of downward direct light, downward diffuse light, and upward diffuse light).We then computed f APAR and f PPAR per layer based on the leaf traits.For each of the sensitivity tests of Chl, Car, LMA, and LAI, we kept other parameters at their default values and doubled the tested parameter (namely Chl to 80 μg cm 2 , Car to 11.4 μg cm 2 , LMA = 0.024 g cm 2 , and LAI to 6 respectively).For the sensitivity test of CI, we reduced CI to 0.5 while keeping other parameters at their default values.

Potential Biases in f APAR • f PPAR
To further illustrate how leaf absorption features may be biased, we evaluated the impacts from two aspects at the global scale: (a) chlorophyll content and (b) light source.First, we computed annual mean Chl for each 1°× 1°g rid from the weekly mean derived based on Medium Resolution Imaging Spectrometer (MERIS) data (Croft et al., 2020).Second, we computed the maximum LAI per grid using the monthly LAI data for the year 2019 (Dai et al., 2019;Wang et al., 2022).Third, we ran the CliMA Land RT module at each grid using the average Chl and maximum LAI, and obtained the incident spectra at the top of the canopy (TOC) and bottom of the canopy (BOC).Last, we used the TOC and BOC radiation spectra to compute the spectrally integrated f APAR • f PPAR .The comparison between TOC and BOC per grid would help explain how leaf absorption features differ for leaves at different canopy depths.
Note here that we used average Chl and maximum LAI for illustration purposes.In the global simulations, we used weekly Chl time series per 1°× 1°grid, and computed light spectra of each canopy layer based on the time series of LAI, CI, solar zenith angle, and diffuse light fraction.

Global Simulations
We performed global simulations using CliMA Land at four contrasting configurations regarding f APAR • f PPAR and K D .The first scenario was our default where we ran the hyperspectral canopy RT model with spatial + temporal Chl variations and accounted for K D temperature dependency (we labeled it as HS_KDTD).The second scenario is that we ran the hyperspectral canopy RT model with spatial + temporal Chl variations but used a constant K D = 0.85 (we labeled it as HS_KDST).The third scenario is that we ran the RT model in broadband mode using the CLM configuration while accounting for K D temperature dependency (we labeled it as BB_KDTD).When running the model at broadband mode, we still use the same amount of wavelength bins (default is 125 bins from 300 to 2,400 nm), but the spectral shapes per PAR and NIR regions were flat.To run the model at broadband mode, we first computed the leaf level broadband reflectance and transmittance using the PFT fractions per grid.We then used these PFT-based values to replace the computed leaf hyperspectral reflectance and transmittance, for example, all the PAR bins (≤700 nm) have the same PFT-based reflectance and transmittance, and all the NIR bins (>700 nm) have the same PFT-based reflectance and transmittance.We also force f PPAR to be 1 so that computed PPAR equals APAR in the canopy RT module.Mathematically, this is equivalent to using only the PAR and NIR bands.However, to model SIF, we still used leaf pigment contents to derive the SIF matrices (leaf-level fluorescence quantum yield was computed using the broadband model).The last scenario is that we ran the RT model in broadband mode using the CLM configuration and used a constant K D = 0.85 (we labeled it as BB_KDST).
We ran CliMA Land at 1°× 1°resolution at hourly time step for the year 2019 using the protocol described in Wang et al. (2023).We then re-sampled the simulated results and obtained daily, 8-daily, monthly, and annual means for GPP and SIF at 740 nm.We compared the results among scenarios to determine how the biases in electron transport modeling may impact global photosynthesis modeling.We compared the results using annual and monthly means.
To explore the major drives for the model differences, we used the general linear model to examine how well the combinations of Chl, LMA, LAI, and CI explain the spatial distribution of ΔGPP and ΔSIF (there is no global scale data set for carotenoid; in our simulations, we assumed Car = Chl/7).For each of the Chl (Croft et al., 2020), LMA (Butler et al., 2017), LAI (Yuan et al., 2011), and CI (Wei et al., 2019) data sets, we resampled the original data set to 1°resolution and took the annual mean.Further, to estimate how the difference in GPP and SIF among scenarios were related to PFT, we used the dominant PFT that has the highest fraction to categorize each grid using the PFT map from P. J. Lawrence and Chase (2007).We selected the following 11 PFTs following the CLM setup (D.M. Lawrence et al., 2019): needle leaf evergreen temperate (NET-P), needle leaf evergreen boreal (NET-B), needle leaf deciduous boreal (NDT-B), broad leaf evergreen tropical (BET-T), broad leaf evergreen temperate (BET-P), broad leaf deciduous tropical (BDT-T), broad leaf deciduous temperate (BDT-P), broad leaf deciduous boreal (BDT-B), evergreen shrub temperate (BES-P), deciduous shrub temperate (BDS-P), and deciduous shrub boreal (BDS-B).We then compared how the GPP and SIF differences between BB_KDST and HS_KDTD scenarios vary with the dominating PFT per grid.

Vertical f APAR and f PPAR Profiles
Because of the changes in the light spectrum in the lower canopy (i.e., lower red and blue light fractions), both spectrally integrated f APAR and f PPAR and thus f APAR • f PPAR decrease in the lower canopy layers (Figure 2).When leaf chlorophyll content increases, the leaf can absorb more photons with the wavelength from 400 to 700 nm (Figure 1a), and a higher fraction of photons goes to the photosystems.Thus, both f APAR and f PPAR increase (Figure 2).When leaf carotenoid content increases, the leaf can only absorb more photons from 400 to 550 nm (Figure 1a) but this extra absorption does not necessarily count into PPAR.As a result, f PPAR and thus f APAR • f PPAR decrease in the lower canopy (Figure 2).Increasing LMA results in higher total light absorption (higher f APAR ) but lower photon partitioning to the photosystems (lower f PPAR ), and overall f APAR • f PPAR decreases (Figure 2).When LAI increases, blue and red light fractions would further decrease in the lower canopy.Even though leaf-level hyperspectral absorption features are not changing, the spectrally integrated f APAR and f PPAR both decrease (Figure 2).When CI decreases, more light can penetrate through the canopy to the lower layers, and as a result, spectrally weighted f APAR and f PPAR both increase (Figure 2).

Potential Bias in f APAR • f PPAR
The leaf PPAR absorption ratio, denoted as f APAR • f PPAR , exhibits significant geographical variation attributed to both spatial and temporal fluctuations in leaf pigments, particularly chlorophyll (refer to Figure 3a for the impact of spatial chlorophyll content variations; LAI has minimum contribution to the TOC values).Beyond chlorophyll content, vertical radiation distribution in the canopy further influences the effective absorption properties of leaves due to chlorophyll's preferential absorption of blue and red light.Consequently, in the lower canopy with higher green light fractions, the effective f APAR • f PPAR ratio is notably lower in contrast to the leaves at the top of the canopy (as illustrated in Figures 3b and 3d).As most traditional LSMs model broadband leaf reflectance and transmittance regardless of leaf biophysical parameters and radiation spectrum, these models could have a biased f APAR • f PPAR (up to >60% relative bias in the boreal regions).The magnitude of this bias is higher for shaded leaves in the lower canopy (as showcased in Figures 3c and 3d for the potential biases of CLM).

Global GPP and SIF
Compared to the HS_KDTD scenario when both chlorophyll variations and K D temperature dependency were accounted for, HS_KDST scenario that removed K d temperature dependency had higher GPP with the highest ΔGPP ≈ 0.10 gC m 2 day 1 in the tropics (Figure 4a).Similarly, HS_KDST SIF overestimated SIF by up to 0.04 mW m 2 nm 1 sr 1 (Figure 4b).Compared to the HS_KDTD scenario, BB_KDTD scenario at broadband mode had higher GPP across the globe with the highest annual mean ΔGPP >1.2 gC m 2 day 1 in the tropical regions (Figure 4c).Annually mean SIF, however, showed different spatial patterns from GPP and most regions had lower SIF (Figure 4d).Combining the impacts from BB_KDTD and HS_KDST, scenario BB_KDST also had higher GPP across the globe (Figure 4e), and the overestimation may be >20% in the boreal regions.In comparison, SIF was also lower in most regions except for the arid regions (Figure 4f).When integrated globally, BB_KDST GPP was higher by 8.1% than HS_KDTD, and SIF at 740 nm was lower by 8.8%.
Because of the seasonality of Chl, LAI, and solar radiation, the seasonality of ΔGPP and ΔSIF changed accordingly (see Figure S1 for the comparison between scenarios BB_KDST and HS_KDTD).In general, ΔGPP and ΔSIF showed relatively minor seasonal variations in the tropics, but strong seasonality in the north hemisphere temperate and boreal forests (highest difference in June, July, and August) and in the south hemisphere temperate forests (highest difference in December, January, and February).
The variation of ΔGPP concurred with the LAI spatial pattern (R 2 = 0.87 for general linear regression; Table 2).Further adding other parameters into the general linear regression did not improve the R 2 (Table 2).In comparison, LAI explained only 30% of the variation in ΔSIF; however, LAI and Chl together explained 61% of the ΔSIF variation (Table 2).PFT-wise, BB_KDST GPP was always higher than HS_KDTD GPP for all PFTs, and the difference is highest for BET-T, which has the highest LAI (Figure 5a).In comparison, BB_KDST SIF was, in general, higher than HS_KDTD SIF for most of the tested PFTs, except NET-B and NDT-B (Figure 5b).ΔSIF was highest in BET-T and BDT-T, which have the highest annual mean radiation.
The dotted curves plot the profiles at the default setting, and the solid curves plot those with higher chlorophyll content (Chl), higher carotenoid content (Car), higher leaf mass per area (LMA), higher leaf area index (LAI) and lower clumping index (CI).

Discussion
Electron transport within photosynthesis models is often estimated through the application of a constant conversion factor from PAR. Regrettably, this convenient approach tends to overlook the critical influence of leaf biophysical traits and radiation spectra.However, recent strides in modeling hyperspectral RT within canopies and the remote sensing of plant traits have unlocked the potential for a more precise depiction of this process within large-scale LSMs, even though this advancement has incurred substantial computational costs.In this study, we evaluated the potential biases of the traditional LSMs due to their imperfect modeling of canopy RT and electron transport.Leveraging a comprehensive global chlorophyll content map and harnessing the capabilities of a next-generation land model, we conducted a comprehensive assessment.Our findings revealed that the constant conversion factor used in traditional LSMs could yield >60% biases, with the most pronounced discrepancies observed within boreal forests.Additionally, the bias increased within the lower canopy, where the availability of red and blue light is relatively constrained.Building upon these insights, we extended our investigation to assess the impacts of such biased electron transport on global-scale photosynthesis and fluorescence modeling.Our modeled outcomes showed that ignoring variations in leaf biophysical traits and/or the temperature-dependent partitioning of PPAR could lead to biases surpassing 20% in both GPP and SIF.Our results underscore the necessity of incorporating more intricate and realistic biophysical processes within vegetation and land surface models.
We note that the substantial overestimation of GPP in scenarios BB_KDTD and BB_KDST are due to the lightlimited leaves, that is, leaves in the lower canopy when radiation is abundant and leaves when radiation is limited.Therefore, the parameters that impact electron transport rate computation would result in a biased lightlimited photosynthetic rate.However, most existing LSMs are not capable of accurately representing these trait-, temperature-, and environment-dependent parameters, as these LSMs use broadband canopy RT schemes.Thus, actions need to be taken to address this problem if these LSMs do not shift to a hyperspectral canopy RT scheme.
First, broadband reflectance and transmittance maps based on leaf traits such as chlorophyll content and leaf mass per area can be derived offline using models like PROSPECT and CliMA Land (Féret et al., 2021;Wang et al., 2023).These maps with spatial and temporal variations can supersede the look-up tables based on plant functional types used in most LSMs.This step addresses the issue in f APAR , and will help not only photosynthesis but also the leaf energy balance modeling.Second, an empirical parameterization of f PPAR based on leaf traits and leaf position in a canopy (z from 0 for top to 1 for bottom) could help reduce the bias in modeled electron transport rate, for example, f PPAR = f(Chl, z).For example, we randomly combine LAI from 0.5 to 6, Chl from 10 to 80, and compute f PPAR at different depths of a canopy using CliMA Land.We find that the computed f PPAR for sunlit and shaded leaves at different canopy depths can be fitted using an empirical function (Figure 6): The fitting parameters are k z = 0.0199, k LAI = 0.00124, k Chl = 0.210, and l = 0.511 for the sunlit leaves; and k z = 0.0550, k LAI = 0.00310, k Chl = 0.176, and l = 0.549 for the shaded leaves.The fitting parameters are k z = 0.0375, k LAI = 0.000930, k Chl = 0.193, and l = 0.530 for the sunlit and shaded leaves combined.
Third, the temperature-dependent K D can be readily modeled in existing LSMs using leaf temperature, and Φ PSII,max needs to be changed accordingly.While these improvements would require the traditional LSMs to rewire the hard-coded canopy RT and photosynthesis modules, they could substantially reduce the model biases in simulated energy balance, electron transport, and canopy assimilation.
We examined whether the above suggestions help reduce the inconsistency between hyperspectral and broadband RT models and minimize the difference in modeled GPP.We ran CliMA Land to simulate the GPP and SIF in a diurnal cycle using three RT models: (a) hyperspectral RT model, (b) PAR + NIR broadband RT model using leaf reflectance and transmittance from CLM look-up table (with K D temperature dependence), and (c) PAR + NIR broadband model with corrections made.The corrections were that leaf broadband reflectance and transmittance for PAR and NIR were computed from leaf traits used in the hyperspectral RT model and that f PPAR was computed using Equation 7 with the parameter sets fitted for sunlit and shaded leaves combined.We ran the model for two example sites, and our simulations showed that the GPP of the correction-made broadband RT model better agreed with the hyperspectral RT model values (Figure 7).However, the modeled SIF using the correction-made broadband RT model did not always show improvements (Figure 7).Thus, if accurate SIF modeling is desired, we recommend using hyperspectral RT models rather than a fusion of hyperspectral and broadband RT models.Notably, the suggested corrections hold great potential in improving the broadband RT model that is used in most LSMs.
We note here that the results in Figure 6 are based on the Reference Air Mass 1.5 Spectra from the American Society for Testing and Materials (ASTM) G-173.Thus, using the coefficients along with any light source different from the ATM G0173 Reference Air Mass 1.5 Spectra would result in different results (e.g., when the diffuse light faction is different).Consequently, the ultimate solution for this light-, plant trait-, and temperaturedependent molding of canopy RT, leaf electron transport, and photosynthesis would require the implementation  of a hyperspectral atmosphere RT module to account for the spatial and temporal variations of the direct and diffuse light spectra (Jeyaram et al., 2022;Sanghavi et al., 2014).

Conclusion
Electron transport in photosynthesis machinery is impacted by plant traits (such as chlorophyll content and leaf mass per area) and the environment (such as light source).However, this level of complexity has been overlooked by the traditional LSMs that model broadband canopy RT.Using a next-generation LSM that models hyperspectral canopy RT-CliMA Land, we explored how the implementation of plant trait-, temperature-, and environment-dependent electron transport may impact the global modeling of canopy RT and electron transport, and thus photosynthesis and fluorescence.We found that the electron transport parameterization used in the traditional LSMs would overestimate photosynthesis globally but underestimate fluorescence in most areas.We discussed the potential correction approaches that can be undertaken by the traditional LSMs, including (a) superseding the look-up tables of leaf reflectance and transmittance with spatially and temporally variant maps of leaf-level reflectance and transmittance, and (b) employing an empirical function to compute f PPAR based on LAI, Chl, and leaf location in a canopy.We believe that the use of a more realistic representation of canopy RT and leaf electron transport would help improve the model predictive skills of the LSMs.

Figure 1 .
Figure1.Leaf hyperspectral absorption features and light absorption coefficients.(a) Absorption coefficients of Chl (coefficient unit: cm 2 μg 1 ), car (coefficient unit: cm 2 μg 1 ), water (coefficient unit: cm 1 ), and LMA (coefficient unit: cm 2 mg 1 ).(b) The f APAR • f PPAR at different chlorophyll contents and light sources.See Table1for the list of symbols.The simulations are done using the PROSPECT leaf radiative transfer model(Féret et al., 2017;Jacquemoud & Baret, 1990) embedded in CliMA Land(Wang et al., 2023;Wang, Köhler, et al., 2021).The f APAR • f PPAR is computed for a leaf with a structure equivalent to 1.4 mesophyll layers(Jacquemoud & Baret, 1990), leaf mass per area of 0.012 g cm 2 , and leaf carotenoid content of 1/7 the chlorophyll content(Croft et al., 2020).The natural radiation spectra shapes are from the American Society for Testing and Materials (ASTM) G-173 look-up table, blue light with flat flux density from 448 to 458 nm, green light with flat flux density from 530 to 540 nm, and red light with flat flux density from 655 to 665 nm.

Figure 2 .
Figure 2. Vertical profiles of f APAR and f PPAR for plants with different leaf traits and canopy structures.Top row: f APAR , middle row: f PPAR , and bottom row: f APAR • f PPAR .The dotted curves plot the profiles at the default setting, and the solid curves plot those with higher chlorophyll content (Chl), higher carotenoid content (Car), higher leaf mass per area (LMA), higher leaf area index (LAI) and lower clumping index (CI).

Figure 3 .
Figure 3. Leaf PPAR absorption ratio and its biases.(a) Top of the canopy (TOC) f APAR • f PPAR .(b) Bottom of the canopy (BOC) f APAR • f PPAR .(c) Potential bias of TOC f APAR • f PPAR in community land model (CLM) which uses a f APAR • f PPAR around 0.86.(d) Difference between TOC and BOC f APAR • f PPAR .Leaf absorption features were simulated using CliMA Land based on the annual mean chlorophyll content from Croft et al. (2020) and maximum LAI of the year 2019 and were meant to illustrate how variations in leaf traits and the light source may impact leaf light absorption features at the global scale.The simulations ignored spatial variations in the other leaf biophysical parameters such as water content and leaf biomass.

Figure 6 .
Figure 6.Empirical fitting of CliMA Land modeled f PPAR .The f PPAR is computed for leaves at different depths of a canopy with random LAI and Chl (LAI from 0.5 to 6; Chl from 5 to 80 μg cm 2 ).(a) Comparison of the sunlit leaves.The black dots plot the point comparisons, and the red line plots the 1:1 line.(b) Comparison of the shaded leaves.

Figure 7 .
Figure 7.Comparison between modeled GPP and SIF in a diurnal cycle.Solid curves plot the GPP and SIF modeled using the hyperspectral RT module, dashed curves plot those using the broadband RT model, and dotted curves plot those using the broadband model with corrections.(a) Plant with LAI = 4 and Chl = 60 μg cm 2 at the equator, the reflectance for PAR and NIR are 0.10 and 0.45, and the transmittance for PAR and NIR are 0.05 and 0.25, respectively.(b) Plant with LAI = 4 and Chl = 60 μg cm 2 at 45°N, the reflectance for PAR and NIR are 0.07 and 0.35, and the transmittance for PAR and NIR are 0.05 and 0.10, respectively.

Table 1
List of Symbols

Table 2
R 2 of the General Linear Models of ΔGPP and ΔSIF