Improving the accuracy of estimates of forest carbon exchange is a central priority for understanding ecosystem response to increased atmospheric CO2 levels and improving carbon cycle modelling. ...However, the spatially continuous parameterization of photosynthetic capacity (Vcmax) at global scales and appropriate temporal intervals within terrestrial biosphere models (TBMs) remains unresolved. This research investigates the use of biochemical parameters for modelling leaf photosynthetic capacity within a deciduous forest. Particular attention is given to the impacts of seasonality on both leaf biophysical variables and physiological processes, and their interdependent relationships. Four deciduous tree species were sampled across three growing seasons (2013–2015), approximately every 10 days for leaf chlorophyll content (ChlLeaf) and canopy structure. Leaf nitrogen (NArea) was also measured during 2014. Leaf photosynthesis was measured during 2014–2015 using a Li‐6400 gas‐exchange system, with A‐Ci curves to model Vcmax. Results showed that seasonality and variations between species resulted in weak relationships between Vcmax normalized to 25°C (Vcmax25) and NArea (R2 = 0.62, P < 0.001), whereas ChlLeaf demonstrated a much stronger correlation with Vcmax25 (R2 = 0.78, P < 0.001). The relationship between ChlLeaf and NArea was also weak (R2 = 0.47, P < 0.001), possibly due to the dynamic partitioning of nitrogen, between and within photosynthetic and nonphotosynthetic fractions. The spatial and temporal variability of Vcmax25 was mapped using Landsat TM/ETM satellite data across the forest site, using physical models to derive ChlLeaf. TBMs largely treat photosynthetic parameters as either fixed constants or varying according to leaf nitrogen content. This research challenges assumptions that simple NArea–Vcmax25 relationships can reliably be used to constrain photosynthetic capacity in TBMs, even within the same plant functional type. It is suggested that ChlLeaf provides a more accurate, direct proxy for Vcmax25 and is also more easily retrievable from satellite data. These results have important implications for carbon modelling within deciduous ecosystems.
Improving modelled estimates of forest carbon exchange requires accurate parameterization of photosynthetic capacity (Vcmax) within terrestrial biosphere models (TBMs). This research investigates leaf biochemical variables for modelling photosynthetic parameters within a deciduous forest. Seasonality and interspecies variability resulted in weak correlations between Vcmax and leaf nitrogen; challenging assumptions that simple nitrogen–Vcmax25 relationships can reliably constrain photosynthetic capacity in TBMs. Conversely, leaf chlorophyll (ChlLeaf) accounted for 76% of Vcmax25 variability, suggesting that ChlLeaf provides a more accurate and direct proxy for Vcmax.
Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic ...capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential.
•ChlaLeaf*CarLeaf is found to be highly correlated with Vcmax25.•PRILeaf_noon is also found to be highly correlated with Vcmax25.•PRILeaf_noon*(ChlLeaf + CarLeaf) is found to be highly correlated ...with Vcmax25.
Leaf chlorophyll content has recently been found to be a better proxy than leaf nitrogen content for leaf photosynthetic capacity in a mixed deciduous broadleaf forest. A key concept underlying the relationship between leaf photosynthetic capacity and leaf chlorophyll content was the coordinate regulation of photosynthetic components (i.e. light harvesting, photochemical, and biochemical components). In order to test this hypothesis, we measured seasonal variations in leaf nitrogen content (Nleaf), leaf photosynthetic pigments (i.e. chlorophyll (ChlLeaf), carotenoids (CarLeaf) and xanthophyll (XanLeaf)) and leaf photosynthetic capacity (i.e. the maximum rate at which ribulose bisphosphate (RuBP) is carboxylated (Vcmax25) and regenerated (Jmax25) at 25 °C) at a paddy rice site during the growing season in 2016. We investigated the effectiveness of (Nleaf), leaf photosynthetic pigments, leaf-level photochemical reflectance index at sunny noon (PRILeaf_noon) and their possible combinations for estimating leaf photosynthetic capacities (i.e. Vcmax25 and Jmax25) at a paddy rice site. ChlLeaf was highly correlated to Vcmax25 and Jmax25 (R2 = 0.89 and 0.87, respectively), which were better than Nleaf (R2 = 0.80 and 0.85, respectively). The products of PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) were also found to be highly correlated with Vcmax25 (R2 = 0.95–0.96). Also, the product of leaf chlorophyll a and CarLeaf was a good proxy for Vcmax25 (R2 = 0.93). In sum, this study supported the previously findings that leaf chlorophyll content was better correlated with Vcmax25 than leaf nitrogen content. Also, combining PRILeaf_noon with leaf pigments (i.e. ChlLeaf, CarLeaf and XanLeaf) offered an additional way to estimate leaf photosynthetic capacity (i.e. Vcmax25). These findings supported the hypothesis of coordinate regulation of photosynthetic components and they would be helpful to estimation of leaf photosynthetic capacity using remote sensing data.
As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that ...are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant-environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r
= 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal-based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing-based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields.
Abstract
The maximum rate of carboxylation (Vcmax), a key parameter indicating photosynthetic capacity, is commonly fixed as a constant by vegetation types and/or varies according to empirical ...scaling functions in Earth system models (ESMs). As such, the setting of Vcmax results in uncertainties of estimated carbon assimilation. It is known that the coupling between leaf chlorophyll and Rubisco (ribulose-1,5-biphosphate carboxylase-oxygenase) contents can be applied to estimate Vcmax. However, how this coupling is affected by environmental changes and varies among plant functional types (PFTs) has not been well investigated yet. The effect of varying coupling between chlorophyll and Rubisco contents on the estimation of Vcmax is still not clear. In this study, we compiled data from 76 previous studies to investigate the coupling between Chlorophyll (Chl) and Rubisco (Rub), in different PFTs and under different environmental conditions. We also assessed the ability of a Rub-based semi-mechanistic model to estimate Vcmax normalized to 25 °C (Vcmax
25
) based on the Rub–Chl relationship. Our results revealed strong, linear Rub-Chl relationships for different PFTs (
R
2
= 0.73, 0.67, 0.54 and 0.72 for forest, crop, grass and shrub, and C4 plants, respectively). The Rub–Chl slope of natural C3 PFTs was consistent and significantly different from those of crops and C4 plants. A meta-analysis indicated that reduced light intensity, elevated CO
2
, and nitrogen addition strongly altered Rub/Chl. A semi-mechanistic model based on PFT-specific Rub–Chl relationships was able to estimate Vcmax
25
with high confidence. Our findings have important implications for improving global carbon cycle modeling by ESMs through the improved parameterization of Vcmax
25
using remotely sensed Chl content.
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•Annual NEP over mixed mature forest increased over 17 year period.•Interannual variations and trends in NEP influenced by PAR and soil temperature.•Seasonal CO2 budget influenced by ...PAR, air temperature, and soil temperature.•NEP increased with length of carbon uptake period.
The exchanges of carbon dioxide (CO2), water vapor, and energy were measured nearly continuously since 1996 over a mixed mature transition forest at the Borden Forest Research Station, in southern Ontario, Canada. Borden Forest, one of the longest running flux towers in North America, is located in the temperate–boreal ecotone. This transitional region, which includes species close to the limits of their environmental range, may be particularly susceptible to changes in forest composition as a result of climate change. Here we analyze net CO2 exchange, measured using the eddy covariance method, and concurrent meteorological variables. The forest was found to be a low-to-moderate CO2 sink, with uptake of 177±28gCm−2yr−1 (mean±standard error). In two of the years, however, the forest was a weak CO2 source (i.e., 1996: −36gCm−2yr−1 and 2001: −35gCm−2yr−1), demonstrating that the forest can switch between source and sink. Over the 17 years of measurement, annual net ecosystem productivity (NEP) increased by 15.7gCm−2yr−1yr−1, due to a decline in ecosystem respiration of 4.2gCm−2yr−1yr−1 and an increase in gross ecosystem productivity of 11.6gCm−2yr−1yr−1. There were notable long-term indications of climatic warming: annual air temperature rose by 0.09°Cyr−1, while soil temperature increased by 0.08°Cyr−1. Photosynthetically active radiation and soil temperature were found to be the dominant environmental drivers of interannual variations and long-term trends in NEP; on seasonal or monthly time-scales, air temperature and precipitation also influenced CO2 uptake. NEP is positively correlated with the length of the net carbon uptake period, which varied from 111 to 164 days. The large interannual variations in CO2 flux in this dataset demonstrate the need for long time series of CO2, water vapor, and energy fluxes, together with meteorological measurements; such measurements show long-term trends, which can be used to understand and predict future changes in forest-atmosphere exchanges in response to anticipated changes in climate.
•Effective LAI used and clumping ignored, ET is underestimated by 11.5%.•ET estimation has a larger bias in a more clumped canopy if clumping is ignored.•Estimated biases are robust to errors in key ...model parameters.
In forested ecosystems, the aggregation of leaves into different spatial structures at the canopy, branch and shoot scales leads to a non-random spatial distribution of foliar elements. However, effect of foliar aggregation or clumping on the estimation of terrestrial evapotranspiration (ET) is not yet well understood. To evaluate the effect of foliar clumping, the process-based Boreal Ecosystem Productivity Simulator (BEPS) is used to simulate ET at eight flux tower sites in North American forests. BEPS separates a canopy into sunlit and shaded leaf groups in the calculation of canopy-level ET and clumping affects this separation. Three cases are simulated with BEPS, and the modeled ET values are compared with flux tower measurements: Case I serves as a baseline, in which LAI and clumping index at the sites are considered. In this case, BEPS can explain 43–75% the variance of measured ET at these sites. For Case II, the LAI is considered but clumping is ignored; resulting in an overestimation of annual ET at all sites by ∼5% at the most clumped sites. In Case III, when effective LAI is used (i.e. clumping is not considered), the site-averaged mean annual ET is underestimated by 11.5%, with the largest underestimation found at the site with most clumping (CA-DF49; 19.1%). In both Case II and Case III, the more clumped a canopy is, the larger bias is found in the ET estimation (p<0.001). The estimated biases are robust to the errors in key driving variables and model parameters. When LAI is derived from optical measurements on the ground and from satellite platforms without considering the effect of clumping, it can cause substantial underestimation of ET.
These results demonstrate the need for considering foliage clumping in process-based ET modeling, with potential biases having large implications for carbon cycle modeling, water budget calculations and understanding and predicting ecosystem responses to future climatic scenarios.
Evapotranspiration (ET) is commonly estimated using the Penman‐Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy ...stomatal conductance (Gs). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon‐water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two‐big‐leaf scheme (TBL) and a two‐leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r2 = 0.71, root‐mean‐square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light‐saturated and light‐unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gs and intercellular CO2 concentration simulated in TBL compared to their counterparts in TL. We suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf‐level processes to the canopy scale in BL and TBL.
Key Points
Big‐leaf scheme underestimates GPP and ET due to its incorrect simulation of leaf light environment
Two‐big‐leaf and two‐leaf scheme implement sunlit‐shaded radiation regime and show advantages for ET modeling
Two‐leaf scheme is recommended as it applies tight water‐carbon coupling at the leaf level
Photosynthetic rate is a key source of uncertainty in the modeling of the terrestrial carbon cycle. Recent studies have utilized leaf chlorophyll content (Chl) as a proxy for leaf photosynthetic ...capacity in croplands and deciduous forests, with little investigation into this relationship for other plant function types and for different leaf ages. In this study, we evaluated the relationship between the maximum rate of carboxylation (Vcmax25) and the maximum electron transport capacity (Jcmax25) at 25 °C with both leaf nitrogen and Chl from different leaf ages (current and previous year) in Masson's pine (Pinus massoniana Lamb.) and slash pine (Pinus elliottii Engelm.) species in a subtropical evergreen coniferous forest. Our results showed small changes in leaf nitrogen over the growing season. In contrast, Vcmax25, Jmax25, and Chl displayed larger seasonal variations. Vcmax25 was more related to leaf Chl than leaf nitrogen in both previous year's and current year's leaves, likely due to the variable partitioning of leaf nitrogen between and within photosynthetic and nonphotosynthetic fractions. Leaf Chl and month after budding (MAB) were the main predictors for Vcmax25 based on the random forest regression analysis. These findings highlighted the problem in using leaf nitrogen as a proxy for Vcmax25 where there is a dynamic nitrogen investment (i.e., with leaf ontogenesis, or between different species) and illustrated the value of using leaf Chl (as retrievable from remotely sensing) and MAB to constrain Vcmax25 in process‐based models to improve the simulation of photosynthetic rates in evergreen coniferous forests.
Plain Language Summary
The parameter (Vcmax25) representing leaf photosynthetic capacity within models is usually assumed to be constant, which leads to a key source of uncertainty in the simulation of the terrestrial carbon cycle. To consider the time‐varying photosynthetic capacity parameter, recent studies have utilized leaf chlorophyll content (Chl), instead of leaf nitrogen, as a proxy for the parameter in croplands and deciduous forests to reduce this uncertainty. However, there is little investigation into this relationship in evergreen coniferous forests. In this study, we evaluated the relationship between the photosynthetic capacity parameter and both leaf nitrogen and chlorophyll of different leaf ages (current and previous year) in a subtropical evergreen coniferous forest. Vcmax25 was more related to leaf Chl than leaf nitrogen, likely due to the dynamic partitioning of leaf nitrogen between and within photosynthetic and nonphotosynthetic fractions. Leaf Chl and month after budding (MAB) were the main predictors for Vcmax25. These findings highlighted the problem in using leaf nitrogen as a proxy for Vcmax25 where there is a dynamic nitrogen investment among photosynthetic components and illustrated the value of using leaf chlorophyll and MAB to constrain Vcmax25 in models to improve the simulation of photosynthetic rates in evergreen coniferous forests.
Key Points
There were weak correlations between leaf nitrogen and the maximum carboxylation rate of two leaf ages
The fractions of leaf nitrogen partitioning among photosynthetic components were dynamic
The combination of leaf chlorophyll and month after budding was a better proxy for the maximum carboxylation rate
Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant ...species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.
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