Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content

Abstract

The terrestrial biosphere plays a critical role in mitigating climate change by absorbing anthropogenic CO2 emissions through photosynthesis. The rate of photosynthesis is determined jointly by environmental variables and the intrinsic photosynthetic capacity of plants (i.e. maximum carboxylation rate; Vnormalcmax25). A lack of an effective means to derive spatially and temporally explicit Vnormalcmax25 has long hampered efforts towards estimating global photosynthesis accurately. Recent work suggests that leaf chlorophyll content (Chlleaf) is strongly related to Vnormalcmax25, since Chlleaf and Vnormalcmax25 are both correlated with photosynthetic nitrogen content. We used medium resolution satellite images to derive spatially and temporally explicit Chlleaf, which we then used to parameterize Vnormalcmax25 within a terrestrial biosphere model. Modelled photosynthesis estimates were evaluated against measured photosynthesis at 124 eddy covariance sites. The inclusion of Chlleaf in a terrestrial biosphere model improved the spatial and temporal variability of photosynthesis estimates, reducing biases at eddy covariance sites by 8% on average, with the largest improvements occurring for croplands (21% bias reduction) and deciduous forests (15% bias reduction). At the global scale, the inclusion of Chlleaf reduced terrestrial photosynthesis estimates by 9 PgC/year and improved the correlations with a reconstructed solar‐induced fluorescence product and a gridded photosynthesis product upscaled from tower measurements. We found positive impacts of Chlleaf on modelled photosynthesis for deciduous forests, croplands, grasslands, savannas and wetlands, but mixed impacts for shrublands and evergreen broadleaf forests and negative impacts for evergreen needleleaf forests and mixed forests. Our results highlight the potential of Chlleaf to reduce the uncertainty of global photosynthesis but identify challenges for incorporating Chlleaf in future terrestrial biosphere models.

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

“…While f esc is a pure canopy structure property for far-red SIF, APAR is, in principle, not only affected by canopy structure but also by leaf pigments. dfs However, the impact of leaf pigments on APAR was found to be marginal and strongly dominated by canopy structure characteristics such as leaf angle distribution, clumping and LAI (Luo et al, 2019). Only when distinguishing total vs. green APAR would we expect to see an impact of leaf pigments on certain parts of our analysis, which is discussed in detail in section 4.4.…”

Large and projected strengthening moisture limitation on end-of-season photosynthesis

“…Compared with plant phenology derived from remotely sensed greenness indices, plant physiological phenology (i.e., phenology based on photosynthesis) is more sensitive to the environment due to the additional physiological regulation (16), as well as changes in leaf pigment concentration and canopy structure that are not effectively captured by the conventional normalized difference vegetation index (NDVI) (17,18). Factors causing the variations of the late growing-season photosynthesis or ending date of photosynthesis (EOP) can therefore also be regarded as limiting factors to ecosystems productivity.…”

Combining hyperspectral remote sensing and eddy covariance data streams for estimation of vegetation functional traits

“…), our work calls for a combination of hyperspectral 440 and EC data together with models that accurately represent radiative transfer, energy balance and photosynthesis (RTM-SVAT). The characterization of functional (and biophysical) traits in these networks could be later used to inform, constrain and evaluate TBM, to inform TMB input uncertainties (Prichard et al, 2019), or to benchmark estimates provided by different models and methods (e.g., Luo et al, (2019), Croft et al, (2017), Zhou et al, (2014) or Xie et al, (2018)). Also, when comprehensive enough, they could be predicted at global scale using data-driven approaches or RS 445 (Serbin et al, 2015).…”