Abstract
Abstract. Over the last 40 years, the light use efficiency (LUE) model has become a popular approach for gross primary productivity (GPP) estimation in the carbon and remote sensing communities. Despite the fact that the LUE model provides a simple but effective way to approximate GPP at ecosystem to global scales from remote sensing data, when implemented in real GPP modelling, however, the practical form of the model can vary. By reviewing different forms of LUE model and their performances at ecosystem to global scales, we conclude that the relationships between LUE and optical vegetation active indicators (OVAIs, including vegetation indices and sun-induced chlorophyll fluorescence-based products) across time and space are key for understanding and applying the LUE model. In this work, the relationships between LUE and OVAIs are investigated at flux-tower scale, using both remotely sensed and simulated datasets. We find that i) LUE-OVAI relationships during the season are highly site-dependent, which is complexed by seasonal changes of leaf pigment concentration, canopy structure, radiation and Vcmax; ii) LUE tends to converge during peak growing season, which enables applying pure OVAI-based LUE models without specifically parameterizing LUE and iii) Chlorophyll-sensitive OVAIs, especially machine-learning-based SIF-like signal, exhibits a potential to represent spatial variability of LUE during the peak growing season.We also show the power of time-series model simulations to improve the understanding of LUE-OVAI relationships at seasonal scale.
Highlights
Photosynthesis plays an essential role in global carbon cycle (Beer et al 2010)
The focus of the current study is to explore the relationship between light use efficiency (LUE) and OVAIs across time and space using both simulated and measured data, in order to better understand the potential links among different production efficiency models (PEMs) and to improve the use of LUE model in gross primary productivity (GPP) estimation at ecosystem to global scales
A bias could be found for NDVI-fPAR linear relationship, i.e fPAR is proportional to NDVI subtracting a bias: fPAR = k × (NDVI − bias) where the bias should be related to canopy background, which could be site-dependent, while k is determined by the Beer’s Law, which should be ideally a constant and independent of biome type
Summary
Photosynthesis plays an essential role in global carbon cycle (Beer et al 2010). Modelling gross primary productivity (GPP) quantifies the amount of total carbon fixation (prior to respiration) by terrestrial plants through photosynthesis (Running et al 2004; Xiao et al 2004). Wu et al (2010) proposed a VI-based LUE model, where VIs (including the normalized difference vegetation index, NDVI and the enhanced vegetation index, EVI) were used as LUE indicators at seasonal scale, and GPP can be approximated by: GPP = APAR × LUE = APAR × f(OVAI). Since these pure OVAI-based PEMs have been shown to provide improved GPP estimations at ecosystem scales (Wagle et al 2016; Wu et al 2010), several recent studies have shown the potential of applying these models across different biomes and even at global scale. The focus of the current study is to explore the relationship between LUE and OVAIs across time and space using both simulated and measured data, in order to better understand the potential links among different PEMs and to improve the use of LUE model in GPP estimation at ecosystem to global scales
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