A Radiation-Regulated Dynamic Maximum Light Use Efficiency for Improving Gross Primary Productivity Estimation

The light use efficiency (LUE) model has been widely used in regional and global terrestrial gross primary productivity (GPP) estimation due to its simple structure, few input parameters, and particular theoretical basis. As a key input parameter of the LUE model, the maximum LUE (sigma(max)) is crucial for the accurate estimation of GPP and to the interpretability of the LUE model. Currently, most studies have assumed sigma(max) as a universal constant or constants depending on vegetation type, which means that the spatiotemporal dynamics of sigma(max) were ignored, leading to obvious uncertainties in LUE-based GPP estimation. Using quality-screened daily data from the FLUXNET 2015 dataset, this paper proposed a photosynthetically active radiation (PAR)-regulated dynamic sigma(max) (PAR-sigma(max), corresponding model named PAR-LUE) by considering the nonlinear response of vegetation photosynthesis to solar radiation. The PAR-LUE was compared with static sigma(max)-based (MODIS and EC-LUE) and spatial dynamics sigma(max)-based (D-VPM) models at 171 flux sites. Validation results showed that (1) R-2 and RMSE between PAR-LUE GPP and observed GPP were 0.65 (0.44) and 2.55 (1.82) g C m(-2) MJ(-1) d(-1) at the 8-day (annual) scale, respectively; (2) GPP estimation accuracy of PAR-LUE was higher than that of other LUE-based models (MODIS, EC-LUE, and D-VPM), specifically, R-2 increased by 29.41%, 2.33%, and 12.82%, and RMSE decreased by 0.36, 0.14, and 0.34 g C m(-2) MJ(-1) d(-1) at the annual scale; and (3) specifically, compared to the static sigma(max)-based model (MODIS and EC-LUE), PAR-LUE effectively relieved the underestimation of high GPP. Overall, the newly developed PAR-sigma(max) provided an estimation method utilizing a spatiotemporal dynamic sigma(max), which effectively reduced the uncertainty of GPP estimation and provided a new option for the optimization of sigma(max) in the LUE model.

Automated summary

This study proposed a photosynthetically active radiation (PAR)-regulated dynamic sigma(max) (PAR-LUE) model for estimating gross primary productivity (GPP). Compared to existing LUE models, the PAR-LUE model can accurately estimate GPP and reduce estimation errors. The validation results showed that the PAR-LUE model had higher accuracy in GPP estimation compared to other LUE models, and effectively alleviated the underestimation of high GPP. In summary, the PAR-sigma(max) model provides a method for estimating GPP using spatiotemporal dynamic sigma(max), which reduces the uncertainty in GPP estimation and offers a new option for optimizing sigma(max) in the LUE model.