An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors

Although the light use efficiency (LUE) models are widely employed to estimate ecosystem gross primary production (GPP), the majority of these models inadequately consider the effects of environmental and biological factors on GPP, resulting in considerable uncertainty. In addition, most developed LUE models have assumed that the maximum LUE (εmax) is a fixed value for different vegetation types, while εmax should be dynamic under environmental changes. The canopy nitrogen (N) concentrations were considered to have a significant linear relationship with εmax and could be estimated using various vegetation indices. In this study, we selected a vegetation index to characterize the canopy N concentrations and further simulate the dynamic εmax. We then developed an improved LUE model that simultaneously integrated the effects of canopy N concentrations, temperature, water, atmospheric carbon dioxide (CO2) and radiation components on the GPP estimates. Different forms of LUE models that partially integrate the above factors were also constructed for comparison. Our results showed that (1) the green chlorophyll index (CIgreen) correlated well with measured canopy N concentrations (R2 = 0.68), and the model using the CIgreen to characterize canopy N concentrations performed the best; (2) the GPP estimated using the improved model gave the best accuracy (R2 = 0.69, RMSE = 2.13 gC/m2/d, MAE=1.36 gC/m2/d, IOA = 0.915) and performed well for different vegetation types when validated against the FLUXNET GPP; and (3) the estimated GPP had the best accuracy compared with MOD17 GPP and the revised EC-LUE GPP on a both daily and yearly scale. Overall, this study was an attempt to integrate N into the LUE model to obtain the spatiotemporally dynamic εmax while simultaneously taking into account the impacts of multiple environmental variables on the GPP estimates. The proposed model has the potential for satisfactory GPP simulations on a global or regional scale.