###### Abstract  
Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUE\({}_{\text{max}}\) (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover the different plant function types (PFFs). The results revealed better performance (R\({}^{2}\) = 0.86 and RMSE = 1.79 gC m\({}^{-2}\) d\({}^{-1}\) on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models. light use efficiency; gross primary productivity; machine learning 2023152086621 February 202315 April 2  
(CASA) [2], the eddy covariance-light use efficiency (EC-LUE) model [3; 4], the MOD17 algorithm [5], the vegetation photosynthesis model (VPM) [6], and the vegetation photosynthesis and respiration model (VPRM) [7]. In general, LUE models are constructed on the basis of two fundamental assumptions [5; 8]. Firstly, they assume that the ecosystem GPP is linearly correlated with absorbed photosynthetically active radiation (APAR) through LUE, which is defined as the amount of carbon fixed per unit of APAR. Secondly, these models take into account that environmental stresses such as low temperature or water deficit may reduce the maximum light use efficiency (LUE\({}_{\text{max}}\)) below its theoretical maximum value [9], where LUE\({}_{\text{max}}\) means the maximum light use efficiency of vegetation in the absence of any environmental stress. Thus, LUE models estimate vegetation GPP as \(GPP=PAR\times FPAR\times LUE_{max}\times f\), where PAR refers to the incident photosynthetically active radiation per time period and FPAR denotes the fraction of PAR absorbed by the vegetation canopy, the product of which is absorbed photosynthetically active radiation (APAR); LUE\({}_{\text{max}}\) refers to the maximum light use efficiency and \(f\) indicates the effects of environmental stresses on the maximum light use efficiency, the product of which is the actual LUE under real environmental conditions. In LUE models, one of the most important parameters is LUE\({}_{\text{max}}\), which determines the accuracies of quantifying the magnitude of GPP. However, the determination of LUE\({}_{\text{max}}\) values remains largely uncertain. Firstly, LUE\({}_{\text{max}}\) is generally inversed using in situ GPP or net primary productivity (NPP) [10]. Therefore, differences in the used observations and differences in computing environmental stress on photosynthesis can lead to large differences in the LUE\({}_{\text{max}}\) values among the various LUE models [10; 11].