光能利用率模型是一种基于遥感数据估算植被生产力的参数模型，其核心参数最大光能利用率（LUEmax）在早期模型中被认为是一个适用于所有植被类型的固定值，而从MODIS-LUE模型开始则成为一个依植被类型而变化的参数，直至近年来被认为需要进一步根据植被的物候及生理状态而进行实时调整。相较于基于静态LUEmax参数估算的植被生产力，尽管目前基于季节性动态LUEmax参数估算的植被生产力都显示出更高的精度，但这些研究结果大多局限于特定的植被类型或空间范围，而在更广泛的植被类型和区域的适用性以及不同的动态LUEmax参数在地域间的适用性差异等问题尚不明确。因此，本文针对目前已有的3种典型动态LUEmax参数模拟方法（基于叶绿素遥感指数、基于LAI季节调节因子、马尔科夫链蒙特卡洛模拟），通过采用相同的生产力估算与评估数据集（FLUXNET 2015数据集）和模型结构（MODIS-LUE模型结构）对它们进行了对比分析。结果显示，不同的动态LUEmax参数在各植被类型上的季节性变化特征有明显差异，总体上呈现出单峰、“U”型和水平波动三种特征。相比于原静态LUEmax参数，基于各动态LUEmax参数估算的总初级生产力（GPP）精度均有一定的提升，但是依赖于特定的动态LUEmax参数模拟方法，其中的马尔科夫链蒙特卡洛方法对LUEmax参数有着较好的模拟效果，并且其GPP的估算精度在全植被类型的所有时段都不低于基于原静态LUEmax参数的结果（相比静态参数的GPP结果，RMSE总体降低了13.3 g·C·m-2·month-1），尤其在郁闭灌丛、落叶针叶林以及常绿阔叶林上的提升效果十分明显。本文研究结果可为植被生产力光能利用率模型的不确定性分析以及发展新的模型提供依据。

Light use efficiency model is a parametric model for estimating vegetation productivity based on remote sensing data. Its core parameter, maximum light use efficiency (LUEmax), was considered as a fixed value for all vegetation types in the early stage. However, it had become a parameter varied with vegetation types since the MODIS-LUE model, and recently it was considered that this parameter need to be adjusted in time according to the phenological and physiological status of vegetation. Although the current vegetation productivity estimation model based on seasonal dynamic LUEmax parameters showed a relatively higher accuracy, these studies were mostly limited to specific vegetation types or spatial scales. Thus the applicability of different dynamic LUEmax parameters in a wider range of vegetation types or regions and the differences between geographical areas are still not clear. Therefore, we presented a comparative analysis of three typical dynamic LUEmax parameter simulation methods (which based on Chlorophyll Index, Leaf Area Index and Markov Chain Monte Carlo) by using the same dataset (FLUXNET 2015 dataset) and model structure (MODIS-LUE model structure). The results showed that the seasonal variation characteristics of three different dynamic LUEmax parameters differed significantly, generally showing three characteristics of single-peaked, U-shaped and horizontal fluctuations in different vegetation types. The accuracy of the estimated gross primary productivity (GPP) based on dynamic LUEmax parameters were better than using the original static parameter, but relied on the specific LUEmax parameter simulation method. The Markov chain Monte Carlo method has a good simulation effect on the LUEmax parameter, and its GPP estimation accuracy is improved in all vegetation types at all time periods (compared to the original MODIS-LUE static LUEmax, △RMSE = 13.3 g·C·m-2·month-1), especially in closed shrub, deciduous needle-leaf forest and evergreen broadleaf forest. These fingdings can provide a basis for uncertainty analysis of light-use-efficiency-based vegetation productivity estimation and the development of new models.