Comparative evaluation of simulation methods for the maximum light-use efficiency of vegetation
The 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 a fixed vaLUE for all vegetation types in the early stage.However,it became a parameter that varied with vegetation type since the MODIS-LUE model,and recently,it was considered to require adjusting 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 remain clear.In this paper,we presented a comparative analysis of three typical dynamic LUEmax parameter simulation methods(which are based on the 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).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 was better than when the original static parameter was used,but it relied on the specific LUEmax parameter simulation method.The Markov chain Monte Carlo method had a good simulation effect on the LUEmax parameter,and its GPP estimation accuracy improved in all vegetation types at all time periods(compared to the original MODIS-LUE static LUEmax,ΔRMSE=10.9 g/(m2· month),calculated in units of carbon),especially in closed shrub,deciduous needle-leaf forest,and evergreen broadleaf forest.These findings can provide a basis for the uncertainty analysis of light-use-efficiency-based vegetation productivity estimation and the development of new models.
remote sensingvegetation productivitygross primary productivitylight use efficiency modelmaximum light use efficiencyparameter evaluation
NETL
NSTL
万方数据
