Considering time-lag effects can improve the accuracy of NPP simulation using a light use efficiency model

Most terrestrial models synchronously calculate net primary productivity (NPP) using the input climate variable, without the consideration of time-lag effects, which may increase the uncertainty of NPP simulation. Based on Normalized Difference Vegetation Index (NDVI) and climate data, we used the time lag cross-correlation method to investigate the time-lag effects of temperature, precipitation, and solar radiation in different seasons on NDVI values. Then, we selected the Carnegie–Ames–Stanford approach (CASA) model to estimate the NPP of China from 2002 to 2017. The results showed that the response of vegetation growth to climate factors had an obvious lag effect, with the longest time lag in solar radiation and the shortest time lag in temperature. The time lag of vegetation to the climate variable showed great tempo-spatial heterogeneities among vegetation types, climate types, and vegetation growth periods. Based on the validation using eddy covariance data, the results showed that the simulation accuracy of the CASA model considering the time-lag effects was effectively improved. By considering the time-lag effects, the average total amount of NPP modeled by CASA during 2001–2017 in China was 3.977 PgC a −1 , which is 11.37% higher than that of the original model. This study highlights the importance of considering the time lag for the simulation of vegetation growth, and provides a useful tool for the improvement of the vegetation productivity model.