利用植被指数与植被吸收的光合有效辐射比例之间的线性关系,由1992 年4 月～1993年3 月12 个月的1 km AVHRR NDVI资料及同期地面气象资料确定地表植被吸收的光合有效辐射,然后由光能利用率得到植被净第一性生产力（NPP）.为了更准确计算NPP, 本文还考虑了温度及土壤水分条件对光能利用率的影响，最后得到我国陆地植被年NPP分布图，并对中国陆地植被净第一性生产力分布情况的季节变化及不同植被类型的NPP季节变化进行了初步研究。结果表明,我国NPP的分布主要受水分条件的影响,呈从东南到西北递减的趋势,全国年总净第一性生产力约为2.645×109tC.

It is significant to estimate terrestrial net primary productivity (NPP) accurately not only for global change research, but also for natural resources management to achieve sustainable development. The estimation of NPP by climate data is only a potential NPP rather than true NPP. But remote sensing data can describe large scale distribution of plant resources better. So, 1 km AVHRR NDVI data was used adopted here to estimate the distribution of NPP in China. First, the fraction of absorbed photosynthetically active radiation (FPAR) by vegetation is derived from NDVI data using the linear relationship between FPAR and vegetation index. The incident PAR was estimated by climate data. Then NPP was calculated with absorbed PAR and energy efficiency ε * g . In order to estimate NPP more accurate, the effects of temperaturely, soil water content and plant respiration were also considered in the model. The model can be described as below:NPP=ε g×f 1(T)×f 2(β)×FPAR×PAR-Rin which f 1(T) and f 2(β) mean the temperature and soil water content effects on photosynthesis, R means plant respiration including maintenance respiration and growth respiration. Monthly and annual net primary productivity in China was computed by monthly 1km AVHRR NDVI data, climate data between April, 1992 and March, 1993, vegetation type map and soil texture map. The results gained were compared with ground-observation and Miami model results. It shows that the results using remote sensing data are closer to truth. Total annual NPP in China is 2 645×10 9tC. The distribution of NPP in China is mainly effected by precipitation and has the trend of decreasing from south east to north west. Finally, the seasonal change of NPP was investigated on the basis of monthly NPP.