植被净初级生产力模型估算及其对气候变化的响应研究进展

PROGRESS IN ESTIMATION OF NET PRIMARY PRODUCTIVITY AND ITS RESPONSES TO CLIMATE CHANGE

近年来随着遥感和地理信息系统技术的广泛应用，植被净初级生产力研究经历了从小范围的传统测量阶段到大范围的模型估算阶段的重要转变，并参与全球变化研究。其研究手段和研究内容大大拓宽，在植被净初级生产力模型估算以及对气候变化（如温度、降水、ＣＯ２浓度等）的响应等方面的研究取得了可喜的进展。

Recently, with the development of remote sensing and geographical information system (GIS), the traditional small scale measurement of net primary productivity (NPP) of vegetation has been substituted gradually by the large scale model based estimation. By using both environmental values and satellite data, the models based on remote sensing and GIS have become one of the key approaches to the issues about global change. In this paper, progresses in approaches to estimate NPP were reviewed firstly: traditionally, NPP is measured by experiment; but with the application of remote sensing and GIS, NPP is usually estimated by models. Here statistical, parametric, and mechanic models had been compared and assessed comprehensively. Then the impacts of climate changes (temperature, water use efficiency, CO 2) on NPP were discussed comprehensively. The responses of NPP to climate changes are very complex, which depend on the interactions between climate, vegetation, and soil both spatially and temporally. Generally, solar radiation, temperature, precipitation, air humidity, and atmospheric CO 2 concentration are some of the most important external forces that drive ecosystem processes, and they affect NPP directly or indirectly through changeable soil conditions. As for forests, there is a positive relationship between NPP and temperature or actual transpiration, but the effects of CO 2 individually on NPP are still confused. For grasslands, precipitation and its seasonal distribution impact NPP mostly. And for some crops, precipitation persistence during their developing period is an important factor which has an effect on NPP or yield. Therefore, we conclude that further research should focus on as follows: ①Try to improve the precision of NPP, especially from remote sensing data; ②Emphasize the ecosystem process, including different distribution of NPP or Biomass during different period; ③Strengthen the feedback of vegetation to climate change, taking climate-vegetation-soil as a whole.