中国陆地植被净初级生产力遥感估算

ESTIMATION OF NET PRIMARY PRODUCTIVITY OF CHINESE TERRESTRIAL VEGETATION BASED ON REMOTE SENSING

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989～1993年中国陆地植被NPP平均值为3.12PgC(1Pg=1015g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。

Aims Net primary productivity （NPP） is a key component of the terrestrial carbon cycle. Model simulation is commonly used to estimate regional and global NPP given difficulties to directly measure NPP at such spatial scales. A number of NPP models have been developed in recent years as research issues related to food security and biotic response to climatic wanning have become more compelling. However, large uncertainties still exist because of the complexity of ecosystems and difficulties in determining some key model parameters. Methods We developed an estimation model of NPP based on geographic information system （GIS） and remote sensing （RS） technology. The vegetation types and their classification accuracy are simultaneously introduced to the computation of some key vegetation parameters, such as the maximum value of normalized difference vegetation index （NDVI） for different vegetation types. This can remove some noise from the remote sensing data and the statistical errors of vegetation classification. It also provides a basis for the sensitivity analysis of NPP on the classification accuracy. The maximum light use efficiency （LUE） for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The simulated values of LUE are greater than the value used in the CASA model and less than the values simulated with the BIOME-BGC model. The computation of the water restriction factor is driven with ground meteorological data and remote sensing data, and complex soil parameters are avoided. Results are compared with other studies and models. Important findings The simulated mean NPP in Chinese terrestrial vegetation from 1989 - 1993 is 3.12 Pg C （1 Pg = 10^15 g）. The simulated NPP is close to the observed NPP, and the total mean relative error is 4.5 % for 690 NPP observation stations distributed in the whole country. This illustrates the utility of the model for the estimation of terrestrial primary production over regional scales.