基于植被指数季节变化曲线的年总初级生产力估算

针对年总初级生产力估算的研究,提出了一种参数简单、误差较小的估算方法。以"三北"防护林工程区域各类型植被为研究对象,获取2010年研究区全年时序的MODIS植被指数并构建植被指数季节变化曲线,建立该曲线积分ΣVIs与MODIS GPP产品的拟合关系,并研究各植被类型GPP估算适用的植被指数时间序列曲线积分ΣVIs。结果表明:①ΣVIs适用于估算研究区年总GPP并与MODIS GPP在p<0.01置信水平下,显著相关;②ΣNDVI估算郁闭灌丛、稀疏灌丛、草地、耕地以及荒地或稀疏植被GPP的效果要优于ΣEVI和ΣEVI2,但在森林及其他植被类型方面要比ΣEVI或ΣEVI2的精度低;③由于NDVI在高LAI地区趋于饱和,使ΣNDVI估算高LAI植被类型GPP的误差较大,而利用ΣEVI和ΣEVI2估算高LAI植被类型的GPP具有较好的精度,并且EVI2相对于EVI减少了来自于蓝光波段的限制,能够更好地应用于长时间序列GPP研究。

Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP)as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs)and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicableΣVIs for estimating the GPP of all vegetation types.The results show that:(1)ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2)ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect ofΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior toΣEVI andΣEVI2;(3)Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type byΣNDVI is larger,while usingΣEVI andΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.