中文摘要：

遥感技术兴起于20世纪60年代初,随后被广泛应用于草地遥感估产研究,通过不同尺度数据之间建立植被指数—生物量函数关系来完成由点及面的转换。本研究选用了14种常用于草地估产的植被指数,对内蒙古锡林浩特市白音锡勒典型草原分别建立植被指数—干重、植被指数—鲜重的回归模型并对14种植被指数进行DCA分析发现,1）鲜重及干重应用在草地生物量遥感估测中均是可行的,但干重效果要优于鲜重,考虑到实验条件限制,鲜重具有更广泛的应用;2）DCA排序的第一、二轴分别代表土壤、大气的影响,且土壤是影响植被指数最主要的因子;DCA排序将14个植被指数分为4类,综合排除了土壤及大气影响的一类植被指数,也就是归一化差异植被指数及由其衍生的土壤调整植被指数、修改型土壤调整植被指数效果最好;3）经验数据显示：当生物量低于370g/m2时,建立的估产模型都是一元线性的;当生物量在370～720g/m2时,一元线性模型和指数模型的模拟效果都很好;当生物量高于720g/m2时,估产模型都是指数的,因此,随着生物量范围的增大,模型逐渐由一元线性趋近于指数模型。

英文摘要：

Remote sensing technology emerged in the early 1960s and was widely used in grassland yield estimation.Estimation of the relationship between vegetation index and biomass on different scales was always used to complete the conversion from points to surface.Fourteen vegetation indices which were commonly used in grassland yield estimation were selected to establish regression models of vegetation index-dry weight and vegetation index-fresh weight in typical grassland in Baiyinxil,Xilinhaote,Inner Mongolia.The DCA analysis of 14 vegetation indices showed that： 1） It was feasible to use fresh weight and dry weight in remote sensing estimation of grass biomass,and that although dry weight was better than fresh weight,for reasons of experimental conditions,fresh weight had a wider range of applications.2） The first and second axis of DCA analysis represent the effects of soil and atmosphere with soil the most important factor that affected vegetation index;DCA divided 14 vegetation indices into 4 categories,and the category which generally excluded the influences of soil and atmosphere（e.g.NDVI,SAVI and MSAVI） were the best.3） Empirical dates showed that while the biomass was lower than 370 g/m2,the yield estimation model was a simple unitary linear model,but when it was between 370 and 720 g/m2,the simulation results of the linear and the exponential model were both very good.When the biomass was greater than 720 g/m2,the exponential model was the best yield estimation model.Therefore,as the range of biomass increases,the yield estimation model should be gradually changed from a simple linear model to an exponential model.