对现有叶绿素遥感估测研究方法进行比较, 确定植被指数法是其中最实用、 普适性最强的研究方法。 近年来, 草地退化问题日益严峻, 需要进一步从光谱分析、 植物生化参数估测的角度加以研究, 因而亟需建立一种用于反演草地植被叶绿素含量的植被指数。 首先对四川省松潘草原和内蒙古自治区贡格尔草原的草地实测反射率光谱曲线及其一阶微分曲线进行分析, 通过这两种光谱与叶绿素含量的相关性分析, 找到红边区域(red-edge position, REP)与草地叶绿素含量之间的规律, 即叶绿素含量越高, 反射率一阶微分曲线的红边拐点(red-edge inflection point, REIP)取值越高, 由此构建草地叶绿素含量估测植被指数(grassland chlorophyll index, GCI), 选取最适宜反演的波段, 最后采用卫星高光谱影像计算GCI, 将计算结果与野外试验观测的叶绿素含量数据进行精度分析验证。 结果证明, 对于草地叶绿素含量来说, GCI比其他叶绿素指数的敏感性更强, 具有较高的草地叶绿素含量估测精度。 GCI是第一个针对草地叶绿素含量估测而被提出的植被指数, 其对遥感反演草地叶绿素含量具有广泛应用潜力。 同时这种基于光谱分析的草原植被叶绿素含量估测方法为其他的草原植被生化参数估测、 草原植被生长状况评价以及草地生态环境变化大面积监测提供了新的研究思路。

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters’ estimation, vegetation growth status’ evaluation and grassland ecological environment change’s monitoring.