中文摘要：

定量的估算草原光合植被覆盖度（fPV）和非光合植被覆盖度（fNPV）对草原畜牧业和土地荒漠化具有重要的意义。以锡林郭勒盟西乌珠穆沁旗为研究区，以MODIS 500 m分辨率地表反射率产品MOD09GHK为数据源，采用干枯燃料指数（DFI）指数构建NDVI-DFI像元三分模型估算了锡林郭勒草原的fPV和fNPV，并分析了锡林郭勒草原fPV和fNPV的动态变化。研究结果表明：锡林郭勒草原NDVI-DFI特征空间表现为三角形，与理论上的概念模型基本一致，符合像元三分模型的基本假设；NDVI-DFI像元三分模型适用于对草原黄枯期NPV的监测，对草原生长期NPV监测并不十分敏感；利用NDVI-DFI像元三分模型估算的fPV和fNPV动态变化与牧草物候发育特征相吻合，可以有效的估算典型草原地区fPV和fNPV值，进一步将其应用于长时间序列的典型草原fPV和fNPV动态变化分析。

英文摘要：

The quantitative estimation of fractional cover of photosynthetic vegetation （fPV）, non-photosynthetic vegetation （fNPV）, and bare soil （fBS） is critical for grassland animal husbandry and land desertification. Remote sensing is an important tool for estimating the fractional cover of vegetation as a key descriptor of grassland ecosystem function. Developing tools that allow for monitoring of vegetation in space and time is a key step needed to improve management of grassland. The present study describes a method for resolving fPV, fNPV, and fBS in the Xilingol steppe region with MODIS-Terra daily surface reflectance data at 500 m resolution （MOD09GHK）. Fractional cover of fPV, fNPV, and fBS was quantified with MOD09GHK data by calculating the Normalized Difference Vegetation Index （NDVI） and the Dead Fuel Index （DFI） and applying a linear unmixing technique. We concurrently analyzed the dynamic change of Xilingol typical grassland of fPV and fNPV. Five MODIS images were acquired on April 5, May 30, July 31, August 21, and November 26 in 2014. The approach assumes that cover fractions are made up of a simple mixture of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. In the present study, one important assumption in our method is that the mixing of fractional cover in NDVI and DFI is linear. DFI is a four-band index that takes into account the differences in spectral features among DFI, photosynthetic vegetation, and bare soil in the VIS-NIR and SWIR wavelength regions, in which the slope of NPV from MODIS band 6 to 7 lies between those of photosynthetic vegetation and bare soil. The correlation between fraction of NPV and DFI was linear. Different end-member extraction methods, including the Pixel Purity Index （PPI） method and 2D scatter plot, were adopted to retrieve the end-member values of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil from NDVI and DFI. The NDVI-DFI feature space follows a triangular distribution, where the vertices