选择线性混合像元分解模型、亚像元模型、最大三波段梯度差法模型以及修正的三波段梯度差法的2个变异模型来提取植被覆盖度，结合地面实测数据，探讨了提取干旱区荒漠稀疏植被覆盖度信息的适宜模型，并以简单平均法模拟了不同尺度的覆盖度影像，通过尺度上推检验了模型在MODIS尺度上的反演效应.结果表明：线性混合像元分解模型反演覆盖度的精度高于其他模型，适于稀疏植被地区，但端元的正确选取较难，从而影响其运用；亚像元分解模型是一个通用模型，植被分类图越精细，通过亚像元分解模型得到的覆盖度精度越高，但这也同时意味着该模型需要测定大量的输入参数；最大三波段梯度差法的算法简单、易于操作，其在农田等中高植被覆盖区及裸土区的预测值与实测值接近，但对干旱区稀疏植被的估计精度偏低；修正后的三波段最大梯度差法模型在稀疏植被覆盖区的预测值与实测值基本一致，在不同尺度上反演的覆盖度信息与实测值的一致性较好.该方法可有效提取干旱区低覆盖度植被信息.
Five kinds of remote sensing inversion models, i.e., linear spectral un-mixing model, sub-pixel un-mixing model, maximal gradient difference model, and two modified maximal gradient difference models, were used to derive fc from remote sensing data, and the results were compared with those measured in field, aimed to select appropriate model for deriving the data of the coverage of sparse desert vegetation in arid area. The virtual multi-scale coverage images were generated by using the simple mean scale extending method to verify the inversion information from MODIS data. It was shown that linear un-mixing spectral model had a higher precision than the other models, being applicable for deriving the data of the coverage of sparse desert vegetation, but the selection of end member was rather difficult and affected the application of the model. Sub-pixel un-mixing model was universal, high precision could be obtained based on finely detailed vegetation map, but needed to measure lots of parameters. Maximal gradient difference model was simple and easy to perform, by which, the values of the coverage of crops and bare land predicted with the original model were close to the field-measured results, but the values of the coverage of sparse vegetation were underestimated. The results predicted by the modified three-band maximal gradient difference models were close to the field-measured values, and the inversed results of vegetation coverage under different scales were ideal, indicating that these models were reliable to effectively extract the information of the coverage of sparse vegetation in arid area.