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摘要： 以河南省鹤壁市为研究区，以FY3/MERSI数据为主要遥感信息源，采用基于光谱匹配的自适应最佳端元组合混合像元分解方法提取夏玉米端元丰度值，构建像元中夏玉米端元丰度值和夏玉米种植面积百分比值之间多种方程形式的回归模型，综合模型建立时的相关系数、显著性水平和验证样点的均方根误差情况，选择立方模型对研究区夏玉米种植面积进行估算。经验证，遥感估算的夏玉米种植面积精度为97.1%，位置精度为82.5%。研究结果可为采用中低空间分辨率遥感数据在种植结构复杂地区准确估算大范围作物的种植面积提供科学基础。 关键词： MERSI;  EVI;  混合像元分解;  回归分析     Abstract: Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure，when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop，mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However，the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches，taking Hebi City，Henan Province as the study area，which located in Huang\|Huai\|Hai plain，has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model，and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building，significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%，the position precision is 82.5%.

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