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分析了北京大屯科技站水稻叶面积指数 (LAI)、叶绿素密度 (CH .D)与高光谱分辨率遥感数据在整个生育期内的变化过程。利用微分技术处理水稻群体反射光谱以减少土壤等低频背景光谱噪音的影响。通过单相关分析和逐步回归方法研究水稻LAI、CH .D分别与光谱反射率、反射率的一阶微分光谱的相关关系 ,并建立预测回归方程。结果表明 ,微分技术能够改善光谱数据与LAI、CH .D的相关性 ,CH .D与光谱数据的相关明显优于同LAI的。
The changes of rice leaf area index (LAI), chlorophyll density (CH. D) and hyperspectral resolution remote sensing data of Beijing Datun Science and Technology Station during the whole growth period were analyzed. Using differential technique to deal with rice population reflectance spectrum to reduce the impact of soil and other low-frequency background spectral noise. Single correlation analysis and stepwise regression method were used to study the correlation between LAI, CH. D and first order differential spectra of spectral reflectance and reflectance respectively, and the regression equation was established. The results show that the differential technique can improve the correlation between spectral data and LAI, CHD, and the correlation between CHD and spectral data is better than that of LAI.