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 paper studied the variational process of leaf area index (LAI), leaf chl orophyll density (CH.D) and Hyperspectral data with growing period. The correla tion between the hyperspectral data and LAI, CH.D of rice was also analyzed. Sp ectral derivative technique was used to suppress the effects of low frequency sp ectral noises on background (such as Soil and so on). The derivatives of relfect ance spectrum can enhance the correlation and improve the precision of predictin g LAI and CH.D. Results show that the derivatives of reflectance spectrum and CH.D more markedly correlate with LAI at some wavelength; CH.D is more av ailable to express crop canopy spectral information than LAI.