叶面降尘的高光谱定量遥感模型

利用自主设计的叶面降尘量测定方法,测定了榆树叶面的降尘量数据,结合地面高光谱遥感数据,研究了叶面降尘对榆树叶片高光谱特征的影响及叶面降尘量的高光谱定量反演.研究结果表明,叶面降尘可提高可见光波段的反射率,降低近红外波段的反射率,且对可见光波段的影响要大于近红外波段; 叶面降尘对三边位置没有影响,对三边幅值和面积有明显影响; 利用降尘光谱指数和三边参数建立的叶面降尘量模型,只具备粗略预测能力,而采用多元线性回归、主成分回归、偏最小二乘回归建立的模型,均具有很强的预测能力,其中以一阶微分建立的偏最小二乘回归模型的效果最佳,预测决定系数为0.92,预测均方根误差为1.06,样本标准差与预测均方根误差比为8.2.

Quantitative model of foliar dustfall content using hyperspectral remote sensing

By analyzing hyperspectral features of elm foliar dustfall content (FDC), a models of hyperspectral monitoring was built. Relationship between hyperspectral parameters and FDC was investigated by using regression analysis method. The results showed that FDC increased spectral reflectance in the visible band while decreased it in the near infrared band. Foliar dust didn't affect the "three edge" position but significantly affected its amplitudes and areas. FDC of elm was badly predicted with the models based on spectrum index or "three edge" parameter. Models based on multivariate linear regression, principal component regression and partial least squares regression can predict FDC primely. The model with 1st derivative value as variables was the best one for estimating FDC by the hyperspectral. Predictive correlation coefficient, predictive root mean square error, and the ratio of sample standard deviation to predictive root mean square error of this model were 0.92, 1.06, and 8.2, respectively.