2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆(Glycine max)冠层高光谱反射率与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized difference vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大豆叶绿素a与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSR等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度(R²>0.75);小波能量系数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明,小波分析可以对高光谱进行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。

Aims A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters. We use a regression model, based on wavelet-transformed reflectance, and vegetation indices (VI) to estimate a wide range of soybean (Glycine max) canopy reflectances to study the sensitivity of wavelet-transformed reflectance and vegetation indices to soybean chlorophyll a concentration. We modify some VI to enhance their sensitivity to variations in chlorophyll a concentration.
Methods We collected soybean canopy hyperspectral reflectance and chlorophyll a concentration data in 2003 and 2004 at two sites in the black soil belt of China. We correlated reflectance, derivative reflectance and soybean chlorophyll a concentration and regressed vegetation indices (NDVI, SAVI, RDVI and MSRI) and soybean chlorophyll a concentration. We transformed soybean canopy reflectance with wavelet analysis and applied extracted wavelet energy coefficient in a regression model for estimation of chlorophyll a concentration.
Important findings Soybean canopy reflectance shows a negative correlation with chlorophyll a concentration in the visible spectral region, while it shows a positive correlation with soybean chlorophyll a concentration in the near-infrared region. Reflectance derivative has a strong relationship with soybean chlorophyll a concentration in the blue, green and red edge spectral region, with maximum correlation coefficient in the red-edge region. Four vegetation indices have strong correlations with soybean chlorophyll a concentration, with R² >0.75. The single variable regression model based upon wavelet-extracted reflectance energy can accurately estimate soybean chlorophyll a concentration, with R ² about 0.75, while R ² was 0.85 with the multivariate regression model. Our study indicated that wavelet analysis can be applied to in-situ collected hyperspectral data for soybean chlorophyll a concentration estimation with accurate prediction and in the future wavelet analysis methods should be applied to hyperspectral data for estimation of other vegetation biophysical and biochemical parameters.