基于高光谱反射率的棉花冠层叶绿素密度估算

为了进一步提高棉花叶绿素密度高光谱估算精度,该研究以棉花冠层叶绿素密度以及冠层高光谱反射率为数据源,在分析叶绿素密度与原始高光谱反射率(R)、一阶导数光谱反射率(DR)、已有光谱指数及全波段组合指数相关性的基础上,采用线性及多元逐步回归技术构建了叶绿素密度高光谱诊断模型,系统对比分析了以上4种光谱形式用于棉花冠层叶绿素密度诊断的精度。结果表明:1)基于一阶导数光谱反射率的估算模型精度明显优于原始光谱反射率;2)基于比值指数或归一化指数形式的估算模型精度及稳定性要优于单波段或多波段的线性模型;3)单波段变量DR756、全波度组合比值指数DR635/DR643以及归一化指数(DR1055-DR684)/(DR1055+DR684)均可较好的实现叶绿素密度估算,其中由DR635/DR643为自变量的模型所得到棉花冠层叶绿素密度估算值与实测值拟合最好,相关系数达到0.821。该研究可为高光谱技术在棉花冠层叶绿素密度诊断中的更好应用提供参考。

Estimating chlorophyll density of cotton canopy by hyperspectral reflectance

In order to further improve the estimation accuracy of cotton chlorophyll density by hyperspectral reflectance, canopy hyperspectral reflectance and chlorophyll density were recorded at four different growth stages of cotton in a field experiment. All two-band combinations (350 to 1100 nm) in the ratio type of vegetation index (RVI) and the normalized difference type of vegetation index (NDVI) were performed on raw spectral reflectance and the first derivative reflectance, and then the correlation between all two-band combinations and cholorophyll density were determined. The coefficients (r) were presented in matrix plots. Basing on the results of correlation analysis, the estimation models of chlorophyll density were established using linear regression and multiply stepwise regression methods, and then the predictive power of four predictors were analyzed, i.e. single narrow band raw reflectance and the first derivative reflectance, the established vegetation indices for chlorophyll density estimation, and the optimal band combination vegetation indices. Three main conclusions were obtained: 1) The performance of first derivative reflectance was evidently better than raw reflectance; 2) The precision and stability of estimation models based on vegetation indices were normally much higher than models based on single band or multiply bands; 3) Among four types independent variables, DR756 was the best candidate for single-band models, ratio index DR635/DR643 and normalized difference index (DR1055-DR684)/(DR1055+DR684) were the best among all band combination indices. In conclusion, the model based on DR635/DR643 obtained the most satisfied results for the estimation of chlorophyll density, and the correlation coefficient between estimated and measured chlorophyll density reached 0.821. The study will provide a reference for the better application of hyperspectral reflectance in chlorophyll density derivation.