摘要
为了进一步提高玉米叶绿素含量的高光谱估算精度,该文测定了西北地区玉米乳熟期叶片的光谱反射率及其对应的叶绿素相对含量(soil and plant analyzer development,SPAD)值,分析了一阶微分光谱、高光谱特征参数与SPAD的相关关系,构建了基于一阶微分光谱、高光谱特征参数和BP神经网络的SPAD估算模型,并对模型进行验证;再结合主成分回归(principal component regression,PCR)、偏最小二乘回归(partial least squares regression,PLSR)以及传统回归模型与BP神经网络模型进行比较。结果表明:SPAD值与一阶微分光谱在763nm处具有最大相关系数(R=0.901);以763 nm处的一阶微分值、蓝边内最大一阶微分为自变量建立的传统回归模型可用于玉米叶片SPAD估算;将构建传统回归模型时筛选到的光谱参数作为输入,实测SPAD值作为输出,构建BP神经网络模型,其建模与验模R2分别为0.887和0.896,RMSE为2.782,RE为4.59%,与其他回归模型相比,BP神经网络模型预测精度最高,研究表明BP神经网络对叶绿素具有较好的预测能力,是估算玉米叶片SPAD值的一种实时高效的方法。
Leaf chlorophyll content provides valuable information about the productivity, physiological status of vegetation. Measurement of hyperspectral reflectance offers a rapid, nondestructive method for leaf chlorophyll content estimation. In order to improve the accuracy of hyperspectral estimation about the leaf chlorophyll content, in this paper, the modeling of chlorophyll content of maize leaves based on the hyperspectrum was developed. The field experiments were conducted in the testing farm of Northwest Agriculture and Forest University, Yangling City, Shaanxi Province. During the maize growth period of milk stage, hyperspectral reflectance measurements were collected in wavelength of 350 to 2500 nm using spectrometer（SVC HR-1024i）, and at the same time, chlorophyll content of maize leaves was obtained by using SPAD-502. There were totally 120 samples collected, two thirds of which were utilized as the training set and remaining one third as the validation set. The model constructed relied on the training set and the validation set was evaluated, respectively. The correlation between first derivative spectra, hyperspectral characteristic parameters and SPAD values were analyzed. Then single variable linear and nonlinear fitting traditional regression models respectively based on first derivative spectra and hyperspectral characteristic parameters were established to estimate the SPAD values. Besides, taken the first derivative values at 763 nm, the maximum first derivative values within blue edge（Db）, red edge position（λr） and blue edge area（SDb） as the input parameters, the measured SPAD values as the output parameters, BP neural network model was built. By using the same input parameters, principal component regression（PCR） and partial least squares regression（PLSR） were used to estimate the SPAD values, too. Then we compared the predictive power of traditional regression models, PCR and PLSR models to BP neural network model. Some critical conclusions were made based on the study. First, the maximum correlation coefficient between SPAD values and first derivative spectra located at 763 nm（R=0.901） and the polynomial model was better than the linear model. As to the hyperspectral characteristic parameters, the variable among which the maximum first derivative values within blue edge（Db） was significantly related with SPAD values（R=-0.850） and its linear model was the best model of SPAD estimation models established by the hyperspectral characteristic parameters. The coefficients of determination for the calibration set of the two traditional regression models were 0.868 and 0.711, and the corresponding values of root mean square error（RMSE） were 3.069 and 4.340; for the validation set, the coefficients of determination were 0.864 and 0.743, and the values of RMSE were 3.186 and 4.317. Second, when using the BP neural network, PCR and PLSR established estimation models, the coefficients of determination for the calibration set were 0.887, 0.813 and 0.673 respectively, and the corresponding values of RMSE were 3.169, 3.495 and 5.797, respectively; for the validation set, the coefficients of determination were 0.896, 0.854 and 0.704, and the corresponding values of RMSE were 2.782, 3.221 and 6.034. Third, compared the five SPAD estimation models, BP neural network model achieved the best result in this research and the coefficients of determination of the calibration set and the validation set were highest, the value of RMSE of the validation set was lowest. The traditional regression model based on first derivative values at 763 nm performed second to BP neural network. Finally, BP neural network model had better predictive power of the chlorophyll content. The results showed that the method was a real-time and efficient method for maize leaf SPAD estimation. Our research may provide a theoretical basis for the improvement of remote sensing inversion accuracy of maize chlorophyll content.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第16期135-142,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家高技术研究发展计划(863计划)资助项目(2013AA102401)
关键词
光谱分析
神经网络
模型
一阶微分光谱
高光谱特征参数
叶绿素相对含量值
玉米
spectrum analysis
neural networks
models
first derivative spectra
hyperspectral characteristic parameter
SPAD value
maize
作者简介
李媛媛，女，河北唐山人，研究方向为遥感模型与信息处理。杨凌西北农林科技大学资源环境学院，712100。Email：yuanyuanli@nwsuaf．edu．cn
通信作者：常庆瑞，男，陕西子洲人，教授，博士生导师，主要从事土地资源与空间信息技术研究。杨凌西北农林科技大学资源环境学院，712100。Email：changqr@nwsuaf.edu．cn
