小波分析用于水稻叶片氮含量高光谱反演

Estimation of Nitrogen Content in Rice Leaves with Hyperspectral Reflectance Measurements Using Wavelet Analysis

针对叶片氮营养高光谱诊断中光谱弱信息提取困难的问题,利用小波对作物冠层光谱信号进行分解,提取光谱弱信息建立氮含量反演模型.在东北平原长春市采集水稻冠层高光谱数据建立氮含量预测模型,并对该模型精度进行检验.采用Daubechies小波系的Db5函数对水稻原始反射光谱和导数光谱进行8层小波分解,选择不同尺度和位置的小波系数作为输入参数建立192个反演模型,分析不同输入参数对模型精度的影响,从中选择对应较高模型精度的输入参数组合建立氮含量最佳反演模型.实验结果表明,小波系数预测叶片氮素含量模型具有较高的估算精度,预测值与实测值的复相关系数最大为0.99,显著优于传统光谱指数的估算模型精度.此项研究表明,小波分析在提取反射光谱弱信息反演作物生化成分方面有良好的应用前景.

To solve the problem in diagnosing nitrogen nutrient in rice leaves based on hyperspectral reflectance, we extract weak information from the spectral signal to estimate nitrogen content by applying wavelet analysis to decompose the reflectance spectra. Hyperspectral data were collected in China＇s northeast city Changchun. The data were used to develop predictive models of nitrogen contents and test the accuracy of the models. We decompose the reflectance and derivative spectra of rice canopy into eight levels using the Db5 function of Daubechies wavelets, and establish 192 models among the obtained wavelet coefficients at different levels and different decomposition positions. By comparison, wavelet coefficients with high precision are selected to establish the best model. The results indicate that wavelet coefficients can be used to obtain accurate prediction of nitrogen content with a high correlation coefficient up to 0.99. The wavelet-based ap- proach outperforms predictive models based on a range of existing spectral indices, showing good prospects in applications for estimating biochemical components of crops that need to extract weak information from hyperspectral data.