基于高光谱和连续投影算法的棉花叶面积指数估测

为实现快速、无损、实时监测不同灌溉处理下棉花植株叶面积指数,借助高光谱遥感技术获取了棉花植株4个生育期的冠层反射率,同时获取每株棉花的叶面积指数,用一阶导数、二阶导数、标准正态变换,多元散射校正、小波分析等光谱预处理方法,经过连续投影算法提取特征波段,用偏最小二乘法建立4个生育期（总体）和各生育期的高光谱估算模型。对比6种预处理方法在4个生育期和各生育期建模精度表明,4个生育期（总体）、蕾期、花期、花铃期的小波分解尺度为4、2、8、2,模型分别为CWT-SPA-PLS、CWT-FD-SPA-PLS、CWT-SPA-PLS、CWT-FD-SPA-PLS时可取得较好的精度;经二阶导数处理后,铃期可取得较好的结果,R2和RPD分别0.973、5.3295,优于其他预处理。试验结果表明,利用预处理方法尤其是小波分析方法得到的光谱信息可有效估测棉花4个生育期（总体）和各生育期的叶面积指数。

Cotton LAI Estimation Based on Hyperspectral and Successive Projection Algorithm

In order to realize rapid, non-destructive and real-time monitoring of the leaf area index of cotton plants under different irrigation treatments, the canopy reflectance of cotton plants in four growth periods was obtained with the help of hyperspectral remote sensing technology, and the leaf area index of each cotton plant was obtained at the same time. The spectral preprocessing methods such as first-order derivation, second-order derivation, standard normal variate, multiple scattering correction and wavelet analysis were used to extract characteristic bands through continuous projection algorithm, PLS was used to establish hyperspectral estimation models for four growth periods and each growth period. Comparing the modeling accuracy of six pretreatment in four growth stages and each growth stage, it was shown that the wavelet decomposition scales of four growth stages, bud stage, flower stage and flower boll stage were 4, 2, 8 and 2, respectively, and the models were CWT-SPA-PLS, CWT-FD-SPA-PLS, CWT-SPA-PLS and CWT-FD-SPA-PLS respectively, which can achieve better accuracy;after SD treatment, better results were obtained in boll stage, R2 and RPD were 0.973 and 5.3295 respectively, which were better than other pretreatment results. The experimental results showed that the spectral information obtained by the preprocessing algorithm, especially the wavelet analysis method, can effectively estimate the leaf area index of cotton in four growth stages and each growth stage.