叶绿素作为参与植被光合作用最重要的色素, 是监测毛竹虫害的一项重要指标。 通过对不同光谱数据集进行波长筛选, 建立虫害胁迫下竹叶叶绿素含量的高光谱估算模型, 为利用高光谱遥感监测毛竹虫害提供理论依据。 试验在福建省毛竹生产基地顺昌县进行, 使用ASD FieldSpec 3光谱仪采集不同虫害程度竹叶光谱102条, 并利用SPAD-502叶绿素计测定相应叶片叶绿素含量。 通过对比不同虫害程度竹叶的光谱特征, 探测利用高光谱数据估算叶绿素含量的机理。 对竹叶原始光谱（OS）进行包络线去除（CR）、 一阶导数（FD）、 包络线去除一阶导数（CR-FD）变换, 分析不同光谱数据与叶绿素含量的相关性, 并利用连续投影算法（SPA）分别提取4种光谱的特征波长。 采用基于x-y距离结合的样本划分法（SPXY）和随机法对4种光谱数据集进行划分, 结合多元逐步回归（MSR）建立竹叶叶绿素含量估算模型, 分析光谱变换及样本划分对估算叶绿素含量的影响。 结果表明, 不同虫害程度竹叶光谱反射率差异明显, 主要表现为可见光波段范围内的“绿峰”和“红谷”的逐渐消失, “红边”斜率减小, 近红外波长反射率降低。 通过光谱变换可有效提升光谱与叶绿素含量的相关性, 其中CR-FD光谱与叶绿素含量在724 nm处的相关系数最大。 经连续投影算法提取的不同光谱数据集的特征波长集中分布在绿光、 红光、 “红边”位置, 多个被选择波长位于与叶绿素含量相关性较高的波长区（600~750 nm）。 基于SPXY样本划分法建立的MSR模型相比于随机样本划分法能显著提升叶绿素含量的估算精度, 其中R2和RPD平均提高0.1和0.5, RMSE平均降低0.7。 以CR-FD光谱特征波长结合SPXY样本划分法建立的多元逐步回归模型对竹叶叶绿素含量的估算精度最高, R2, RMSE和RPD分别为0.835, 2.604和2.364, 可对虫害胁迫下毛竹叶片叶绿素含量进行准确的估算。

As the most important pigment involved in photosynthesis of plant, the chlorophyll is an important indicator for monitoring bamboo pests. This study aims to establish the hyperspectral estimation model for the chlorophyll content of bamboo leaves under pests stress by wavelength screening of different spectral data sets, and provide a theoretical basis for monitoring the pests of bamboo by hyperspectral remote sensing. The test was carried out in Shunchang County, the bamboo production base in Fujian Province. The ASD FieldSpec 3 spectrometer was used to collect 102 bamboo leaves spectra of different pest levels, and the chlorophyll content of the corresponding leaves was determined by SPAD-502 chlorophyll meter. By comparing the spectral characteristics of bamboo leaves with different pest levels, the mechanism of estimating chlorophyll content with hyperspectral data was explored. The original spectrum (OS) of the bamboo leaves was subjected to continuum removal (CR), first derivative (FD), and continuum removal-first derivative (CR-FD), and the correlation between different spectral data and chlorophyll content was analyzed. The characteristic wavelengths of the four spectra were extracted by the successive projection algorithm (SPA). Four spectral datasets were divided by sample set partitioning based on joint x-y distances method (SPXY) and random method. Combined with multiple stepwise regression (MSR), the chlorophyll content estimation model of bamboo leaves was established, and the effects of spectral transformation and sample partitioning on estimating chlorophyll content were analyzed. The results showed that there were significant differences in the spectral reflectance of bamboo leaves with different pest levels. The main manifestations were the gradual disappearance of the “green peak” and “red valley” in the visible light range, the “red edge” was levelled and the near-infrared wavelength reflectance was reduced. The spectral transformation could effectively improve the correlation between the spectrum and chlorophyll content, and the correlation coefficient between the CR-FD spectrum and chlorophyll content at 724 nm was the largest. The characteristic wavelengths of different spectral data sets extracted by the successive projection algorithm were concentrated in the green band, red band, and “red edge”, and the multiple selected wavelengths were located in bands (600～750 nm) that highly correlated with chlorophyll content. The MSR model based on SPXY sample partitioning method could significantly improve the estimation accuracy of chlorophyll content compared with the random sample partitioning method, in which R2 and RPD increased by 0.1 and 0.5, and RMSE decreased by 0.7 on average. The multiple stepwise regression model established by CR-FD spectrum characteristic wavelengths combined with SPXY sample partitioning method had the highest accuracy for estimating chlorophyll content of bamboo leaves, and the R2, RMSE, RPD were 0.835, 2.604 and 2.364 respectively, which could accurately estimate the chlorophyll content of bamboo leaves under pests stress.