图谱合一的近地成像高光谱是现代数字农业对田块尺度的作物长势信息进行动态监测和实时管理的需要, 是促进农业定量遥感发展的重要手段之一。 文章通过自主研制的田间扫描成像光谱仪近地获得盆栽和大田玉米的冠层高光谱影像, 从影像中精确提取玉米不同层位的叶片反射光谱并计算TCARI, OSAVI, CARI, NDVI等多种光谱植被指数, 构建玉米叶绿素含量的光谱预测模型, 并对模型进行了验证。 结果表明, 基于光谱指数MCARI/OSAVI构建的玉米植株叶绿素含量预测模型精度较高, 验证样本预测的决定系数R2=0.887, 预测均方根误差RMSE为1.8。 研究表明, 成像光谱仪在微观尺度上的作物组分光谱信息探测方面具有较大的应用潜力。

As an image-spectrum merging technology, the field-hperspectral imaging technology is a need for dynamic monitoring and real-time management of crop growth information acquiring at field scale in modern digital agriculture, and it is also an effective approach to promoting the development of quantitative remote sensing on agriculture. In the present study, the hyperspectral images of maize in potted trial and in field were acquired by a self-development push broom imaging spectrometer (PIS). The reflectance spectra of maize leaves in different layers were accurately extracted and then used to calculate the spectral vegetation indices, such as TCARI, OSAVI, CARI and NDVI. The spectral vegetation indices were used to construct the prediction model for measuring chlorophyll content.The results showed that the prediction model constructed by spectral index of MCARI/OSAVI had high accuracy. The coefficient of determination for the validation samples was R2=0.887, and RMSE was 1.8. The study indicated that PIS had extensive application potentiality on detecting spectral information of crop components in the micro-scale.