一种玉米苗期冠层叶片营养诊断动态光谱指数

A Novel Vegetation Index(MPRI)of Corn Canopy by Vehicle-Borne Dynamic Prediction

为了有效的解决玉米苗期冠层叶片营养状态车载动态诊断过程中,土壤干扰信息无法剔除的问题,本文提出了一种动态测量用光谱指数MPRI,根据MPRI的构成和特点、论述了利用MPRI辨识土壤与冠层光谱信息的机理,构建了基于MPRI的玉米苗期冠层叶片叶绿素含量的预测模型,通过车载式作物长势检测系统平台,运用模型对玉米苗期冠层叶片营养状态进行动态诊断与评估,取得良好的效果。研究表明:在车载动态条件下测量玉米苗期冠层叶片营养状态时,土壤的MPRI呈正值而玉米冠层的MPRI呈负值,因此使用光谱指数MPRI能够有效识别土壤背景与冠层叶片光谱信息。设定固定的阈值,能够较为准确和便捷的去除土壤背景光谱信息。基于MPRI构建的冠层叶片叶绿素含量的动态测量预测模型,能够准确的表征冠层叶片的叶绿素含量,模型决定系数R2达0.72,动态测量中对植株冠层的识别率达80%。与其他常用的指数相比,在车载动态测量环境下,光谱指数MPRI具有土壤背景信息识别速度快、正确率高,模型预测精度良好等特点,为玉米苗期冠层营养状态的诊断提供了新的途径。

Ground-based remote sensing system is a significant way to understand the growth of corn and provide accurate and scientific data for precision agriculture.The vehicle-borne system is one of the most important tools for corn canopy monitoring. However,the vehicle-borne growth monitoring system cannot maintain steady operations due to the row spacing of corn.The re-flectance of corn canopy,which was used to construct the model for the chlorophyll content,was disturbed by the reflectance of soil background.The background interference with the reflectance could not be removed effectively,which would result in a de-viation in the growth monitoring.In order to overcome this problem,a novel vegetation index named MPRI was developed in the present paper.The tests were carried out by the vehicle-borne system on the cornfield.The sensors which configured the vehi-cle-borne system had 4 bands,being respectively 550,650,766 and 850 nm.It would obtain the spectral data while the vehicle moved along the row direction.The sampling rate was about 1 point per second.The GPS receiver obtained the location informa-tion at the same rate.MPRI was made up by the reflectance ratio of 660 and 550 nm.It was very effective to analyze the infor-mation about the reflectance of the canopy.The results of experiments showed that the MPRI of soil was the positive value and the MPRI of canopy was the negative value.So it is easier to distinguish the spectral information about soil and corn canopy by MPRI.The results indicated that：it had satisfactory forecasting accuracy for the chlorophyll content by using the MPRI on the moving monitoring.The R2 of the prediction model was about 0. 72.The R2 of the model of NDVI,which was used to represent the chlorophyll content,was only 0. 24.It indicates that MPRI had good measurement results for the dynamic measurement process.It provided the novel measurement way to get the canopy reflectance spectra and the better vegetation index to construct the prediction model of the contents of chlorophyll.