基于车载近地遥感的玉米苗期长势动态诊断研究

Research on Corn Growth Based on Vehicle-borne Ground-based Remote Sensing Dynamic Prediction

为了使用车载平台对玉米叶绿素含量进行快速预测,优化车载系统测量结果,提出了一种适于玉米苗期利用车载系统动态预测冠层叶片动态光谱指数(MPRI),构建了基于PRI的叶绿素含量预测模型,结合空间插值手段对其空间分析能力进行了分析和评估。结果表明:基于车载系统动态获取的冠层叶片MPRI,对于单点位置的冠层叶绿素含量预测效果较好,模型决定系数为0.81;车载系统动态获取的玉米冠层MPRI能够通过定值阈值较为准确地被识别,利用其构建的玉米冠层叶片叶绿素含量预测模型具有较好的单点预测效果。结合反距离加权插值法进行空间分析,能够得到较佳的空间预测效果,空间分布预测偏差率小于7%的数据占总数据量的85%。基于MPRI构建的冠层叶绿素的预测模型,结合GIS手段进行空间分析能够得到较佳的空间预测效果,为车载系统动态测量提供了新的思路。

The application of ground-based remote sensing is significant for understanding the growth of corn seedlings and providing the accurate and scientific data for precision agriculture.The vehicle-borne system is one of the most important tools for growth monitoring and management,since it is efficient,flexible and economical to be operated in small region.However,the vehicle-borne growth monitoring system can not maintain steady operation due to the row spacing of corn.The background interference on the reflectance will not be suppressed effectively,which will result in a deviation in the growth monitoring.In order to overcome this problem,a novel spectral index,named MPRI,was developped in this paper,including the GIS analysis.The results indicated that it had the satisfactory forecasting accuracy of chlorophyll content by using MPRI,with an average R2 of 0.81.By focusing on the optimization of the spatial datum distribution obtained by vehicle,it was transformed with the inverse distance weighted（IDW）.The deviation rate less than 7 % between the predication and real value accounted for about 85 %of the entire data.The theoretical analysis and test results proved that the spectral index-MPRI had the characteristics of estimating the corn growth by the traverse measurement system.It also presented the good effect on solving the dynamic crop growth predication with severe background interference.