不同无人机飞行高度下玉米叶片叶绿素相对含量的无人机遥感反演及其指示叶位的识别  

Unmanned aerial vehicle remote sensing inversion of relative chlorophyll content of maize leaves and identification of their indicator leaf at different flight altitudes

作　　者：李瑞鑫 张宝林[1,2,3] 潘丽杰 牛潘婷 斯琴高娃 何美玲[1] LI Ruixin;ZHANG Baolin;PAN Lijie;NIU Panting;Siqingaowa;HE Meiling(College of Chemistry and Environmental Science,Inner Mongolia Normal University,Hohhot 010020,China;Inner Mongolia Key Laboratory of Environmental Chemistry,Hohhot 010020,China;Inner Mongolia Water-saving Agriculture Engineering Research Center,Hohhot 010020,China)

机构地区：[1]内蒙古师范大学化学与环境科学学院,内蒙古呼和浩特010020 [2]内蒙古自治区环境化学重点实验室,内蒙古呼和浩特010020 [3]内蒙古节水农业工程研究中心,内蒙古呼和浩特010020

出　　处：《江苏农业学报》2024年第7期1234-1244,共11页Jiangsu Journal of Agricultural Sciences

基　　金：内蒙古自然科学基金项目(2022LHMS03009);内蒙古自治区科技重大专项(2021ZD0003-1);内蒙古师范大学基本科研业务费专项(2022JBTD009)。

摘　　要：玉米叶片叶绿素含量的空间异质性对其监测精度有影响。本研究旨在基于无人机遥感技术探究玉米叶片叶绿素相对含量(SPAD值)与植被指数间的关系,从而明确指示叶位、无人机的最佳飞行高度。采用随机森林法构建基于植被指数的叶绿素相对含量遥感估算模型,并进行模型的评价。结果表明,玉米灌浆期叶片的叶绿素相对含量高于乳熟期叶片的叶绿素相对含量,植株中部叶片的叶绿素相对含量高于上部、下部叶片的叶绿素相对含量。在玉米灌浆期与乳熟期,玉米叶片SPAD值的指示叶位为第5叶,当无人机飞行高度为20 m时,模型的精度最高[决定系数(R 2)=0.94]。研究结果可为提高叶绿素相对含量遥感监测的精度提供技术支撑,并为农作物的田间智能化管理提供理论依据。The detection accuracy of chlorophyll content in maize leaves is affected by spatial heterogeneity.The purpose of this study was to investigate the relationship between relative chlorophyll content(SPAD value)and vegetation indices of maize leaves based on unmanned aerial vehicle(UAV)remote sensing technology,so as to clarify the indicator leaf and the best UAV flying altitude.The remote sensing estimation model of relative chlorophyll content based on vegetation indices was constructed by random forest method,and the model was evaluated.The results showed that relative chlorophyll content in maize leaves at grain filling stage was higher than that at milking stage,and relative chlorophyll content of middle leaves was higher than that of upper and lower leaves.During the grain filling stage and milking stage,the SPAD value of maize leaves was indicated by the fifth leaf,and the best precision for the regression model(R 2=0.94)was obtained when the flying altitude of UAV was 20 m.The results can provide technical support for improving the accuracy of remote sensing monitoring of relative chlorophyll content,and provide theoretical basis for crop smart management in the fields.

关 键 词：玉米 叶绿素 无人机 遥感 指示叶位 智能 

分 类 号：X87[环境科学与工程—环境工程]