基于无人机多光谱技术的甜菜冠层叶绿素含量反演  

Inversion of Chlorophyll Content in Sugar Beet Canopy Based on UAV Multispectral Technique

作　　者：汪旭 邓裕帅 练雪萌 王宇光[1] 於丽华[1] 耿贵[1] WANG Xu;DENG Yushuai;LIAN Xuemeng;WANG Yuguang;YU Lihua;GENG Gui(College of Advanced Agriculture and Ecological Environment,Heilongjiang University,Harbin 150080)

机构地区：[1]黑龙江大学现代农业与生态环境学院,哈尔滨150080

出　　处：《中国糖料》2022年第4期36-42,共7页Sugar Crops of China

基　　金：财政部和农业农村部国家现代农业产业技术体系(糖料)建设项目“甜菜种植制度”(CARS-170209)资助。

摘　　要：无人机多光谱常用的植被指数包括优化土壤调整植被指数(OSAVI)、归一化差异植被指数(NDVI)、绿色归一化植被指数(GNDVI)、叶片叶绿素指数(LCI)、归一化差异红边指数(NDRE)。为了研究植被指数与甜菜叶绿素含量的相关性,实时监测管理甜菜生长状况和产质量。基于5个不同施氮水平下(0、30、60、90、120 kg/hm^(2))甜菜多光谱遥感影像和田间实测冠层叶绿素含量数据,采用大疆精灵4多光谱无人机对甜菜叶丛快速生长期和块根糖分增长期进行监测,建立叶绿素含量的反演模型。结果表明:随着施氮水平的增加,叶绿素含量和产量都显著提高;施氮水平30 kg/hm^(2)比0 kg/hm^(2)处理的甜菜含糖率有一定增长趋势(增长0.13个百分点),然后随着施氮水平的增加含糖率显著下降;根据5个植被指数与大田实测叶绿素含量值的拟合效果,拟合值R2均在0.7以上,OSAVI为最佳植被指数模型,拟合效果最好,决定系数R2=0.764,均方根误差RMSE=0.396,标准均方根误差NRMSE=8.63%,具有最高的预测精度。因此,无人机遥感技术对甜菜叶绿素含量估测,指导精准施用氮肥,实现增产、增糖和增收具有重要的理论意义。Commonly used vegetation indices for UAV multispectral include Optimized Soil Adjusted Vegetation Index(OSAVI),Normalized Difference Vegetation Index(NDVI),Green Normalized Vegetation Index(GNDVI),Leaf Chlorophyll Index(LCI),and Normalized Difference Red Edge Index(NDRE).In order to investigate the correlation between vegetation indices and chlorophyll content of sugar beet,the growth condition and yield quality of managed sugar beet were monitored in real time.Based on multispectral remote sensing images of sugar beet at five different nitrogen application levels(0,30,60,90,120 kg/hm^(2))and actual canopy chlorophyll content data in the field,the inverse model of chlorophyll content was established by using DJI Elf 4 multispectral UAV to monitor sugar beet leaf clusters during the rapid growth period and tuber sugar growth period.The results showed that the chlorophyll content and yield increased significantly with the increase of nitrogen application level;the sugar content of sugar beet under the nitrogen rate of 30 kg/hm^(2) had a certain increasing trend(0.13 percentage point increase)compared with 0 kg/hm^(2) treatment,and then the sugar content decreased significantly with the increase of nitrogen application level;according to the fitting effect of five vegetation indices with the actual measured chlorophyll content values in the field,the fitted values of R2 were all greater than 0.7.OSAVI is the best vegetation index model with the best fitting effect,the coefficient of determination R2=0.764,root mean square error RMSE=0.396,and standard root mean square error NRMSE=8.63%,which has the highest prediction accuracy.Therefore,UAV remote sensing technology has important theoretical significance in estimating chlorophyll content of sugar beet and guiding accurate application of nitrogen fertilizer to achieve increased yield,sugar and income.

关 键 词：甜菜 无人机遥感 多光谱 叶绿素 植被指数 

分 类 号：S566.3[农业科学—作物学]