玉米科学2024,Vol.32Issue(4) :92-101.DOI:10.13597/j.cnki.maize.science.20240412

东北高产春玉米氮营养指标的无人机多光谱遥感监测

Unmanned Aerial Vehicle Multispectral Remote Sensing for Monitoring of Nitrogen Nutritional Indicators in High-Yielding Spring Maize in Northeast China

辛江风 明博 薛贝贝 杨宏业 郭慧荣 冯大云 谢瑞芝 王克如 侯鹏 李少昆 禄兴丽
玉米科学2024,Vol.32Issue(4) :92-101.DOI:10.13597/j.cnki.maize.science.20240412

东北高产春玉米氮营养指标的无人机多光谱遥感监测

Unmanned Aerial Vehicle Multispectral Remote Sensing for Monitoring of Nitrogen Nutritional Indicators in High-Yielding Spring Maize in Northeast China

辛江风 1明博 2薛贝贝 2杨宏业 2郭慧荣 1冯大云 2谢瑞芝 2王克如 2侯鹏 1李少昆 1禄兴丽3
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作者信息

  • 1. 宁夏大学农学院,银川 750021;中国农业科学院作物科学研究所/农业部作物生理生态重点实验室,北京 100081
  • 2. 中国农业科学院作物科学研究所/农业部作物生理生态重点实验室,北京 100081
  • 3. 宁夏大学农学院,银川 750021
  • 折叠

摘要

为了探究随机森林算法在春玉米氮营养指标预测精度,于2021-2022年在东北地区进行氮肥梯度试验,以迪卡159为试验材料,设置10个施氮水平,利用无人机搭载多光谱分别在小喇叭口期(V9)、大喇叭口期(V12)和抽雄期(VT)获取遥感数据.利用19个植被指数分别构建地上部生物量(AGB)、植株吸氮量(PNU)、叶面积指数(LAI)和比叶氮(SLN)模型.结果表明,随机森林算法在预测AGB和LAI有较高的精度,R2分别是0.83和0.9.通过相关分析,LAI、AGB、PNU及SLN与结构不敏感色素指数(SIPI)相关性最高,相关系数分别为-0.75、-0.70、-0.84和0.63,SIPI在4个模型中重要性均最高.研究结果表明,随机森林算法在春玉米氮素监测中具有一定的发展潜力,SIPI在氮素监测中有重要作用,研究结果可为春玉米氮素营养监测提供参考依据.

Abstract

To investigate the accuracy of the random forest algorithm for nitrogen nutrition prediction in spring maize,this study conducted a nitrogen fertilization gradient trial in northeast China in 2021-2022.10 nitrogen appli-cation levels were set with Deka 159 as the test material,and remote sensing data were acquired at the small trum-pet stage(V9),large trumpet stage(V12)and male tapping stage(VT)using a UAV with multispectral.Using 19 vege-tation indices,four models were constructed,including aboveground biomass(AGB),nitrogen uptake by plants(PNU),leaf area index(LAI)and specific leaf nitrogen(SLN).The results showed that the random forest algorithm had high accuracy in predicting AGB and LAI,and R2 is 0.83 and 0.9.The correlation analysis of LAI,AGB,PNU and SLN had the highest correlation with the structurally insensitive pigment index(SIPI),with correlation coeffi-cients of-0.75,-0.7,-0.84 and 0.63,respectively;Structurally SIPIis the highest importance among the four mod-els.The results of the study indicate that the random forest algorithm has a certain development potential in spring maize nitrogen monitoring,and theSIPI has an important role in nitrogen monitoring,and the results of the study can provide a reference basis for monitoring nitrogen nutrients in spring maize.

关键词

春玉米/多光谱遥感/随机森林/氮营养指标

Key words

Spring maize/Multispectral remote sensing/Random forest/Nitrogen nutrient index

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基金项目

内蒙古自治区科技重大专项(2021ZD0003)

国家重点研发计划(2016YFD0300605)

现代农业产业技术体系建设项目(CARS-02-25)

财政部-农业部中国农业研究体系以及农业科技创新计划(CAAS-ZDRW202004)

出版年

2024
玉米科学
吉林省农业科学院 国家玉米工程技术研究中心(吉林) 国家玉米改良中心 中国农业科技东北创新中心

玉米科学

CSTPCDCSCD北大核心
影响因子:1.163
ISSN:1005-0906
参考文献量46
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