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中国农学通报 ›› 2021, Vol. 37 ›› Issue (19): 134-142.doi: 10.11924/j.issn.1000-6850.casb2020-0229

所属专题: 油料作物

• 农业信息·科技教育 • 上一篇    下一篇

基于无人机多光谱遥感的大豆叶面积指数反演

王军1(), 姜芸2()   

  1. 1自然资源部第二地理信息制图院,哈尔滨 150080
    2东北农业大学公共管理与法学院,哈尔滨 150030
  • 收稿日期:2020-07-01 修回日期:2020-09-09 出版日期:2021-07-05 发布日期:2021-07-29
  • 通讯作者: 姜芸
  • 作者简介:王军,男,1979年出生,山东人,高级工程师,硕士,主要从事GIS、遥感、无人机开发与应用研究。通信地址:150080 哈尔滨市南岗区测绘路32号,E-mail: dreamgis@163.com
  • 基金资助:
    国家自然科学基金资助项目“基于光谱分类的区域土壤有机质遥感预测模型研究”(41501357);黑龙江省自然科学基金“田块尺度黑土有机质遥感反演研究”(D20170001)

Inversion of Soybean Leaf Area Index Based on UAV Multispectral Remote Sensing

Wang Jun1(), Jiang Yun2()   

  1. 1The Second Geomatics Cartography Institute, Ministry of Natural Resource, Harbin 150080
    2School of Public Administration and Law, Northeast Agricultural University, Harbin 150030
  • Received:2020-07-01 Revised:2020-09-09 Online:2021-07-05 Published:2021-07-29
  • Contact: Jiang Yun

摘要:

为给大豆科学管理提供基础数据,利用无人机多光谱遥感数据实现对大豆叶面积指数(LAI)的反演估值。从多种光谱植被指数中选出与LAI相关性较好的5种指数,分析探讨在田块尺度上,适用于东北地区的大豆叶面积指数的低空无人机遥感反演模型。结合田间实测LAI数据及模型精度及拟合效果,NDVI模型精度较好,但拟合效果较差,其余4种植被指数模型精度和拟合效果较好,拟合效果R2均达到了0.6以上;支持向量机模型决定系数R2达到0.688,均方根误差达0.016,具有更好的预测能力。2种模型均表明无人机多光谱遥感系统可以快速反演田间大豆叶面积指数,在指导精准农业生产方面具有实用意义。

关键词: 无人机, 植被指数, 回归分析, 支持向量机, 大豆, 叶面积指数

Abstract:

In order to provide basic data for the scientific management of soybean, the inversion and estimation of LAI was realized by using the multispectral remote sensing data of UAV. Five indices with good correlation with LAI were selected from various spectral vegetation indices, and the remote sensing inversion model of soybean leaf area index in northeast China was analyzed and discussed. The results show that except NDVI, the other four vegetation index models have better precision, and the determination coefficient R2 is more than 0.6; support vector machine model, the determination coefficient R2 is 0.688, and the root mean square error is 0.016, which has better prediction ability. Both models show that the UAV multispectral remote sensing system can quickly retrieve the soybean leaf area index in the field, which has practical significance in guiding precision agricultural production.

Key words: UAV, vegetation index, regression analysis, support vector machine, soybean, leaf area index

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