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Smart Agriculture ›› 2020, Vol. 2 ›› Issue (3): 118-128.doi: 10.12133/j.smartag.2020.2.3.202006-SA001

• 信息处理与决策 • 上一篇    下一篇

基于无人机多光谱遥感的夏玉米叶面积指数估算方法

邵国敏1(), 王亚杰1, 韩文霆1,2()   

  1. 1.西北农林科技大学 机械与电子工程学院,陕西杨凌 712100
    2.西北农林科技大学 水土保持研究所,陕西杨凌 712100
  • 收稿日期:2020-06-02 修回日期:2020-08-01 出版日期:2020-09-30 发布日期:2020-10-09
  • 基金资助:
    国家自然科学基金项目(51979233);杨凌示范区产学研用协同创新重大项目(2018CXY-23)
  • 作者简介:邵国敏(1991-),男,博士研究生,研究方向为无人机遥感技术估算作物需水方法研究。E-mail:shaoguomin@nwafu.edu.cn
  • 通讯作者: 韩文霆 E-mail:shaoguomin@nwafu.edu.cn;hanwt2000@126.com

Estimation Method of Leaf Area Index for Summer Maize Using UAV-Based Multispectral Remote Sensing

SHAO Guomin1(), WANG Yajie1, HAN Wenting1,2()   

  1. 1.College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    2.Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
  • Received:2020-06-02 Revised:2020-08-01 Online:2020-09-30 Published:2020-10-09
  • corresponding author: Wenting HAN E-mail:shaoguomin@nwafu.edu.cn;hanwt2000@126.com

摘要:

无人机多光谱遥感技术可以快速、无损地监测农作物叶面积指数(LAI)。为研究水分胁迫条件下,利用无人机多光谱植被指数估算夏玉米LAI的可行性,本研究基于无人机多光谱遥感系统,结合同时期实地采集的夏玉米LAI,选择5种植被指数,包括归一化差值植被指数(NDVI)、土壤调节植被指数(SAVI)、增强型植被指数(EVI)、绿度归一化植被指数(GNDVI)和抗大气指数(VARI),作为模型输入参数,使用随机森林回归算法建立全生育期不同灌溉条件下大田玉米冠层植被指数与LAI之间的关系模型,并与一元线性回归和多元线性回归算法建立的模型进行对比分析。结果表明,在充分灌溉条件下,植被指数的多元线性回归模型可以较好地估算LAI(R2 = 0.83);在水分胁迫条件下,植被指数的随机森林回归模型可以较好地估算LAI(R2 = 0.74~0.87),水分胁迫因素对该模型影响较小,且NDVI和VARI对估算LAI的贡献最大。上述结果表明基于无人机多光谱遥感技术,使用随机森林回归算法估算多种灌溉条件下的夏玉米LAI是可行的。该研究为实现快速、准确地监测全生育期不同灌溉条件下的大田夏玉米LAI提供了技术和方法支持。

关键词: 无人机, 叶面积指数(LAI), 植被指数, 多光谱遥感, 水分胁迫, 随机森林回归

Abstract:

Maize is an important food crop in China. In order to quickly and non-destructively estimate summer maize leaf area index (LAI) under different water stress conditions, in this study, maize samples with multiple irrigation treatments throughout the growth period were used for modeling analysis. Then, based on the unmanned aerial vehicle (UAV) multi-spectral remote sensing technology, combined with the summer maize LAI collected in the field during the same period, five kinds of vegetation indices, including the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected in this research as model input parameters, and random forest regression algorithm was used to establish the relationship between the field maize canopy vegetation indices and LAI under different irrigation conditions during the entire growth period. The accuracies of the model were compared with that of the model established by the university linear regression and multiple linear regression algorithms. The results showed that under sufficient irrigation condition, the vegetation index using multiple linear regression model could well (R2 = 0.83, RMSE = 0.05) estimate LAI; under water stress conditions, the vegetation index using random forest regression model could well estimate LAI (R2 = 0.74~0.87, RMSE = 0.02~0.10), water stress factors had little effect on the random forest regression model, and NDVI and VARI contributed the LAI estimation model better. The spatial distribution map of LAI was generated based on the random forest regression algorithm. The above results showed that it was feasible to use the random forest regression algorithm to estimate the summer maize LAI under various irrigation conditions based on the UAV multi-spectral remote sensing technology. The results indicates that the model established has a good applicability. This research can provide technical and method support for the rapid and accurate monitoring of field summer maize LAI under different irrigation conditions during the entire growth period.

Key words: UAV, leaf area index, vegetation index, multispectral, water stress, random forest regression

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