{"title":"Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale","authors":"","doi":"10.1016/j.eja.2024.127360","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Accurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index.</p></div><div><h3>Objective</h3><p>In this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale.</p></div><div><h3>Methods</h3><p>To address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method.</p></div><div><h3>Results and conclusions</h3><p>The Vis_H+RF method of Tongliao experimental station has an R<sup>2</sup> of 0.96 in the vegetative stages and a R<sup>2</sup> of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities.</p></div><div><h3>Significance</h3><p>The LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.</p></div>","PeriodicalId":51045,"journal":{"name":"European Journal of Agronomy","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Agronomy","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1161030124002818","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
Context
Accurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index.
Objective
In this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale.
Methods
To address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method.
Results and conclusions
The Vis_H+RF method of Tongliao experimental station has an R2 of 0.96 in the vegetative stages and a R2 of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities.
Significance
The LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.
背景准确监测叶面积指数(LAI)有利于及时采取有针对性的管理措施。本研究利用无人机的可见光(RGB)和多光谱遥感数据以及植物冠层分析仪 LAI-2200 C 的地面测量 LAI 数据,对玉米 LAI 进行精细尺度反演。方法针对无人机图像空间分辨率与地面测量的 LAI 之间存在空间尺度不匹配的问题,通过剔除 LAI-2200 C 仪器测量的最外圈数据,根据植株高度计算无人机图像重新采样后的空间分辨率,以及基于圆的重新采样方法,减小无人机图像数据与地面测量数据之间的尺度差。最后,通过上述方法对无人机图像进行重采样,提取植被指数和冠层高度特征作为随机森林模型的输入变量,分别建立玉米无性期和生育期的 LAI 反演模型,即 Vis_H+RF 方法。意义基于该方法构建的LAI反演模型与实际情况基本一致,可为玉米生长监测提供数据支持。
期刊介绍:
The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics:
crop physiology
crop production and management including irrigation, fertilization and soil management
agroclimatology and modelling
plant-soil relationships
crop quality and post-harvest physiology
farming and cropping systems
agroecosystems and the environment
crop-weed interactions and management
organic farming
horticultural crops
papers from the European Society for Agronomy bi-annual meetings
In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.