干旱地区农业研究2025，Vol.43Issue(5)：224-233,10.DOI:10.7606/j.issn.1000-7601.2025.05.22
基于无人机多光谱遥感的滴灌单元尺度棉花生长指标反演
Inversion of cotton growth indexes at drip irrigation subunit scale based on UAV multi-spectral remote sensing
摘要
Abstract
Based on the UAV remote sensing platform equipped with RTK module and 4 bands multi-spectral camera,canopy surface elevation and multi-spectral image data at cotton bud stage,flowering stage,and bolling stage were collected.The cotton plant height,leaf area index,and biomass at drip irrigation subunit scale were de-termined.The Digital Surface Model was constructed for retrieving cotton plant height.Partial Least Squares Regres-sion,Support Vector Machine,and Random Forest models were used to establish inverse models of cotton leaf area index and biomass.The optimal model was used to predict the crop growth index at drip irrigation subunit scale.The results showed that the determination coefficient(R2)of the telemetry cotton height and measured plant height based on the digital surface model method was 0.59.The random forest model had the best inversion effect for cotton leaf area index(LAI)and biomass,while the model R2 were 0.56 and 0.74,respectively.The coefficient R2 be-tween measured and predicted plant height for the drip irrigation subunits was 0.83.The average difference between the measured and predicted leaf area index at bud stage,flowering stage,and bolling stage were 0.17,0.46,and 0.97,respectively.The average difference between the measured and predicted biomass was 0.12 t·hm-2,1.37 t·hm-2,and 3.17 t·hm-2,respectively,and the data points basically fell within the 95％agreement limit.The measured value of crop growth index in drip irrigation subunits were consistent with the predicted value.关键词
棉花/生长指标/反演模型/无人机多光谱遥感/滴灌单元Key words
cotton/growth index/inversion model/UAV multispectral remote sensing/drip irrigation subunit分类
农业科技引用本文复制引用
徐亚薇,王珍,栗岩峰,李久生..基于无人机多光谱遥感的滴灌单元尺度棉花生长指标反演[J].干旱地区农业研究,2025,43(5):224-233,10.基金项目
国家重点研发计划项目(2022YFD1900404) （2022YFD1900404）
国家自然科学基金(52279053) （52279053）
