智能化农业装备学报（中英文）

基于不同飞行高度无人机多光谱及集成学习的冬小麦叶面积指数反演研究

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刘豪 ¹ , 徐霁钰 ¹ , 刘彦甫 ¹ , 杨晓飞 ¹ , 张智韬 ¹^,² , 陈俊英 ¹^,²^,^*^,^*

智能化农业装备学报（中英文） | 2026,7(2): 91-101

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智能化农业装备学报（中英文） | 2026 , 7 (2) : 91 -101

基于不同飞行高度无人机多光谱及集成学习的冬小麦叶面积指数反演研究

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刘豪¹, 徐霁钰¹, 刘彦甫¹, 杨晓飞¹, 张智韬¹^,², 陈俊英¹^,²^,^*^,^*

作者信息

^1.西北农林科技大学水利与建筑工程学院，陕西杨凌，712100

^2.西北农林科技大学旱区农业水土工程教育部重点实验室，陕西杨凌，712100

通讯作者:

^* 陈俊英，女，1975年生，陕西咸阳人，博士，教授；研究方向为遥感技术在节水灌溉及水资源中的应用。E-mail：cjyrose@126.com

作者简介:

刘豪，男，2000年生，河北邯郸人，硕士研究生；研究方向为无人机遥感技术应用在农业和水资源中的应用。E-mail：120330@nwafu.edu.cn

Research on inversion of winter wheat leaf area index based on UAV multispectral data at different flight altitudes and integrated learning

Hao Liu¹, Jiyu Xu¹, Yanfu Liu¹, Xiaofei Yang¹, Zhitao Zhang¹^,², Junying Chen¹^,²

Affiliations

^1.School of Water Resources and Civil Engineering, Northwest A&F University, Yangling 712100, China

^2.Key Laboratory of Agricultural Water and Soil Engineering in Arid Areas (Ministry of Education), Northwest A&F University, Yangling 712100, China

Corresponding Author:

^* Chen Junying, E-mail: cjyrose@126.com

Liu Hao, E-mail: 120330@nwafu.edu.cn

出版时间: 2026-05-15 doi: 10.12398/j.issn.2096-7217.2026.02.008

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摘要

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叶面积指数（leaf area index，LAI）是表征冬小麦冠层结构特征与生长状态的关键生物物理参数，在精准农业监测与作物长势评估中具有重要作用。然而，现有反演方法在不同飞行高度下存在精度不稳定、模型泛化能力不足的问题，为提升冬小麦LAI的反演精度与泛化能力，本研究基于多高度无人机多光谱数据结合集成学习模型与多种机器学习方法，构建LAI反演模型。采用ANOVA分析飞行高度对反射率及纹理特征的影响机制，基于多光谱数据提取植被与纹理指数，并通过皮尔逊和斯皮尔曼相关性分析筛选LAI敏感指数用于建模，构建XGBoost、RF、XGBoost-EN和RF-Ridge 4种模型，开展多高度、多特征条件下的LAI反演及精度对比分析。结果表明：无人机飞行高度对反射率与纹理特征存在明显尺度效应，80 m为反射率影响临界值；单特征与混合特征输入下模型在多高度LAI反演中均表现为20 m高度反演精度最高、效果最佳，R²最高达0.745，RMSE最低为0.699，MAE最低为0.507；XGB-EN、RF-Ridge集成模型在LAI反演中的精度均高于其余特征模型，表现出更优的反演效果，R²最高达0.802，RMSE最低为0.592，MAE最低为0.421，具有优异的拟合精度和稳定性。综合来看，XGBoost-EN是LAI反演任务中最具优势的模型。研究验证了多高度下集成学习模型与机器学习方法在LAI高精度反演中的可行性，为遥感驱动的作物生长监测提供了理论基础与技术路径。

关键词

冬小麦 / 叶面积指数 / 无人机 / 多高度 / 集成模型 / LAI反演

Abstract

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The leaf area index (LAI) is a key biophysical parameter characterizing the canopy structure and growth status of winter wheat, playing a crucial role in precision agriculture monitoring and crop condition assessment. However, existing inversion methods suffer from unstable accuracy and insufficient model generalization capabilities across varying flight altitudes. To adress these limitaions, this study develops an LAI estimation framework based on multi-altitude UAV multispectral data by integrating ensemble learning architectures with multiple machine learning algorithms. Analysis of variance (ANOVA) was applied to analyze the mechanisms through which flight altitude influences spectral reflectance and textural features. Following the extraction of vegetation and textural indices from the multispectral data, Pearson and Spearman correlation analyses were conducted to screen LAI-sensitive features for predictive modeling. Four distinct models—XGBoost, RF, XGBoost-EN, and RF-Ridge—were subsequently established to perform multi-altitude and multi-feature LAI inversions and comparative accuracy evaluations. The results indicate that UAV flight altitude exerts a significant scale effect on both reflectance and textural characteristics, with 80 m indentified as the critical threshold for reflectance variations. Regardless of whether single-feature or mixed-feature inputs were utilized, 20 m flight altitude consistently yielded the highest estimation accuracy across all tested altitudes, R² achieving a maximum of 0.745, RMSE of 0.699, and MAE of 0.507. Furthermore, The XGB-EN and RF-Ridge ensemble models outperformed the standalone machine learning models, exhibiting superior inversion performance with an R² of up to 0.802, an RMSE down to 0.592, and an MAE down to 0.421, indicating excellent fitting accuracy and stability. Overall, XGBoost-EN is the optimal model for the LAI inversion task. This study validated the feasibility of ensemble learning models and machine learning methods for high-precision LAI inversion across multiple altitudes, providing a theoretical foundation and technical pathway for remote sensing-driven crop growth monitoring.

Key words

winter wheat / leaf area index / unmanned aerial vehicle / multiple altitudes / ensemble model / LAI inversion

引用本文

刘豪, 徐霁钰, 刘彦甫, 杨晓飞, 张智韬, 陈俊英. 基于不同飞行高度无人机多光谱及集成学习的冬小麦叶面积指数反演研究. 智能化农业装备学报（中英文）, 2026 , 7 (2) : 91 -101 . DOI: 10.12398/j.issn.2096-7217.2026.02.008

Hao Liu, Jiyu Xu, Yanfu Liu, Xiaofei Yang, Zhitao Zhang, Junying Chen. Research on inversion of winter wheat leaf area index based on UAV multispectral data at different flight altitudes and integrated learning[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (2) : 91 -101 . DOI: 10.12398/j.issn.2096-7217.2026.02.008

基金

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国家自然科学基金面上项目(52279047)
陕西省科技厅区域科技创新体系建设项目(2025ZY-QYCXYL-06)

参考文献

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2026年第7卷第2期

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文章信息

doi: 10.12398/j.issn.2096-7217.2026.02.008

接收时间：2026-04-23
首发时间：2026-11-05
出版时间：2026-05-15

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出版历史

收稿日期：2026-04-23
修回日期：2026-05-08

基金

National Natural Science Foundation of China General Program(52279047)

国家自然科学基金面上项目(52279047)

Shaanxi Provincial Department of Science and Technology Project on the Construction of a Regional Science and Technology Innovation System(2025ZY-QYCXYL-06)

陕西省科技厅区域科技创新体系建设项目(2025ZY-QYCXYL-06)

作者信息

^1.西北农林科技大学水利与建筑工程学院，陕西杨凌，712100

^2.西北农林科技大学旱区农业水土工程教育部重点实验室，陕西杨凌，712100

通讯作者:

^* 陈俊英，女，1975年生，陕西咸阳人，博士，教授；研究方向为遥感技术在节水灌溉及水资源中的应用。E-mail：cjyrose@126.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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