智能化农业装备学报（中英文）

基于三维辐射传输模型模拟点云数据的水稻叶面积指数反演方法研究

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吴锐 ¹^,² , 白驹驰 ¹^,² , 李世隆 ¹^,² , 惠尹宣 ¹^,² , 于丰华 ¹^,²^,³^,^*^,^* , 许童羽 ¹^,²^,³

智能化农业装备学报（中英文） | 2026,7(2): 102-111

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智能化农业装备学报（中英文） | 2026 , 7 (2) : 102 -111

基于三维辐射传输模型模拟点云数据的水稻叶面积指数反演方法研究

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吴锐¹^,², 白驹驰¹^,², 李世隆¹^,², 惠尹宣¹^,², 于丰华¹^,²^,³^,^*^,^*, 许童羽¹^,²^,³

作者信息

^1.沈阳农业大学信息与电气工程学院，辽宁沈阳，110866

^2.国家数字农业区域创新分中心（东北），辽宁沈阳，110866

^3.辽宁省智慧农业技术重点实验室，辽宁沈阳，110866

通讯作者:

^* 于丰华，男，1989年生，吉林白山人，博士，教授，博士生导师；研究方向为智慧农业。E-mail：adan@syau.edu.cn

作者简介:

吴锐，女，2003年生，吉林松原人，硕士研究生；研究方向为农业信息化技术。E-mail：WuR@stu.syau.edu.cn

Retrieval of rice leaf area index from point cloud data simulated by a three-dimensional radiative transfer model

Rui Wu¹^,², Juchi Bai¹^,², Shilong Li¹^,², Yinxuan Hui¹^,², Fenghua Yu¹^,²^,³, Tongyu Xu¹^,²^,³

Affiliations

^1.College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

^2.National Digital Agriculture Sub-center of Innovation (Northeast Region), Shenyang 110866, China

^3.Key Laboratory of Intelligent Agriculture in Liaoning Province, Shenyang 110866, China

Corresponding Author:

^* Yu Fenghua, E-mail: adan@syau.edu.cn

Wu Rui, E-mail: WuR@stu.syau.edu.cn

出版时间: 2026-05-15 doi: 10.12398/j.issn.2096-7217.2026.02.009

文章导航

摘要

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利用辐射传输模型模拟光谱数据为水稻叶面积指数（leaf area index，LAI）反演模型提升鲁棒性和可解释性是目前相关研究的重要方向。然而，点云数据作为能够直接反映水稻物理结构的数据，相关的研究和应用却不够成熟。针对这一问题，本研究建立了一种融合三维辐射传输模型与无人机激光雷达技术的水稻LAI反演方法。基于LESS三维辐射传输模型构建虚拟水稻冠层场景，生成模拟点云数据；利用无人机激光雷达采集田间实测数据；通过Pearson相关性分析筛选关键特征，构建包含高度特征、垂直分层密度特征和累积密度特征的特征子集。采用岭回归、支持向量回归和随机森林3种机器学习模型，分别在模拟数据集、实测数据集及“模拟—实测”混合数据集上进行训练与验证。研究结果表明，LESS模型生成的模拟数据与实测点云在垂直分布上具有高度一致性，验证了模拟的有效性。特征分析表明，90%分位数高度H90和植被冠层体积VCM与LAI相关性最强。模型比较显示，随机森林在混合数据集上表现最优，验证集R²=0.918，RMSE=0.787，混合数据集策略较实测数据集拟合效果更好，有效增强模型的泛化能力。综上所述，本研究提出的三维辐射传输模型与无人机激光雷达技术融合方法可以快速准确地获取水稻LAI信息，为水稻长势信息精准监测提供了新的技术途径，为农业低空经济中作物表型信息的高通量获取提供了技术支撑。

关键词

叶面积指数 / 无人机激光雷达 / 三维辐射传输模型 / 机器学习 / 混合数据集

Abstract

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Utilizing radiative transfer models to simulate spectral data for improving the robustness and interpretability of leaf area index (LAI) inversion models is a major focus in current research. However, the application of point cloud data, which can directly reflect the physical structure of rice, remains underdeveloped. To bridge this gap, this study proposes a rice LAI inversion method that integrates a three-dimensional radiative transfer model with unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology. Virtual rice canopy scenes were constructed using the LESS three-dimensional radiative transfer model to generate simulated point cloud data, while UAV LiDAR was employed to collect field-measured data. Pearson correlation analysis was used to select key features, leading to the construction of a feature subset comprising height features, vertical layer density features, and cumulative density features. Three machine learning models-ridge regression, support vector regression, and random forest-were trained and validated on simulated datasets, measured datasets, and a simulation-measurement hybrid dataset. The results show that the simulated data generated by the LESS model demonstrate strong consistency with measured point clouds in vertical distribution, confirming the validity of the simulation. Feature analysis indicates that the 90th percentile height (H90) and vegetation canopy volume (VCM) manifest the strongest correlations with LAI. Model comparison reveals that the random forest model performs best on the hybrid dataset, achieving a validation R² of 0.918 and RMSE of 0.787. The hybrid dataset strategy yields better fitting performance than using the measured dataset alone, effectively enhancing model generalization. In conclusion, the proposed method, which integrats three-dimensional radiative transfer modeling and UAV LiDAR technology, enables rapid and accurate estimation of rice LAI, providing a new technical pathway for precise monitoring of rice growth status and supporting high-throughput acquisition of crop phenotypic information within the framework of agricultural low-altitude economy.

Key words

leaf area index / UAV LiDAR / three-dimensional radiative transfer model / machine learning / hybrid dataset

引用本文

吴锐, 白驹驰, 李世隆, 惠尹宣, 于丰华, 许童羽. 基于三维辐射传输模型模拟点云数据的水稻叶面积指数反演方法研究. 智能化农业装备学报（中英文）, 2026 , 7 (2) : 102 -111 . DOI: 10.12398/j.issn.2096-7217.2026.02.009

Rui Wu, Juchi Bai, Shilong Li, Yinxuan Hui, Fenghua Yu, Tongyu Xu. Retrieval of rice leaf area index from point cloud data simulated by a three-dimensional radiative transfer model[J]. Journal of Intelligent Agricultural Mechanization, 2026 , 7 (2) : 102 -111 . DOI: 10.12398/j.issn.2096-7217.2026.02.009

基金

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国家自然科学基金(32572182)
辽宁省“兴辽英才计划”项目(XLYC2203005)

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.2096-7217.2026.02.009

接收时间：2026-04-01
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：2026-04-01
修回日期：2026-04-24

基金

National Natural Science Foundation of China(32572182)

国家自然科学基金(32572182)

LiaoNing Revitalization Talents Program(XLYC2203005)

辽宁省“兴辽英才计划”项目(XLYC2203005)

作者信息

^1.沈阳农业大学信息与电气工程学院，辽宁沈阳，110866

^2.国家数字农业区域创新分中心（东北），辽宁沈阳，110866

^3.辽宁省智慧农业技术重点实验室，辽宁沈阳，110866

通讯作者:

^* 于丰华，男，1989年生，吉林白山人，博士，教授，博士生导师；研究方向为智慧农业。E-mail：adan@syau.edu.cn

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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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