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