沈阳农业大学学报2025，Vol.56Issue(6)：1-9,9.DOI:10.3969/j.issn.1000-1700.2025.06.001
基于植被二向性反射统一模型的水稻LAI反演方法研究
Retrieval Method of Rice LAI Based on Unified Model of Vegetation Bidirectional Reflectance
摘要
Abstract
[Objective]Leaf Area Index(LAI)is a core indicator for crop growth assessment and plays an irreplaceable key role in precise field management decision-making.In order to break through the limitations of traditional empirical models,this study optimized model parameters according to the differences in canopy structure of rice at different growth stages,reduced the impacts of factors such as soil background,leaf overlap,improved the accuracy and efficiency of retrieval,to achieve rapid conversion from remote sensing data to leaf area index.[Methods]This paper proposes a retrieval method of rice LAI based on the unified model of vegetation bidirectional reflection.Using the Haicheng Precision Agriculture Aviation Research Base of Shenyang Agricultural University as the research area,unmanned aerial vehicle hyperspectral data(400-1 000 nm)and ground measured LAI data were collected during the seedling regreening,tillering,jointing,and heading stages of rice in 2023.The Successive Projection Algorithm(SPA)was used to screen characteristic bands for reducing data redundancy.In terms of model construction,the range of sensitive parameters of the model was determined through global sensitivity analysis,and multiple simulated datasets of LAI and canopy reflectance were established.The inversion models were constructed using the lookup table(LUT)method and the lion swarm optimization algorithm(LSO),and compared and verified with traditional methods such as vegetation index method,BP neural network,extreme learning machine(ELM),and random forest(RF).[Results]The SPPA algorithm can effectively characterize the spectral information of rice canopy by selecting feature bands;the error between the simulated rice canopy spectrum based on the unified model of vegetation bidirectional reflectance and the measured spectrum is small in the range of 400-1 000 nm;the LAI inversion based on LSO has the best performance,with a coefficient of determination(R²)of 0.779 and a root mean square error(RMSE)of 0.599,significantly superior to the lookup table method(R²=0.638,RMSE=0.767)and machine learning method(BP neural network R²=0.668,RMSE=0.736;ELM extreme learning machine R²=0.588,RMSE=0.819;RF random forest R²=0.649,RMSE=0.756).[Conclusion]With a clear physical mechanism,the unified model of vegetation bidirectional reflection can effectively overcome the over fitting problems of traditional data driven methods and maintain high stability in different growth periods and under complex soil backgrounds.This study provides a reliable technical solution for dynamic monitoring of rice growth and precise farmland management,which is of great significance for promoting the large-scale application of smart agriculture.关键词
水稻/叶面积指数(LAI)/植被二向性反射统一模型/无人机高光谱/狮群优化算法Key words
rice/leaf area index(LAI)/unified model of vegetation bidirectional reflectance/UAV hyperspectral imaging/lion swarm optimization algorithm分类
农业科技引用本文复制引用
许童羽,刘泓泽,金忠煜,李世隆,穆肖彤,刘美含..基于植被二向性反射统一模型的水稻LAI反演方法研究[J].沈阳农业大学学报,2025,56(6):1-9,9.基金项目
辽宁省教育厅平台项目(JYTPT2024002) （JYTPT2024002）
