苏伟,郭皓,赵冬玲,刘婷,张明政.基于优化PROSAIL叶倾角分布函数的玉米LAI反演方法[J].农业机械学报,2016,47(3):234-241.
Su Wei,Guo Hao,Zhao Dongling,Liu Ting,Zhang Mingzheng.Leaf Area Index Retrivel for Maize Canopy Using Optimized Leaf Angle Distribution Function of PROSAIL Model[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):234-241.
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基于优化PROSAIL叶倾角分布函数的玉米LAI反演方法   [下载全文]
Leaf Area Index Retrivel for Maize Canopy Using Optimized Leaf Angle Distribution Function of PROSAIL Model   [Download Pdf][in English]
投稿时间:2015-09-22  
DOI:10.6041/j.issn.1000-1298.2016.03.033
中文关键词:  玉米  PROSAIL模型  叶倾角分布函数  叶面积指数  地基激光雷达  高分一号
基金项目:国家自然科学基金项目(41371327)和北京高等学校青年英才计划项目(YETP0316)
作者单位
苏伟 中国农业大学 
郭皓 中国农业大学 
赵冬玲 中国农业大学 
刘婷 中国农业大学 
张明政 中国农业大学
山东农业大学 
中文摘要:叶面积指数(LAI)是描述玉米冠层结构的重要参数之一,PROSAIL模型是常用于反演LAI的机理模型,能较为真实地反演植被冠层真实情况,但PROSAIL模型中使用的叶倾角分布函数假定区域内所有作物叶倾角分布是相同的,不能反映玉米植株真实的叶倾角分布情况。本研究以高分一号遥感影像和地基激光雷达点云数据作为数据源,充分利用地基激光雷达(TLS)在获取植被结构参数上的优势,通过体素化的方法对玉米叶片回波点云进行分割,获取每个拟合叶片单元的叶倾角,进而得到玉米植株真实的叶倾角分布,结合椭球分布函数得到玉米精确的叶倾角分布函数,实现对PROSAIL模型中叶倾角分布函数的优化。研究过程中分别基于未改进的PROSAIL模型和经过TLS优化后的PROSAIL模型反演黑龙江825农场主要玉米种植区的LAI。LAI反演结果表明:2种反演方法得到的LAI与实测LAI都具有较好的相关性,决定系数R2分别为0.5576和0.8583,模型可信度较高;但基于PROSAIL模型反演所得LAI结果偏低,在利用TLS数据提取叶倾角对模型进行优化后,反演LAI的估算精度由26.53%提高到96.23%。由此可知,通过引入TLS点云数据改进农作物叶倾角分布函数能大幅度提高LAI反演的准确性。
Su Wei  Guo Hao  Zhao Dongling  Liu Ting  Zhang Mingzheng
China Agricultural University;Shandong Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University;Shandong Agricultural University
Key Words:maize  PROSAIL model  leaf angle distribution function  LAI  TLS  GF-1
Abstract:Leaf area index (LAI) is one of the important parameters to describe the corn canopy structure. PROSAIL model is a mechanism model for retriving LAI, which can express canopy situation more truly. But the leaf angle distribution function used in PROSAIL model assumed that the leaf angle distribution is constant during whole crop growth period, and it cant reflect the actual leaf angle distribution of the corn plant. This paper studied the extracting method of the maize LAI based on the PROSAIL model, using GF-1 images and terrestrial LiDAR data. In order to get the leaf angle distribution of maize, the point cloud of maize was separated into small leaf units through voxel method, and then the surface was matched according to the point cloud in each voxel. The accurate leaf angle distribution function was acquired from statistics data of each leaf unit angle. Combining with the ellipsoid distribution function, the accurate leaf angle distribution function was got, which is used to optimize the PROSAIL model. In this research, the maize canopy LAI was retrived in Farm No.852, Heilongjiang Province, through traditional PROSAIL model and optimized PROSAIL model respectively. The main conclusion is as follows: all of the two methods of inversion of LAI have a good correlation with measured LAI as coefficient is 0.5576 and 0.8583 respectively, which proved that this model is credibly. But the result of inversed LAI based on unimproved model is low. After optimized model with TLS data, the inversion of LAI estimation accuracy was improved from 26.53% to 96.23%.Therefore, it can greatly improve the accuracy of LAI inversion by introducing the TLS point cloud data to improve crops leaf angle distribution function in PROSAIL model.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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