基于叶片双层辐射传输机理的水稻叶绿素含量反演  

Inversing chlorophyll contents in rice using radiation transport of leaf bilayer

作　　者：王楠 陈春玲[1,2,3] 相爽 金忠煜 白驹驰 于丰华 WANG Nan;CHEN Chunling;XIANG Shuang;JIN Zhongyu;BAI Juchi;YU Fenghua(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;National Digital Agriculture Sub-center of Innovation(Northeast Region),Shenyang 110866,China;Key Laboratory of Intelligent Agriculture in Liaoning Province,Shenyang 110866,China)

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110866 [2]国家数字农业区域创新分中心(东北),沈阳110866 [3]辽宁省智慧农业技术重点实验室,沈阳110866

出　　处：《农业工程学报》2024年第17期171-178,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：辽宁省自然科学基金面上项目(2023-MSLH-283);国家自然科学基金青年项目(32201652);辽宁省“兴辽英才计划”项目(XLYC2203005)。

摘　　要：水稻是主要的粮食作物,对其生长发育过程中叶绿素含量进行精准监测,在指导田间管理方面具有十分重要的意义。叶片辐射传输模型能够有效地模拟水稻叶片光谱信息,描述叶片各参数对光谱反射率的影响,具有较强的机理性,可作为基于物理驱动方式反演水稻叶片叶绿素含量的重要机理模型。PIOSL(PROSPECT consider the internal optical structure of the leaves)模型假设叶片内部是由两层不同的光学特性层叠加而成,其叶片内部结构的假设更加符合植物的实际生长状况。为了验证PIOSL模型反演水稻叶片叶绿素的可行性,并为作物理化参量反演提供新思路,该研究利用此模型对水稻叶片叶绿素含量开展反演研究。首先利用PIOSL模型构建查找表,筛选查找表中与实测光谱较为接近的模拟样本数据,利用SVM(support vector machine)构建分类预测模型,判定查找表中随机生成的参数组合是否符合叶片实际情况,并构建新的查找表数据集。将改进后的查找表按7:3的比例随机拆分为训练集和测试集,通过WOAELM(whale optimization algorithm,WOA;extreme learning machine,ELM)模型反演水稻叶片叶绿素含量。结果表明:基于PIOSL-WOA-ELM构建的反演模型,模型R2和RMSE分别为0.977和2.356μg/cm^(2),与PROSPECT-WOA-ELM模型的反演精度均在0.9以上,且优于传统的多元回归模型。由此看来,利用PIOSL-WOA-ELM模型对水稻叶片叶绿素含量进行反演是可行的,可为精准反演水稻叶绿素在叶片中的分布提供新的思路,进而科学有效地开展田间管理。Rice is one of the major grain crops in the world.It is of great significance to accurately inverse the chlorophyll content during growth and development in the field.However,there are some deficiencies using conventional interpretability that are driven by data inversion of the physical and chemical parameters.The leaf radiative transfer model can be expected to effectively simulate the spectral information of rice leaves,and then describe the effects of leaf parameters on spectral reflectance.A strong mechanism model can be used for the inversion of chlorophyll content of rice leaves,according to the physically driven method.The PIOSL(PROSPECT considering the internal optical structure of the leaves)model can be utilized to assume that the internal structure of the leaf blade is composed of the superposition of two layers of optical properties,which is much more in line with the actual growth of the plant.This study aims to verify the feasibility of retrieving rice leaf chlorophyll content by the PIOSL model.Firstly,the sensitivity analysis was carried out on each input parameter into the PIOSL model.The parameters Cab and Cab12 were determined as the high sensitivity in the 400-750 nm band.The inversion of chlorophyll content(Cab)was then implemented.The projection(SPA)was used to extract the features of spectral data.Five characteristic bands were sensitive to chlorophyll:490,575,645,675,and 725 nm.A multiple regression model was constructed to realize the inversion of rice leaf chlorophyll.The inversion performance of the mechanism model was then evaluated using the PIOSL model.The chlorophyll content of rice leaves was also inverted to construct an initial lookup table.LSE(least squares estimate)was used to screen the simulated sample data in the initial lookup table closer to the measured spectra.A classification prediction model was constructed using a support vector machine(SVM),in order to determine whether the combination of the screened sample parameters was in line with the actual growth of the leave

关 键 词：高光谱 模型 PIOSL 叶绿素含量 WOA-ELM SVM 

分 类 号：S24[农业科学—农业电气化与自动化]