基于高光谱植被指数的大豆地上部生物量估算模型研究  

Model Analysis of Estimating Soybean Above-ground Biomass by Hyperspectral Vegetation Index

作　　者：龚荣新 鲁向晖[1,2] 张海娜[1,2] 王倩 陈志琪 杨宝城 马露露 GONG Rong-xin;LU Xiang-hui;ZHANG Hai-na;WANG Qian;CHEN Zhi-qi;YANG Bao-cheng;MA Lu-lu(School of Water Conservancy and Ecological Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Jiangxi Camphor Tree Breeding and Development and Utilization Engineering Research Center,Nanchang Institute of Technology,Nanchang 330099,China)

机构地区：[1]南昌工程学院水利与生态工程学院,江西南昌330099 [2]南昌工程学院江西省樟树繁育与开发利用工程研究中心,江西南昌330099

出　　处：《大豆科学》2023年第3期352-359,共8页Soybean Science

基　　金：国家自然科学基金(52269013)。

摘　　要：本研究以大豆为研究对象,利用光谱仪测定大豆鼓粒期冠层高光谱数据并计算多种高光谱植被指数。分别采用一元线性回归(LR)、支持向量机(SVM)、反向传播神经网络(BPNN)和随机森林(RF)建立高光谱植被指数组合和大豆地上部生物量之间相互关系的数学模型。结果显示:基于LR、SVM、BPNN和RF建立的估算AGB模型的决定系数(R^(2))分别为0.59,0.71,0.73和0.76;均方根误差(RMSE)分别为2559.0,481.1,1194.6和805.2 kg·hm^(-2);相对分析误差(RPD)分别为1.22,1.55,1.87和1.92。基于RF建立模型的预测精准度比LR、SVM和BPNN模型更可靠,因此运用RF模型可以更精确地估算大豆地上部生物量。In this study,soybean was taken as the research object,and the hyper-spectral data of soybean canopy at the filling stage were measured by spectrograph and various hyper-spectral vegetation indexes were calculated.We used Linear Regression(LR),Support Vector Machine(SVM),Back Propagation Neural Network(BPNN)and Random Forest(RF)to established the mathematical model of the relationship between hyper-spectral vegetation index combination and soybean above-ground biomass.The results showed that the determination coefficients(R^(2))of estimating AGB model based on LR,SVM,BPNN and RF were 0.59,0.71,0.73 and 0.76 respectively.The Root Mean Square Error(RMSE)was 2559.0,481.1,1194.6 and 805.2 kgha^(-1) respectively.The relative analysis error(RPD)was 1.22,1.55,1.87 and 1.92 respectively.The prediction accuracy of models based on RF was more reliable than LR,SVM and BPNN models.Therefore,the above ground biomass of soybean can be estimated more accurately by using RF models.

关 键 词：大豆 地上部生物量 随机森林 高光谱 植被指数 

分 类 号：S565.1[农业科学—作物学]