[1]郑智康,常庆瑞,符欣彤,等.基于变换光谱与光谱指数的夏玉米叶片含水率高光谱估算[J].江苏农业学报,2023,(09):1883-1890.[doi:doi:10.3969/j.issn.1000-4440.2023.09.010]
　ZHENG Zhi-kang,CHANG Qing-rui,FU Xin-tong,et al.Hyperspectral estimation of leaf water content of summer maize based on transformed spectrum and spectral index[J].,2023,(09):1883-1890.[doi:doi:10.3969/j.issn.1000-4440.2023.09.010]
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基于变换光谱与光谱指数的夏玉米叶片含水率高光谱估算()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年09期
页码:
1883-1890
栏目:
农业信息工程
出版日期:
2023-12-31

文章信息/Info

Title:
Hyperspectral estimation of leaf water content of summer maize based on transformed spectrum and spectral index
作者:
郑智康; 常庆瑞; 符欣彤; 张子娟; 李铠; 姜时雨; 宋子怡
(西北农林科技大学资源环境学院,陕西杨凌712100)
Author(s):
ZHENG Zhi-kang; CHANG Qing-rui; FU Xin-tong; ZHANG Zi-juan; LI Kai; JIANG Shi-yu; SONG Zi-yi
(College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China)
关键词:
夏玉米; 叶片含水率; 高光谱; 光谱指数
Keywords:
summer maize; leaf water content; hyperspectral; spectral index
分类号:
S127
DOI:
doi:10.3969/j.issn.1000-4440.2023.09.010
文献标志码:
A
摘要:
为实现陕西关中地区夏玉米叶片含水率遥感估算,本研究通过夏玉米叶片高光谱反射率和含水率的测定,利用原始光谱及转换光谱,构建任意两波段的光谱指数,分析光谱指数与叶片含水率之间的关系,构建玉米叶片含水率估算的单因素回归模型和基于支持向量回归算法(SVR)、反向传播神经网络回归算法(BPNN)和麻雀搜索随机森林回归算法(SSA-RFR)的多因素模型,并根据模型精度筛选玉米叶片含水率估算的优化模型。结果表明,随叶片含水率的增加,短波红外波段的光谱反射率降低,最优光谱指数的构成波段主要位于短波红外波段,其中基于一阶导数光谱的比值光谱指数 (R1 563/R1 406)和归一化光谱指数[(R1 563-R1 406)/(R1 563+R1 406)]与叶片含水率相关性最佳,其相关系数绝对值均达0.83；多因素回归模型的模拟效果优于单因素回归模型,基于麻雀搜索随机森林回归模型的精度最高,验证集决定系数(R2)为0.78,均方根误差(RMSE)和相对误差(RE)分别为1.14%和1.09%。本研究通过分析玉米叶片含水率与高光谱反射率之间的关系,建立遥感估算模型,为关中地区夏玉米生产水分管理提供依据。
Abstract:
In order to realize the remote sensing estimation of summer maize leaf moisture content in Guanzhong area of Shaanxi province, this study constructed the spectral index of any two bands by measuring the hyperspectral reflectance and moisture content of summer maize leaves, using the original spectrum and conversion spectrum, and analyzed the relationship between spectral index and leaf moisture content. The single factor regression model of maize leaf moisture content estimation and the multivariate estimation models based on support vector regression algorithm (SVR), back propagation neural network regression algorithm (BPNN) and sparrow search algorithm-random forest regression algorithm (SSA-RFR) were constructed, and the accuracy of the model was compared. The results showed that the spectral reflectance decreased with the increase of leaf moisture content in shortwave infrared band, and the band of the optimal spectral index was mainly located in the shortwave infrared band. The ratio spectral index (R1 563/R1 406) and normalized spectral index [(R1 563-R1 406)/(R1 563+R1 406)] based on the first derivative spectrum had the best correlation with leaf moisture content, and the absolute value of the correlation coefficient was 0.83. The simulation effect of multi-factor regression model was better than that of single-factor regression model. The accuracy of the model based on SSA-RFR was the highest. The validation set determination coefficient (R2) was 0.78, and the root mean square error (RMSE) and relative error (RE) were 1.14% and 1.09%, respectively. In this study, by analyzing the relationship between maize leaf moisture content and hyperspectral reflectance, a remote sensing estimation model was established to provide a basis for water management in summer maize production in Guanzhong area.

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备注/Memo

备注/Memo:
收稿日期:2022-10-18基金项目:国家高技术研究发展计划(“863”计划)资助项目(2013AA102401)作者简介:郑智康(1997-),男,安徽安庆人,硕士研究生,主要从事土地资源与空间信息技术研究。(E-mail)15129257078@163.com通讯作者:常庆瑞,(E-mail) changqr@nwsuaf.edu.cn
更新日期/Last Update: 2024-01-15