Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

文献类型： 外文期刊

第一作者： Yuan, Huanhuan

作者： Yuan, Huanhuan;Yang, Guijun;Wang, Yanjie;Liu, Jiangang;Yu, Haiyang;Feng, Haikuan;Xu, Bo;Zhao, Xiaoqing;Yang, Xiaodong;Yuan, Huanhuan;Li, Changchun;Wang, Yanjie;Yuan, Huanhuan;Yang, Guijun;Liu, Jiangang;Feng, Haikuan;Yang, Xiaodong;Yang, Guijun;Yu, Haiyang;Xu, Bo;Zhao, Xiaoqing;Yang, Xiaodong

作者机构：

关键词： LAI retrieval;hyperspectral remote sensing;sampling method;random forests;artificial neural networks;support vector machine

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2017 年 9 卷 4 期

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR 2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

分类号：

  • 相关文献

[1]Ion mobility spectrometry fingerprints: A rapid detection technology for adulteration of sesame oil. Zhang, Liangxiao,Shuai, Qian,Li, Peiwu,Zhang, Qi,Ma, Fei,Zhang, Wen,Ding, Xiaoxia,Shuai, Qian,Zhang, Qi,Ma, Fei,Li, Peiwu,Zhang, Qi,Ma, Fei,Li, Peiwu,Ding, Xiaoxia,Zhang, Liangxiao,Li, Peiwu,Zhang, Wen,Zhang, Liangxiao. 2016

[2]Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Gong, Daozhi.

[3]Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Li, Wenbin,Ma, Yansong,Liu, Zhangxiong,Guo, Yong,Qiu, Lijuan,Ma, Yansong,Luan, Xiaoyan,Reif, Jochen C.,Jiang, Yong,Wen, Zixiang,Wang, Dechun,Han, Tianfu,Wu, Cunxiang,Sun, Shi,Wei, Shuhong,Wang, Shuming,Yang, Chunming,Wang, Huicai,Yang, Chunming,Zhang, Mengchen,Lu, Weiguo,Xu, Ran,Zhou, Rong,Zhou, Xinan,Wang, Ruizhen,Sun, Zudong,Chen, Huaizhu,Zhang, Wanhai,Sun, Bincheng,Wu, Jian,Han, Dezhi,Yan, Hongrui,Hu, Guohua,Liu, Chunyan,Fu, Yashu,Chen, Weiyuan,Guo, Tai,Zhang, Lei,Yuan, Baojun.

[4]Comparison of artificial intelligence and empirical models for estimation of daily diffuse solar radiation in North China Plain. Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Cui, Ningbo,Zhang, Qingwen,Zhao, Lu,Feng, Yu,Gong, Daozhi.

[5]Identification of Empoasca onukii (Hemiptera: Cicadellidae) and Monitoring of its Populations in the Tea Plantations of South China. Shi, Long-Qing,Zeng, Zhao-Hua,Huang, Huo-Shui,Zhou, Yong-Mei,Vasseur, Liette,You, Min-Sheng,Shi, Long-Qing,Zhou, Yong-Mei,Vasseur, Liette,You, Min-Sheng,Zeng, Zhao-Hua,Huang, Huo-Shui,Vasseur, Liette.

[6]Application of artificial neural network to predict the change of inosine monophosphate for lightly salted silver carp (hypophthalmichthys molitrix) during thermal treatment and storage. Shi, Ce,Cui, Jianyun,Liu, Xiaochang,Zhang, Yuemei,Luo, Yongkang,Shi, Ce,Qin, Na,Luo, Yongkang. 2017

[7]Evaluating soil reinforcement by plant roots using artificial neural networks. Zhang, C.,Jiang, J.,Ma, J.,Ouyang, Q.,Lei, X.,Zhang, X.,Yang, Q..

[8]Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples. Badia-Melis, R.,Ruiz-Garcia, L.,Qian, J. P.,Fan, B. L.,Yang, X. T.,Hoyos-Echevarria, P..

[9]Dynamic monitoring and driving power analysis of LUCC based on remote sensing in Beijing in recent thirty years. Gu, Xiaohe,Guo, Wei,Dong, Yansheng,Wang, Yanchang. 2013

[10]Survey of Support Vector Machine in the Processing of Remote Sensing Image. Li, Su,Wang, Wenchao. 2013

[11]Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Zhang Qing,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Cai Shu-hong,Yang Gui-jun. 2014

[12]Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions. Wen, Xiaohu,Si, Jianhua,He, Zhibin,Yu, Haijiao,Wu, Jun,Shao, Hongbo,Shao, Hongbo. 2015

[13]A New Strategy in Observer Modeling for Greenhouse Cucumber Seedling Growth. Qiu, Quan,Qiao, Xiaojun,Zheng, Chenfei,Wang, Wenping,Yu, Jingquan,Shi, Kai,Bai, He. 2017

[14]Geographic Characterization of Leccinum rugosiceps by Ultraviolet and Infrared Spectral Fusion. Yao, Sen,Liu, Hong-Gao,Li, Jie-Qing,Yao, Sen,Wang, Yuan-Zhong,Li, Tao,Wang, Yuan-Zhong. 2017

[15]Verification and predicting temperature and humidity in a solar greenhouse based on convex bidirectional extreme learning machine algorithm. Zou, Weidong,Yao, Fenxi,Zhang, Baihai,Guan, Zixiao,He, Chaoxing.

[16]Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features. Hasituya,Chen, Zhongxin,Wang, Limin,Wu, Wenbin,Li, He,Jiang, Zhiwei. 2016

[17]DIAGNOSTIC MODEL FOR WHEAT LEAF CONDITIONS USING IMAGE FEATURES AND A SUPPORT VECTOR MACHINE. Du, K.,Sun, Z.,Li, Y.,Zheng, F.,Chu, J.,Su, Y.. 2016

[18]Application of support vector machine for detecting rice diseases using shape and color texture features. Yao, Qing,Guan, Zexin,Zhou, Yingfeng,Tang, Jian,Hu, Yang,Yang, Baojun. 2009

[19]Feature Extraction and Classification of Animal Blood Spectra with Support Vector Machine. Lu Peng-fei,Fan Ya,Zhou Lin-hua,Gao Bin,Qian Jun,Liu Lin-na,Zhao Si-yan,Kong Zhi-feng. 2017

[20]Kharif Dryland Crop Identification Based on Synthetic Aperture Radar in the North China Plain. Dong Zhaoxia,Wang Di,Zhou Qingbo,Chen Zhongxin. 2015

作者其他论文 更多>>

微信小程序