A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

文献类型： 外文期刊

第一作者： Yue, Jibo

作者： Yue, Jibo;Feng, Haikuan;Yang, Guijun;Li, Zhenhai;Yue, Jibo;Yue, Jibo;Yang, Guijun;Li, Zhenhai;Feng, Haikuan;Yang, Guijun;Li, Zhenhai

作者机构：

关键词： regression techniques;biomass;vegetation indexes;sampling methods;noise immunity;biomass estimation model;hyperspectral;multi-collinearity

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： 2018 年 10 卷 1 期

页码：

收录情况： SCI

摘要： Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R-2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models.

分类号：

  • 相关文献

[1]Evaluating Multispectral and Hyperspectral Satellite Remote Sensing Data for Estimating Winter Wheat Growth Parameters at Regional Scale in the North China Plain. Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Koppe, Wolfgang,Chen, Xinping,Zhang, Fusuo,Li, Fei,Miao, Yuxin,Miao, Yuxin. 2010

[2]Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Bareth, Georg,Lenz-Wiedemann, Victoria I. S.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Li, Fei,Lenz-Wiedemann, Victoria I. S.,Chen, Xinping,Gnyp, Martin L.,Li, Fei,Miao, Yuxin,Jia, Liangliang,Koppe, Wolfgang,Hennig, Simon D.,Jia, Liangliang,Chen, Xinping,Zhang, Fusuo,Laudien, Rainer. 2014

[3]Monitoring biomass of water hyacinth by using hyperspectral remote sensing. Wang, Jingjing,Sun, Ling,Liu, Huazhou. 2012

[4]Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices. Xie, Qiaoyun,Huang, Wenjiang,Zhang, Bing,Dong, Yingying,Xie, Qiaoyun,Chen, Pengfei,Song, Xiaoyu,Pascucci, Simone,Pignatti, Stefano,Laneve, Giovanni. 2016

[5]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

[6]MONITORING AVAILABLE PHOSPHORUS CONTENT IN SOIL OF CULTIVATED LAND BASED ON HYPERSPECTRAL TECHNOLOGY. Gu, Xiaohe,Wang, Lei,Wang, Lizhi,Fan, Youbo,Yang, Hao,Long, Huiling. 2016

[7]Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis. Wang Hui-fang,Huo Zhi-guo,Wang Hui-fang,Wang Ji-hua,Dong Ying-ying,Gu Xiao-he. 2014

[8]The inversion model of soil organic matter of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2015

[9]Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity Based on Leaf Level Spectral Analysis. Yuan Lin,Zhang Jing-cheng,Zhao Jin-ling,Wang Ji-hua,Yuan Lin,Zhang Jing-cheng,Wang Ji-hua,Huang Wen-jiang. 2013

[10]A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology. Wang Chao,Feng Mei-chen,Yang Wu-de,Xiao Lu-jie,Li Guang-xin,Zhao Jia-jia,Ren Peng,Li Guang-xin. 2015

[11]Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Song Xiao-yu,Yang Gui-jun. 2014

[12]Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Zhang, Jingcheng,Huang, Wenjiang,Yuan, Lin,Luo, Juhua,Wang, Jihua,Zhang, Jingcheng,Pu, Ruiliang,Zhang, Jingcheng,Yuan, Lin,Wang, Jihua,Huang, Wenjiang. 2012

[13]Monitoring total nitrogen content in soil of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Lizhi,Zhang, Liyan,Yang, Guijun. 2017

[14]Stitching of hyper-spectral UAV images based on feature bands selection. Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Xia, L.,Zhang, R. R.,Chen, L. P.,Jiang, H. J.,Zhao, F.. 2016

[15]Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat. Yuan, Lin,Zhang, Jing-Cheng,Wang, Ke,Wang, Ji-Hua,Yuan, Lin,Zhang, Jing-Cheng,Wang, Ji-Hua,Zhao, Jin-Ling,Loraamm, Rebecca-W.,Huang, Wen-Jiang.

[16]Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation. Li, Zhenhai,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Li, Zhenhai,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Li, Zhenhai,Wang, Jihua,Wang, Jihua,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Xu, Xingang,Zhao, Chunjiang,Yang, Guijun,Feng, Haikuan,Jin, Xiuliang. 2015

[17]Estimating the crop leaf area index using hyperspectral remote sensing. Liu Ke,Zhou Qing-bo,Wu Wen-bin,Tang Hua-jun,Liu Ke,Zhou Qing-bo,Wu Wen-bin,Tang Hua-jun,Wu Wen-bin,Xia Tian.

[18]The influence of soil particle sizes on hyperspectral prediction of soil organic matter content. Yao, Yanmin,Si, Haiqing,Wang, Deying,Huang, Qing,Chen, Zhongxin,Liu, Ying. 2015

[19]Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance. Zhu, Linhai,Wang, Jianjian,Jiang, Lianhe,Zheng, Yuanrun,Zhu, Linhai,Chen, Zhongxin,Wang, Jianjian,Ding, Jinzhi,Ding, Jinzhi,Yu, Yunjiang,Li, Junsheng,Xiao, Nengwen,Rimmington, Glyn M..

[20]Study on non-destructive estimation of nitrogen concentration of water hyacinth by hyperspectral data. Wang, Jingjing,Sun, Ling,Liu, Huazhou,Wang, Jingjing. 2016

作者其他论文 更多>>

微信小程序