Winter wheat biomass estimation based on canopy spectra

文献类型： 外文期刊

第一作者： Zheng Ling

作者： Zheng Ling;Zhu Dazhou;Zhang Baohua;Wang Cheng;Zhao Chunjiang;Zheng Ling;Liang Dong

作者机构：

关键词： winter wheat;biomass;canopy spectra;crop growth period;partial least square regression

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2015 年 8 卷 6 期

页码：

收录情况： SCI

摘要： The winter wheat aboveground biomass is an important agronomic parameter to estimate the growth status, and evaluate the yield and quality. Spectrum technique provides a nondestructive and fast method for estimating the winter wheat biomass. In order to find the optimum model by analyzing the wheat canopy spectral characteristic during the whole growth period, field trails were conducted at the National Demonstration Base of Precision Agriculture in Beijing Xiaotangshan town. A portable spectrometer (200-1100 nm) was used to collect the wheat canopy spectra of different varieties at the different growth stages (green stage, jointing stage, booting stage, heading stage and filling stage), clipping the winter wheat at ground level at the same time. Regression and correlation analysis were used to establish the winter wheat biomass estimation models in this study. The results showed that the biggest different bands of the winter wheat canopy spectral reflection curves mainly lied along the blue and near-infrared bands. The spectral reflectance at 678 nm in the visible light range had the best correlation with the biomass (correlation=0.724). The monadic regression analysis, the multiple regression analysis and the partial least squares regression analysis were applied to establish the biomass estimation models, among which the partial least squares regression (PLS) model had higher modeling precision. The R-2 of the calibration and validation were 0.916 and 0.911, respectively. The root-mean-square error (RMSE) of the calibration and validation were 0.090 kg and 0.094 kg (Sample area 50 cmx60 cm). The results indicated that the PLS model (400-1000 nm) could fully estimate the aboveground biomass in the whole growth period of wheat with a better measurement accuracy.

分类号：

  • 相关文献

[1]Band Depth Analysis and Partial Least Square Regression Based Winter Wheat Biomass Estimation Using Hyperspectral Measurements. Fu Yuan-yuan,Wang Ji-hua,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan. 2013

[2]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

[3]Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology. Zheng Ling,Dong Da-ming,Zhang Bao-hua,Wang Cheng,Zhao Chun-jiang,Zheng Ling,Zhu Da-zhou. 2016

[4]Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data. Jin, Xiuliang,Yang, Guijun,Xu, Xingang,Yang, Hao,Feng, Haikuan,Li, Zhenhai,Shen, Jiaxiao,Zhao, Chunjiang,Jin, Xiuliang,Yang, Guijun,Xu, Xingang,Yang, Hao,Feng, Haikuan,Li, Zhenhai,Shen, Jiaxiao,Zhao, Chunjiang,Jin, Xiuliang,Yang, Guijun,Zhao, Chunjiang,Xu, Xingang,Zhao, Chunjiang,Lan, Yubin. 2015

[5]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

[6]The Study of Winter Wheat Biomass Estimation Model Based on Hyperspectral Remote Sensing. Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Meng, Lumin. 2016

[7]Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data. Jin, Xiuliang,Kumar, Lalit,Li, Zhenhai,Xu, Xingang,Yang, Guijun,Li, Zhenhai,Xu, Xingang,Yang, Guijun,Wang, Jihua. 2016

[8]ESTIMATING WINTER WHEAT BIOMASS AND NITROGEN STATUS USING AN ACTIVE CROP SENSOR. Li, Fei,Miao, Yuxin,Chen, Xinping,Bareth, Georg,Li, Fei,Zhang, Hailin,Jia, Liangliang,Bareth, Georg. 2010

[9]Regional yield prediction for winter wheat based on crop biomass estimation using multi-source data. Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun,Ren, Jianqiang,Chen, Zhongxin,Zhou, Qingbo,Tang, Huajun,Li, Su. 2007

[10]Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis. Zhang Jing-cheng,Yuan Lin,Wang Ji-hua,Huang Wen-jiang,Chen Li-ping,Zhang Dong-yan,Zhang Jing-cheng,Yuan Lin,Wang Ji-hua,Zhang Dong-yan,Huang Wen-jiang. 2012

[11]Determination of Cr, Zn, As and Pb in Soil by X-Ray Fluorescence Spectrometry Based on a Partial Least Square Regression Model. Lu, Anxiang,Wang, Jihua,Pan, Ligang,Lu, Anxiang,Qin, Xiangyang,Wang, Jihua,Zhu, Dazhou,Sun, Jiang. 2011

[12]ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL. Liao, Qinhong,Zhao, Chunjiang,Yang, Guijun,Wang, Jihua,Zhang, Dongyan,Liao, Qinhong,Zhang, Dongyan,Coburn, Craig,Wang, Zhijie. 2013

[13]Determination of Soil Parameters in Apple-Growing Regions by Near- and Mid-Infrared Spectroscopy. Dong Yi-Wei,Xu Chun-Ying,Li Yu-Zhong,Li Qiao-Zhen,Dong Yi-Wei,Yang Shi-Qi,Fan Zhong-Nan,Wang Ya-Nan,Bai Wei. 2011

[14]A Cotton BURP Domain Protein Interacts With -Expansin and Their Co-Expression Promotes Plant Growth and Fruit Production. Bing Xu,Jin-Ying Gou,Fu-Guang Li,Xiao-Xia Shangguan,Bo Zhao,Chang-Qing Yang,LingJian Wang,Sheng Yuan,Chang-Jun Liu,Xiao-Ya Chen. 2013

[15]Models of Dry Matter Production and Yield Formation for the Protected Tomato. Chen, Yuli,Zhang, Zhiyou,Zhu, Yan,Chen, Yuli,Zhang, Zhiyou,Liu, Yan,Cao, Hongxin. 2012

[16]Switchgrass SBP-box transcription factors PvSPL1 and 2 function redundantly to initiate side tillers and affect biomass yield of energy crop. Wu, Zhenying,Cao, Yingping,Yang, Ruijuan,Qi, Tianxiong,Hang, Yuqing,Zhou, Gongke,Fu, Chunxiang,Wu, Zhenying,Cao, Yingping,Yang, Ruijuan,Qi, Tianxiong,Hang, Yuqing,Zhou, Gongke,Fu, Chunxiang,Lin, Hao,Wang, Zeng-Yu. 2016

[17]Production of Adipic Acid from Sugar Beet Residue by Combined Biological and Chemical Catalysis. Zhang, Hongfang,Su, Xiaoyun,Ang, Ee Lui,Zhao, Huimin,Li, Xiukai,Zhang, Yugen,Su, Xiaoyun,Zhao, Huimin,Zhao, Huimin,Zhao, Huimin. 2016

[18]Amounts of Stubbles Left in Paddy Fields: Evaluation from the Viewpoints of C Sequestration and Soil Fertility. Liu, Jun-Jie,Wang, Guang-hua,Zou, Ping,Fu, Jian-rong,Ando, Ho,Kimura, Makoto. 2017

[19]Optimal Concentration of Zinc Sulfate in Foliar Spray to Alleviate Salinity Stress in Glycine soja. Jiang, W.,Xu, H. L.,Lu, H. F.,Jiang, W.,Sun, X. H.,Mantri, N.. 2014

[20]Zinc application alleviates the adverse effects of lead stress more in female Morus alba than in males. Dong, Tingfa,Liao, Yongmei,Xu, Xiao,Qin, Fang,Dong, Tingfa,Liu, Gang,Huang, Gaiqun,Xu, Xiao. 2018

作者其他论文 更多>>

微信小程序