Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass
DU Wen-yong1, ZHANG Lu-da2, HU Zhen-fang1, Shamaila Z3, ZENG Ai-jun2, SONG Jian-li2, LIU Ya-jia2, Wolfram S3, Joachim M3, HE Xiong-kui1,2*
1. College of Engineering, China Agricultural University, Beijing 100193, China 2. College of Science, China Agricultural University, Beijing 100183，China 3. Institute of Agricultural Engineering (440) University Hohenheim, Stuttgart 70599, Germany
Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass
DU Wen-yong1, ZHANG Lu-da2, HU Zhen-fang1, Shamaila Z3, ZENG Ai-jun2, SONG Jian-li2, LIU Ya-jia2, Wolfram S3, Joachim M3, HE Xiong-kui1,2*
1. College of Engineering, China Agricultural University, Beijing 100193, China 2. College of Science, China Agricultural University, Beijing 100183，China 3. Institute of Agricultural Engineering (440) University Hohenheim, Stuttgart 70599, Germany
摘要： The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation (drip irrigation, sprinkler irrigation, flood irrigation). It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass. The temperature of crop and background was measured by thermal infrared image. It is necessary to get the crop background separation index (CBSIL，CBSIH), which can be used for distinguishing the crop value from the image. CBSIL and CBSIH (the temperature when the leaves are wet adequately; the temperature when the stomata of leaf is closed completely) are the threshold values. The temperature of crop ranged from CBSIL to CBSIH. Then the ICWSI was calculated based on relevant theoretical method. The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI. In order to construct the high accuracy simulation model, the samples were divided into two parts. One was used for constructing the simulation model, the other for checking the accuracy of the model. Such result of the model was concluded as: (1) As for the simulation model of soil moisture, the correlation coefficient (R2) is larger than 0.887 6, the average of relative error (Er) ranges from 13.33% to 16.88%; (2) As for the simulation model of winter wheat yield, drip irrigation (0.887 6, 16.89%, -0.12), sprinkler irrigation (0.970 0, 14.85%, -0.12), flood irrigation (0.969 0, 18.87%, -0.18), with the values of R2, Er and CRM listed in the parentheses followed by the individual term. (3) As for winter wheat biomass, drip irrigation (0.980 0, 13.70%, -0.13), sprinkler irrigation (0.95, 13.15%, -0.14), flood irrigation (0.970 0, 14.48%, -0.13), and the values in the parentheses are demonstrated the same as above. Both the CRM and Er are shown to be very low values, which points to the accuracy and reliability of the model investigated. The accuracy of model is high and reliable. The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.
关键词：Thermal infrared image;Infrared index;ICWSI;Technology of irrigation
Abstract：The present paper utilizes thermal infrared image for inversion of winter wheat yield and biomass with different technology of irrigation (drip irrigation, sprinkler irrigation, flood irrigation). It is the first time that thermal infrared image is used for predicting the winter wheat yield and biomass. The temperature of crop and background was measured by thermal infrared image. It is necessary to get the crop background separation index (CBSIL，CBSIH), which can be used for distinguishing the crop value from the image. CBSIL and CBSIH (the temperature when the leaves are wet adequately; the temperature when the stomata of leaf is closed completely) are the threshold values. The temperature of crop ranged from CBSIL to CBSIH. Then the ICWSI was calculated based on relevant theoretical method. The value of stomata leaf has strong negative correlation with ICWSI proving the reliable value of ICWSI. In order to construct the high accuracy simulation model, the samples were divided into two parts. One was used for constructing the simulation model, the other for checking the accuracy of the model. Such result of the model was concluded as: (1) As for the simulation model of soil moisture, the correlation coefficient (R2) is larger than 0.887 6, the average of relative error (Er) ranges from 13.33% to 16.88%; (2) As for the simulation model of winter wheat yield, drip irrigation (0.887 6, 16.89%, -0.12), sprinkler irrigation (0.970 0, 14.85%, -0.12), flood irrigation (0.969 0, 18.87%, -0.18), with the values of R2, Er and CRM listed in the parentheses followed by the individual term. (3) As for winter wheat biomass, drip irrigation (0.980 0, 13.70%, -0.13), sprinkler irrigation (0.95, 13.15%, -0.14), flood irrigation (0.970 0, 14.48%, -0.13), and the values in the parentheses are demonstrated the same as above. Both the CRM and Er are shown to be very low values, which points to the accuracy and reliability of the model investigated. The accuracy of model is high and reliable. The results indicated that thermal infrared image can be used potentially for inversion of winter wheat yield and biomass.
Key words：Thermal infrared image;Infrared index;ICWSI;Technology of irrigation
通讯作者:
HE Xiong-kui
E-mail: xiongkui@cau.edu.cnIntroduction
引用本文:
DU Wen-yong1, ZHANG Lu-da2, HU Zhen-fang1, Shamaila Z3, ZENG Ai-jun2, SONG Jian-li2, LIU Ya-jia2, Wolfram S3, Joachim M3, HE Xiong-kui1,2* . Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass[J]. 光谱学与光谱分析, 2011, 31(06): 1476-1480.
DU Wen-yong1, ZHANG Lu-da2, HU Zhen-fang1, Shamaila Z3, ZENG Ai-jun2, SONG Jian-li2, LIU Ya-jia2, Wolfram S3, Joachim M3, HE Xiong-kui1,2* . Utilization of Thermal Infrared Image for Inversion of Winter Wheat Yield and Biomass. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(06): 1476-1480.
[1] Fan Yongshen, Huang Xiuqiao, Xu Feng, et al. Transactions of the CSAM，2009, 40(11): 74. [2] Zhang Zhenhua, Cai Huanjie，Yang Runya. Transactions of the CSAE，2006, 22(3): 84. [3] Suarez-Rey E, Choi C Y, Waller P M, et al. Transactions of the ASAE, 2000, 43(2): 631. [4] Li Jiusheng，Rao Minjie. Transactions of the CSAE, 1999, 15(4): 78. [5] Wang Hongyuan，Li Yongguang. Transactions of the CSAM，2010, 41(05): 47. [6] Li Jiusheng，Rao Minjie. Transactions of the CSAE, 2000, 16(6): 38. [7] Chen Shuren, Xiao Weizhong, Zhu Renkai, et al. Transactions of the CSAM, 2008, 39(10): 140. [8] Yang Shifeng, Qian Dongping, Huo Xiaojing, et al. Transactions of the CSAE, 2001, 17(5): 150. [9] WANG Tao, ZHANG Lu-da, LAO Cai-lian, et al. Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(10): 1915. [10] Idso S B, Jackson R D, Pinter P J, et al. Agricultural Meteorlogy, 1981, 24: 45. [11] Sherwood B Idso. Agricultural Meteorlogy, 1982, 27: 59. [12] Ilkka Lenonen, Hamlyn G Jones. Journal of Experimental Botany, 2004, 55: 1423. [13] Hamlyn G Jones, Manfred Stoll, Tiago Santos, et al. Journal of Experimental Botany, 2002, 53: 2249. [14] Moller M，Alchanatis V, Cohen Y, et al. Journal of Experimental Botany, 2007, 58: 823. [15] Leinonen I, Grant O M, Tagliavia C P P, et al. Plant, Cell and Environment, 2006, 29: 1508. [16] Hamlyn G Jones. Agricultural and Forest Meteorlogy, 1999, 95: 139. [17] Jackson R D, Idso S B, Reginato R J. Water Resour Res., 1999, 17: 1133. [18] Suat Irmak, Dorota Z Haman, Ruhi Bastug. Agronomy Journal, 2000, 92: 1221. [19] Abdul-Jabbar A S, Lugg D G, Sammis T W, et al. Trans. ASAE, 1985, 28: 454. [20] Tubaileh A S, Sammis J W, Lugg D G. Agricultural Water Management. 1986, 12: 75.
