Study on Water Content Estimation Model of Winter Wheat Based on Hyperspectral Characteristic Wavelength

doi:10.6048/j.issn.1001-4330.2019.10.002

Abstract

Abstract: 【Objective】 To establish a rapid and non-destructive canopy moisture content estimation model of winter wheat based on hyperspectral technology and physicochemical data in the hope of providing a theoretical basis for the rapid monitoring of winter wheat. 【Method】 The water content of leaves and plants of two kinds of winter wheat samples were measured, the spectral data were collected for SG smoothing, first-order derivative and second-order derivative processing, and the correlation between the three was analyzed. And finally, multiple estimation models of the water content of leaves and plants of winter wheat were constructed and the accuracy was evaluated. 【Results】 The first-order derivative transformation in different spectral data processing could significantly increase the correlation with the water content of wheat. The water content of leaves reached the maximum negative correlation at the wavelength of 456 nm, and the correlation coefficient was 0.87. The water content of plants reached the maximum negative correlation at the wavelength of 457 nm, and the correlation coefficient was 0.890,9. The fitting accuracy of the water content estimation model constructed by partial least square regression was better than that of the linear and multiple linear regression models. The linear model using the model constructed by R₆₅₀, SG₁₉₄₄, R′₄₅₆ and R″₆₈₁ was better to estimate the water content of leaves, and the accuracy to estimated plant water content of R₆₆₄, SG₆₆₃, R′₄₅₇ and R″₆₈₁was higher. Multiple linear regressions and partial least squares regression were both models constructed by using the first derivative transformation, and both of them had the highest fitting accuracy. The external test R² of the leaf and plant water content estimation models reached 0.803,2, 0.867,0, 0.854,0 and 0.885,6, respectively. 【Conclusion】 The first-order derivative transformation of the original spectrum of wheat can significantly improve the correlation with the moisture content. The estimation model of wheat water content constructed by PLSR method has the highest fitting accuracy, which provides a reference for the non-destructive detection of wheat moisture content by using hyperspectral technology.

Key words: wheat; water content; vegetation index; hyperspectral

摘要： 【目的】以高光谱技术为核心,结合理化数据,建立快速、无损的冬小麦冠层水分含量估算模型,为利用高光谱技术进行小麦水分含量的无损检测提供参考。【方法】测定两种冬小麦的叶片、植株含水量,采集其光谱数据作SG平滑、一阶导数和二阶导数处理,分析其相关关系,构建冬小麦叶片和植株含水量的多种估算模型,进行精度评价。【结果】不同光谱数据处理中一阶导数变换能够显著增加与小麦含水量的相关性,叶片含水量在456 nm波长处达到了最大负相关,相关系数为0.87,植株含水量在457 nm波长处达到了最大负相关,相关系数为0.890 9;偏最小二乘回归构建的水分含量估测模型拟合精度优于线性和多元回归模型,线性模型采用R₆₅₀、SG₁₉₄₄、R′₄₅₆、R″₆₈₁构建的模型估测叶片含水量较好,估测植株含水量R₆₆₄、SG₆₆₃、R′₄₅₇、R″₆₈₁精度较高; 多元线性回归和偏最小二乘回归都是采用一阶导数变换构建的模型拟合精度最高,叶片和植株水分含量估测模型的外部检验R²分别达到0.803 2、0.867 0、0.854 0、0.885 6。【结论】小麦原始光谱一阶导数变换后能够显著提高与水分含量的相关性,利用PLSR方法构建的小麦水分含量估测模型拟合精度最高。

关键词: 小麦, 水分含量, 植被指数, 高光谱

CLC Number:

:S512

LI Tian-sheng, CUI Jing, WANG Hai-jiang, YANG Jin. Study on Water Content Estimation Model of Winter Wheat Based on Hyperspectral Characteristic Wavelength[J]. Xinjiang Agricultural Sciences, 2019, 56(10): 1772-1782.

李天胜, 崔静, 王海江, 杨晋. 基于高光谱特征波长的冬小麦水分含量估测模型[J]. 新疆农业科学, 2019, 56(10): 1772-1782.

References

[1] 黄彩霞. 不同栽培技术措施对甘肃小麦产量及产量形成影响效应的研究[D].甘肃农业大学,2016.;HUANG Cai-xia.Effects of different cultivation techniques on wheat yield and yield formation in Gansu [D]. Master Dissertation. Gansu Agricultural University, Lanzhou,2016
[2] 陈智芳,宋妮,王景雷,等. 基于高光谱遥感的冬小麦叶水势估算模型[J]. 中国农业科学,2017,50(5):871-880.;CHEN Zhi-fang, SONG Ni, WANG Jing-lei, et al. Estimation model of leaf water potential of winter wheat based on hyperspectral remote sensing [J]. Scientia Agricultura Sinica, 2017,50(5):871-880.
[3] 梁亮,张连蓬,林卉,等. 基于导数光谱的小麦冠层叶片含水量反演[J]. 中国农业科学,2013,46(1):18-29.;LIANG Liang,ZHANG Lian-peng, LIN Hui, et al. Inversion of water content in wheat canopy leaves based on derivative spectrum [J]. Scientia Agricultura Sinica, 2013,46(1):18-29.
[4] 王大龙,舒英格. 土壤含水量测定方法研究进展[J]. 山地农业生物学报,2017,36(2):61-65.;WANG Da-long, SHU Ying-ge. Research progress in determination of soil moisture content [J]. Journal o Mountain Agricultural Biology, 2017,36(2):61-65.
[5] 朱西存,姜远茂,赵庚星,等.基于光谱指数的苹果叶片水分含量估算模型研究[J].中国农学通报,2014,30(4):120-126. ;ZHU Xi-cun, JIANG Yuan-mao, ZHAO Geng-xing, et al . Study on the estimation model of water content in apple leaves based on spectral index [J]. Chinese Agricultural Science Bulletin, 2014,30(4):120-126.
[6] 王纪华,赵春江,郭晓维,等.用光谱反射率诊断小麦叶片水分状况的研究[[J].中国农业科学,2001, 34(1): 104-107.;WAN Ji-hua, ZHAO Chun-jiang, GUO Xiao-wei, et al. Study on the diagnosis of wheat leaf water status by spectral reflectance [J]. Scientia Agricultura Sinica, 2001,34(1): 104-107.
[7] 孙林,程丽娟.植被叶片生化组分的光谱响应特征分析[J].光谱学与光谱分析,2010, 30(11):3 031-3 035.;SUN Lin, CHENG Li-juan. Spectral response characteristics analysis of biochemical components in vegetation leaves [J]. Spectroscopy & Spectral Analysis, 2010,30(11):3,031-3,035.
[8] Wang, J. , Xu, R. , & Yang, S. Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940. Environmental Monitoring & Assessment,2009,157(1-4):459-469.
[9] Ceccato, P. , Stéphane Flasse, Tarantola, S. , Stéphane Jacquemoud, & Jean-Marie Grégoire.Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 2001,77(1):22-33.
[10] Baghzouz, M. , Devitt, D. A. , & Morris, R. L. Assessing canopy spectral reflectance of hybrid bermudagrass under various combinations of nitrogen and water treatments. Applied Engineering in Agriculture,2007,23(6):763-774.
[11] 王纪华,赵春江,郭晓维,等.利用遥感方法诊断小麦叶片含水量的研究[J].华北农学报,2000, 15(4): 68-72.;WANG Ji-hua, ZHAO Chun-jiang, GUO Xiao-wei, et al.Diagnosis of water content in wheat leaves by remote sensing method [J]. Journal of Agronomy of North China, 2000,15(4): 68-72.
[12] 田庆久,宫鹏,赵春江,等.用光谱反射率诊断小麦水分状况的可行性分析[J].科学通报,2000, 45(24): 2 645-2 650.;TIAN Qing-jiu, GONG Peng, ZHAO Chun-jiang, et al. Feasibility analysis of using spectral reflectance to diagnose moisture status of wheat [J]. Chinese Science Bulletin,2000,45(24): 2,645-2,650.
[13] Yi, Q. X., Bao, A. M. , Wang, Q. , & Zhao, J. Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and Electronics in Agriculture,2013,90, 144-151.
[14] Riedell, W. E., & Blackmer, T. M. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Science,1999,39(6), 1,835-1,840.
[15] 刘小军,田永超,姚霞,等.基于高光谱的水稻叶片含水量监测研究[J].中国农业科学,2012,45(3): 435-442.;LIU Xiao-jun, TIAN Yong-chao, YAO Xia, et al. Study on water content monitoring in rice leaves based on hyperspectral method [J]. Chinese Journal of Agricultural Sciences 2012,45(3): 435-442.
[16] Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Imaging Spectrometry. International Society for Optics and Photonics,1995.
[17] 吉海彦,王鹏新,严泰来.冬小麦活体叶片叶绿素和水分含量与反射光谱的模型建立[J].光谱学与光谱分析,2007,(3):514-516.;JI Hai-yan, WANG Peng-xin, YAN Tai-lai. et al. Modeling of chlorophyll and water content and reflectance spectra in living leaves of winter wheat [J]. Spectroscopy and Spectral Analysis, 2007,(3):514-516.
[18] 李丹,何建国,刘贵珊,等.基于高光谱成像技术的小黄瓜水分无损检测[J].红外与激光工程,2014,43(7):2 393-2 397.;LI Dan, HE Jian-guo, LIU Gui-shan, et al. Non-destructive testing of cucumber moisture based on hyperspectral imaging technology [J]. Infrared and Laser Engineering, 2014,43(7):2,393-2,397.
[19] 刘明博,唐延林,李晓利,等.水稻叶片氮含量光谱监测中使用连续投影算法的可行性[J].红外与激光工程,2014,43(4):1 265-1 271.;LIU Ming-bo, TANG Yan-lin, LI Xiao-li, et al. Feasibility of using continuous projection algorithm in the spectral monitoring of nitrogen content in rice leaves [J]. Infrared and Laser Engineering,2014,43(4):1,265-1,271.
[20] Cheng, T., Rivard, B., & A. Sánchez-Azofeifa. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 2011,115(2):659-670.
[21] Penuelas J. Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosynthetica (Prague),1999,36(3):355-360.
[22] Yu, G. R. , Miwa, T. , Nakayama, K. , Matsuoka, N. , & Kon, H. . A proposal for universal formulas for estimating leaf water status of herbaceous and woody plants based on spectral reflectance properties. Plant and Soil,2000,227(1-2):47-58.
[23] Zhao, C. J. , Wang, J. H. , Liu, L. Y. , Huang, W. J. , & Zhou, Q. F. . Relationship of 2 100-2 300 nm spectral characteristics of wheat canopy to leaf area index and leaf n as affected by leaf water content. Pedosphere,2006,16(3):333-338.
[24] 陈志强,王磊,白由路,等.整个生育期玉米叶片SPAD高光谱预测模型研究. [J].光谱学与光谱分析,2013,33(10):2 838-2 842.;CHEN Zhi-qiang, WANG Lei, BAI You-lu, et al. Study on SPAD hyperspectral prediction model of maize leaves during the whole growth period [J]. Spectroscopy and Spectral Analysis, 2013,33(10):2,838-2,842.
[25] 王海江,刘凡,YUNGER John A,等.不同粒径处理的土壤全氮含量高光谱特征拟合模型[J].农业机械学报,2019,50(2):195-204. ;WANG Hai-jiang, LIU Fan, Yunger John, et al. Modeling of hyperspectral characteristics of total nitrogen content in soil with different particle sizes [J].Acta Agronomica Sinica,2019,50(2):195-204.
[26] 贺婷,李建东,刘桂鹏, 等.基于高光谱遥感的玉米全氮含量估测模型. [J].沈阳农业大学学报, 2016,47(3):257-265.;HE Ting, LI Jian-dong, LIU Gui-peng, et al. Estimation model of corn total nitrogen content based on hyperspectral remote sensing [J]. Journal of Shenyang Agricultural University,2016,47(3):257-265.
[27] 安静,姚国清,朱西存.苹果叶片氮素含量高光谱检测研究.[J].国土资源遥感, 2016,28(2):67-71.;AN Jing, YAO Guo-qing, ZHU Xi-cun. Study on hyperspectral detection of nitrogen content in apple leaves [J]. Remote Sensing of Land and Resources, 2016,28(2):67-71.
[28] 何勇,彭继宇,刘飞,等. 基于光谱和成像技术的作物养分生理信息快速检测研究进展.[J].农业工程学报,2015,31(3):174-189.;HE Yong, PENG Ji-yu, LIU Fei, et al. Research progress in rapid detection of crop nutrient physiological information based on spectral and imaging technology [J]. Journal of Agricultural Engineering,2015, 31(3):174-189.
[29] 乐源. 基于光谱数据的植被重点参数反演方法研究[D].武汉大学,2014.;LE Yuan. Research on the inversion method of key vegetation parameters based on spectral data [D]. PhD Dissertation. Wuhan University, Wuhan.2014.