Scientia Agricultura Sinica ›› 2019, Vol. 52 ›› Issue (14): 2425-2435.doi: 10.3864/j.issn.0578-1752.2019.14.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Spectral Response Analysis of Canopy Water Content of Winter Wheat Under Different Irrigation Conditions

SUN Qian1,2,3,GU XiaoHe1,2(),SUN Lin3,WANG Miao4,ZHOU LongFei1,2,3,YANG GuiJun1,2,LI WeiGuo5,SHU MeiYan1,2,3   

  1. 1National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    2Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture/Beijing Research Center for Information Technology in Agriculture, Beijing 100097
    3College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong
    4Hebei Agricultural Technology Extension General Station, Shijiazhuang 050011
    5Institute of Agricultural Information, Jiangsu Academy of Agricultural Sciences, Nanjing 210014
  • Received:2019-03-05 Accepted:2019-03-29 Online:2019-07-16 Published:2019-07-26
  • Contact: XiaoHe GU E-mail:guxh@nercita.org.cn

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Abstract:

【Objective】 Rapid and non-destructive diagnosis of canopy water content is of great significance for monitoring winter wheat growth, drought assessment and variable irrigation. The response of canopy spectrum to canopy water content under different irrigation treatments was analyzed in this study.【Method】Based on field variable irrigation experiments, the influence of growth stage and irrigation water on canopy water content of winter wheat were analyzed. The response rule of canopy spectrum to canopy water content under different irrigation treatments was explained. The canopy equivalent water thickness (EWTc) was used as the characterization index. Based on continuous wavelet transform (CWT), a spectral diagnostic model of EWTc of winter wheat was constructed. The accuracy of the model was verified by independent samples. 【Result】 The results showed that the EWTc of winter wheat increased with the increase of irrigation water in the later growth stage, and decreased with the advance of growth process. The canopy spectral reflectance of winter wheat decreased with the progress of the growth process. The canopy spectral reflectance of winter wheat at different irrigation treatments in near infrared and mid-infrared bands were as follows: 1 water > 0.5 water > 0 water. Compared with the original canopy spectral reflectance, the correlation between wavelet coefficients after continuous wavelet transform and EWTc was improved in different degrees at the decomposition scales of 1, 2, 3, 5, 6 and 7. In addition, the increase ranged from 8.40% to 26.20%. The spectral diagnostic model of canopy equivalent water thickness constructed at 2 400 nm in scale 6, 1 596 nm in scale 2, and 2 397 nm in scale 7 was better in stability and accuracy. The verification sample determined the coefficient R 2=0.5411, and RMSE=0.0127 cm.【Conclusion】The canopy water content of winter wheat changed regularly with irrigation time and irrigation amount, and showed obvious spectral response characteristics in the water sensitive band. Continuous wavelet transform technology could effectively improve the correlation between canopy spectral parameters and canopy equivalent water thickness. The spectral diagnosis of canopy water content of winter wheat was realized. It could provide technical support for variable irrigation decision-making in winter wheat field.

Key words: winter wheat, EWTc, LAI, irrigation, CWT, canopy spectrum

Fig. 1

Schematic diagram of experimental plot"

Table 1

The date of irrigation and data collection (M-D)"

生育期
Growth stage		 灌溉日期
Irrigation date		 田间取样时间
Field sampling time		 光谱和LAI测定时间
Spectral and LAI measurement time
孕穗期 Booting stage 04-17 04-24 04-24
扬花期 Flowering stage 04-27 05-04 05-04

Fig. 2

Changes of EWTc in winter wheat"

Fig. 3

Changes of spectra in winter wheat canopy"

Fig. 4

Changes of soil spectra with different water contents"

Fig. 5

Correlation coefficient between original spectral reflectance and EWTc"

Fig. 6

Correlation coefficient between wavelet energy coefficient and EWTc at different decomposition scales"

Fig. 7

Absolute value of correlation coefficient between wavelet energy coefficient and EWTc"

Table 2

Maximum absolute value of correlation coefficients of original spectra and wavelet coefficients with EWTc"

分解尺度
Decomposition scale		 ∣R∣最大值
Maximum absolute value of R		 波长
Wavelength (nm)		 提高幅度
Increase range (%)
原始光谱
Original spectra		 0.5167 1946 —
1 0.6521 1222 26.2048
2 0.6353 1596 22.9534
3 0.5601 1595 8.3995
4 0.5064 734 -1.9934
5 0.5727 2418 10.8380
6 0.6355 2400 22.9921
7 0.6004 2397 16.1990
8 0.4664 1316 -9.7349
9 0.4508 474 -12.7540
10 0.2736 2345 -47.0486

Fig. 8

Scatter plot of predicted and measured values of EWTc"

[1] 郭进考, 史占良, 何明琦, 张相岐, 张爱民, 贾旭 . 发展节水小麦缓解北方水资源短缺——以河北省冬小麦为例. 中国生态农业学报, 2010,18(4):876-879.
GUO J K, SHI Z L, HE M Q, ZHANG X Q, ZHANG A M, JIA X . Development of water-saving wheat cultivars to limit water shortage in North China—a case study of Hebei Province. Chinese Journal of Eco-Agriculture, 2010,18(4):876-879. (in Chinese)
[2] 刘佳俊, 董锁成, 李泽红 . 中国水资源承载力综合评价研究. 自然资源学报, 2011,26(2):258-269.
doi: 10.11849/zrzyxb.2011.02.009
LIU J J, DONG S C, LI Z H . Comprehensive evaluation of China's water resources carrying capacity. Journal of Natural Resources, 2011,26(2):258-269. (in Chinese)
doi: 10.11849/zrzyxb.2011.02.009
[3] 张建云, 贺瑞敏, 齐晶, 刘翠善, 王国庆, 金君良 . 关于中国北方水资源问题的再认识. 水科学进展, 2013,24(3):303-310.
ZHANG J Y, HE R M, QI J, LIU C S, WANG G Q, JIN J L . A new perspective on water issues in North China. Advances in Water Science, 2013,24(3):303-310. (in Chinese)
[4] 苏其红, 刘媛, 栗孟飞, 杨德龙, 陈菁菁, 程宏波, 常磊, 柴守玺 . 干旱调控小麦旗叶持绿性与产量变异的遗传与相关性分析. 分子植物育种, 2018,16(19):6353-6364.
SU Q H, LIU Y, LI M F, YANG D L, CHEN J J, CHENG H B, CHANG L, CHAI S X . Hereditary and correlation analysis of yield variability and stay-green of flag leaf regulated by drought in wheat. Molecular Plant Breeding, 2018,16(19):6353-6364. (in Chinese)
[5] 郭瑞, 周际, 杨帆, 李峰, 李昊如, 夏旭, 刘琪 . 拔节孕穗期小麦干旱胁迫下生长代谢变化规律. 植物生态学报, 2016,40(12):1319-1327.
doi: 10.17521/cjpe.2016.0107
GUO R, ZHOU J, YANG F, LI F, LI H R, XIA X, LIU Q . Growth metabolism of wheat under drought stress at the jointing-booting stage. Chinese Journal of Plant Ecology, 2016,40(12):1319-1327. (in Chinese)
doi: 10.17521/cjpe.2016.0107
[6] STEIDLE NETO A J, LOPES D C, SILVA T G F, FERREIRA S O, GROSSI J A S . Estimation of leaf water content in sunflower under drought conditions by means of spectral reflectance. Engineering in Agriculture, Environment and Food, 2017,10(2):104-108.
doi: 10.1016/j.eaef.2016.11.006
[7] GIZAW S A, GARLAND-CAMPBELL K, CARTER A H . Evaluation of agronomic traits and spectral reflectance in Pacific Northwest winter wheat under rain-fed and irrigated conditions. Field Crops Research, 2016,196:168-179.
doi: 10.1016/j.fcr.2016.06.018
[8] 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, 2000,227:47-58.
doi: 10.1023/A:1026556613082
[9] 王纪华, 赵春江, 郭晓维, 田庆久 . 用光谱反射率诊断小麦叶片水分状况的研究. 中国农业科学, 2001,34(1):1-4.
WANG J H, ZHAO C J, GUO X W, TIAN Q J . Study on the water status of the wheat leaves diagnosed by the spectral reflectance. Scientia Agricultura Sinica, 2001,34(1):1-4. (in Chinese)
[10] 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.
doi: 10.1016/j.compag.2012.09.011
[11] GLADIMIR V G B, SPENCER V L, TENN F C . On the detection and monitoring of reduced water content in plants using spectral responses in the visible domain. Land Surface & Cryosphere Remote Sensing III, SPIE Asia-Pacific Remote Sensing, 2016,9877:1-11.
[12] NING L, LI W, LONGSHENG C, HONG S QIAOXUE D, JINGZHU W . Spectral characteristics analysis and water content detection of potato plants leaves. International Federation of Automatic Control, 2018,51(17):541-546.
[13] CECCATO P, GOBRON N, FLASSE S, PINTY B, TARANTOLA S . Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment, 2002,82(2/3):188-197.
doi: 10.1016/S0034-4257(02)00037-8
[14] CLEVERS J G P W, KOOISTRA L, SCHAEPMAN M E . Estimating canopy water content using hyperspectral remote sensing data. International Journal of Applied Earth Observation & Geoinformation, 2010,12(2):119-125.
[15] BLACKBURN G A, FERWERDA J G . Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 2008,112(4):1614-1632.
doi: 10.1016/j.rse.2007.08.005
[16] NOURANI V, BAGHANAM A H, ADAMOWSKI J, GEBREMICHAEL M . Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. Journal of Hydrology, 2013,476:228-243.
doi: 10.1016/j.jhydrol.2012.10.054
[17] MILLER J R, HARE E W, WU J . Quantitative characterization of the vegetation red edge reflectance 1, an inverted-Gaussian reflectance model. International Journal of Remote Sensing, 1990,11(10):1755-1773.
doi: 10.1080/01431169008955128
[18] 王纪华, 赵春江, 黄文江 . 农业定量遥感基础与应用. 北京: 科学出版社, 2008.
WANG J H, ZHAO C J, HUANG W J. The Basis and Application of Agricultural Quantitative Remote Sensing. Beijing: Science Press, 2008. ( in Chinese)
[19] LOBELL D B, ASNER G P . Moisture effects on soil reflectance. Soil Science Society of America Journal, 2002,66(3):722-727.
doi: 10.2136/sssaj2002.7220
[20] 卢艳丽, 白由路, 王磊, 杨俐苹 . 农田不同粒级土壤含水量光谱特征及定量预测. 中国农业科学, 2018,51(9):1717-1724.
LU Y L, BAI Y L, WANG L, YANG L P . Spectral characteristics and quantitative prediction of soil water content under different soil particle sizes. Scientia Agricultura Sinica. 2018,51(9):1717-1724. (in Chinese)
[21] CHENG T, RIVARD B, SÁNCHEZAZOFEIFA G A . Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 2010,114(4):899-910.
doi: 10.1016/j.rse.2009.12.005
[22] BAUER M E . Spectral inputs to crop identification and condition assessment. Proceedings of the IEEE, 1985,73(6):1071-1085.
doi: 10.1109/PROC.1985.13238
[23] GRANT L . Diffuse and specular characteristics of leaf reflectance. Remote Sensing of Environment, 1987,22(2):309-322.
doi: 10.1016/0034-4257(87)90064-2
[24] 郑兴明, 丁艳玲, 赵凯, 姜涛, 李晓峰, 张世轶, 李洋洋, 武黎黎, 孙建, 任建华, 张宣宣 . 基于Landsat 8 OLI数据的玉米冠层含水量反演研究. 光谱学与光谱分析, 2014,34(12):3385-3390.
ZHENG X M, DING Y L, ZHAO K, JIANG T, LI X F, ZHANG S Y, LI Y Y, WU L L, SUN J, REN J H, ZHANG X X . Estimation of vegetation water content from Landsat 8 OLI data. Spectroscopy and Spectral Analysis, 2014,34(12):3385-3390. (in Chinese)
[25] 宋小宁, 马建威, 李小涛, 冷佩, 周芳成, 李爽 . 基于Hyperion高光谱数据的植被冠层含水量反演. 光谱学与光谱分析, 2013,33(10):2833-2837.
SONG X N, MA J W, LI X T, LENG P, ZHOU F C, LI S . Estimation of vegetation canopy water content using Hyperion hyperspectral data. Spectroscopy and Spectral Analysis, 2013,33(10):2833-2837. (in Chinese)
[26] 束美艳, 顾晓鹤, 孙林, 朱金山, 杨贵军, 王延仓, 张丽妍 . 基于新型植被指数的冬小麦LAI高光谱反演. 中国农业科学, 2018,51(18):3486-3496.
SHU M Y, GU X H, SUN L, ZHU J S, YANG G J, WANG Y C, ZHANG L Y . High spectral inversion of winter wheat LAI based on new vegetation index. Scientia Agricultura Sinica, 2018,51(18):3486-3496. (in Chinese)
[27] 张俊华, 张佳宝 . 不同生育期冬小麦光谱特征对叶绿素和氮素的响应研究. 土壤通报, 2008,39(3):586-592.
ZHANG J H, ZHANG J B . Response of winter wheat spectral reflectance to leaf chlorophyll, total nitrogen of above ground. Chinese Journal of Soil Science, 2008,39(3):586-592. (in Chinese)
[28] 卢艳丽, 胡昊, 白由路, 王磊, 王贺, 杨俐苹 . 植被覆盖度对冬小麦冠层光谱的影响及定量化估产研究. 麦类作物学报, 2010,30(1):96-100.
doi: 10.7606/j.issn.1009-1041.2010.01.020
LU Y L, HU H, BAI Y L, WANG L, WANG H, YANG L P . Effects of vegetation coverage on the canopy spectral and yield quantitative estimation in wheat. Journal of Triticeae Crops, 2010,30(1):96-100. (in Chinese)
doi: 10.7606/j.issn.1009-1041.2010.01.020
[29] ZARCO-TEJADA P J, MILLER J R, NOLAND T L, MOHAMMED G H, SAMPSON P H . Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(7):1491-1507.
doi: 10.1109/36.934080
[30] 丛建鸥, 李宁, 许映军, 顾卫, 乐章燕, 黄树青, 席宾, 雷飏 . 干旱胁迫下冬小麦产量结构与生长、生理、光谱指标的关系. 中国生态农业学报, 2010,18(1):67-71.
CONG J O, LI N, XU Y J, GU W, LE Z Y, HUANG S Q, XI B, LEI Y . Relationship between indices of growth, physiology and reflectivity and yield of winter wheat under water stress. Chinese Journal of Eco-Agriculture, 2010,18(1):67-71. (in Chinese)
[31] 谷艳芳, 丁圣彦, 陈海生, 高志英, 邢倩 . 干旱胁迫下冬小麦(Triticum aestivum)高光谱特征和生理生态响应. 生态学报, 2008,28(6):2690-2697.
GU Y F, DING S Y, CHEN H S, GAO Z Y, XING Q . Ecophysiological responses and hyperspectral characteristics of winter wheat (Triticum aestivum) under drought stress. Acta Ecologica Sinica, 2008,28(6):2690-2697. (in Chinese)
[32] 牟筱玲, 鲍啸 . 土壤水分胁迫对棉花叶片水分状况及光合作用的影响. 中国棉花, 2003,30(9):9-10.
MOU X L, BAO X . Effects of soil water stress on water status and photosynthesis of cotton leaves. Chinese Cotton, 2003,30(9):9-10. (in Chinese)
[33] 许振柱, 于振文, 亓新华, 余松烈 . 土壤干旱对冬小麦旗叶乙烯释放、多胺积累和细胞质膜的影响. 植物生理学报, 1995,21(3):295-301.
XU Z Z, YU Z W, QI X H, YU S L . Effect of soil drought on ethylene evolution, polyamine accumulation and cell membrane in flag leaf of winter wheat. Acta Phytophysiologica Sinica, 1995,21(3):295-301. (in Chinese)
[34] AL-GHAMDI A A , Evaluation of oxidative stress tolerance in two wheat ( Triticum aestivum) cultivars in response to drought. International Journal of Agriculture & Biology, 2009,11(1):1560-8530.
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