不同灌溉条件下冬小麦冠层含水量的光谱响应
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摘要
【目的】寻找快速、无损地诊断冠层含水量的方法,对冬小麦长势监测、旱情评估及变量灌溉提供技术支持。【方法】基于田间变量灌溉试验,分析生育期、灌溉量对冬小麦冠层含水量的影响,解析冠层光谱对不同灌溉处理下冠层含水量的响应规律,以冠层等效水厚度(EWTc)为表征指标,基于连续小波变换(CWT)技术,构建冬小麦冠层等效水厚度光谱诊断模型,利用独立样本验证模型精度。【结果】冬小麦冠层等效水厚度在生育后期均随着灌溉量的增多而增加,并随着生育进程的推进而减少;冬小麦冠层光谱反射率随着生育进程的推进而降低,在近红外和中红外波段冠层光谱反射率均表现为1水>0.5水>0水;与原始冠层光谱反射率相比,经连续小波变换后的小波系数与冠层等效水厚度相关性在第1、2、3、5、6、7分解尺度均有不同程度的提高,提高幅度在8.40%—26.20%;以第6尺度2 400 nm、第2尺度1 596 nm和第7尺度2 397 nm构建的冠层等效水厚度光谱诊断模型稳定性和精度较好,验证样本决定系数R~2为0.5411,RMSE为0.0127 cm。【结论】冬小麦冠层含水量随着灌溉时间与灌溉量发生规律性变化,在水分敏感波段范围内呈现明显的光谱响应特征,连续小波变换技术可以有效提高冠层光谱特征参量与冠层等效水厚度的相关性,实现冬小麦冠层含水量光谱诊断,可以为冬小麦田间变量灌溉决策提供技术支持。
【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 scale6, 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.
【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 scale6, 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.
引文
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[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.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)
[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.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)
[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.
[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.
[8]YU G R,MIWA T,NAKAYAMA K,MATSUOKA N,KON H.Aproposal 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.
[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.
[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,JINGZHUW.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.
[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.
[16]NOURANI V,BAGHANAM A H,ADAMOWSKI J,GEBREMICHAELM.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.
[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.
[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.
[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.
[22]BAUER M E.Spectral inputs to crop identification and condition assessment.Proceedings of the IEEE,1985,73(6):1071-1085.
[23]GRANT L.Diffuse and specular characteristics of leaf reflectance.Remote Sensing of Environment,1987,22(2):309-322.
[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)
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