冬小麦叶片光合特征高光谱遥感估算模型的比较研究
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摘要
【目的】光合作用是农作物产量和品质形成的基础,农作物光合参数的准确定量遥感反演不仅能够了解农作物的生长发育和有机物累积状况,还能为基于遥感的生态系统过程模型提供参考。为快速准确的估算光合特征参量,本研究综合原始光谱、3种传统光谱变换技术和4种模拟方法构建冬小麦3种光合参数的高光谱反演模型,探讨高光谱反演冬小麦光合参数的可行性,对比不同类别光谱和模拟方法的适用性。【方法】本研究基于氮肥施用条件冬小麦气体交换和高光谱田间试验,获取不同叶位叶片的最大净光合速率(Amax)、PSⅡ有效光化学量子产量(Fv′/Fm′)、光化学猝灭系数(qP)和高光谱反射率,并对原始高光谱进行倒数、对数和一阶微分变换。根据3种光合参数和4种光谱的相关性分析结果,筛选显著性水平优于0.01的波段作为输入变量,采用偏最小二乘(PLS)、支持向量机(SVM)、多元线性回归(MLR)和人工神经网络(ANN)等方法建立冬小麦叶片光合参量反演模型,以建模和验证的决定系数(R~2)和均方根误差(RMSE)为依据,对不同模型的模拟精度进行比较分析。【结果】(1)3种光合参数和4种光谱的相关性分析结果表明,原始、倒数和对数光谱对3种光合参数(Amax、Fv′/Fm′和qP)的敏感谱区均集中在400—750 nm波谱区间,一阶导数光谱对3个光合参数的敏感谱区为470—560、630—700和700—770 nm波谱区间。(2)Amax、Fv′/Fm′和qP的最优反演模型组合分别为基于倒数光谱的MLR模型、基于一阶导数光谱的MLR模型和基于原始光谱的MLR模型。模型的建模R2分别为0.75、0.65和0.65,验证R2分别为0.73、0.59和0.44,表明基于高光谱模拟Amax和Fv′/Fm′切实可行,模拟qP的有效性需要进一步验证。(3)不同变换的光谱表现能力不同,以PLS模拟Amax为例,光谱的表现能力顺序为原始光谱>倒数光谱>对数光谱>一阶导数光谱。(4)不同模型的估算能力也存在明显差异,以基于原始光谱的Amax模拟为例,不同模型的估算能力顺序为MLR>PLS>ANN>SVM。【结论】通过对比分析4种光谱和4种模拟方法对3种冬小麦光合参数的高光谱反演结果发现,Amax和Fv′/Fm′可以很好通过高光谱进行模拟,而高光谱对qP解释能力偏低,有待进一步研究。高光谱信息对冬小麦光合参量具有较强的敏感性,同时受光谱类型和模拟方法的影响,可以用来监测冬小麦光合能力的动态变化,为把握农作物生长状况提供依据。
【Objective】Photosynthesis is the basis of crop yield and quality formation. Accurate quantitative remote sensing inversion of crop photosynthetic parameters can not only understand the growth and development of crops and the accumulation of organic matter, but also can provide reference for the ecosystem process model based on remote sensing. In order to estimate the photosynthetic characteristic parameters quickly and accurately, the hyperspectral inversion model of three photosynthetic parameters of winter wheat was constructed by combining the original spectrum, three traditional spectral transformation techniques and four simulation methods. The feasibility of hyperspectral inversion of photosynthetic parameters of winter wheat was discussed, and the applicability of different spectra and simulation methods were compared. 【Method】The maximum net photosynthetic rate(Amax),PSⅡ effective photochemical quantum yield(Fv'/Fm') of different leaf ages was obtained under the support of gas exchange and hyperspectral field experiments of winter wheat under different nitrogen application conditions. The photochemical quenching coefficient(qP) and hyperspectral reflectivity were obtained, and the reciprocal, logarithmic and first-order differential transformations of the original hyperspectrum were carried out. According to the results of correlation analysis of three photosynthetic parameters and four spectra, the band whose significant level was better than 0.01 was selected as input variable, and then the partial least square(PLS), support vector machine(SVM), multivariate linear regression(MLR) and artificial neural network(ANN) were used to establish the inversion model of photosynthetic parameters of winter wheat leaves. Based on the determination coefficient(R~2) and root mean square error(RMSE) of modeling and validation process, the simulation accuracy of different models was compared and analyzed.【Result】(1) The correlation analysis of the three photosynthetic parameters and four spectra showed that the sensitive spectral regions of the primitive, reciprocal and logarithmic spectra to the three photosynthetic parameters(Amax, Fv′/Fm′ and qP)were concentrated in the 400-750 nm spectrum range. The sensitive spectral regions of the first derivative spectrum to the three photosynthetic parameters were 470-560, 630-700 and 700-770 nm, respectively.(2) The optimal inversion model of Amax, Fv'/Fm'and qP was composed of MLR model based on reciprocal spectrum, MLR model based on first derivative spectrum and MLR model based on original spectrum, respectively. The R~2 of the modeling was 0.75, 0.65 and 0.65, respectively, and the R~2 of the validation was 0.73, 0.59 and 0.44, respectively. The results showed that the simulation of Amax and Fv'/Fm' based on hyperspectral method was feasible, the effectiveness of simulated qP needed further be verified.(3) The spectral performance of different transformations was different. Taking PLS simulation Amax as an example, the order of spectral performance was original spectrum > reciprocal spectrum > logarithmic spectrum > first derivative spectrum.(4) The estimation ability of different models was also different. Taking Amax simulation based on original spectrum as an example, the order of estimation ability of different models was MLR > PLS >ANN > SVM.【Conclusion】By comparing four spectra and four simulation methods, the hyperspectral inversion results of three photosynthetic parameters of winter wheat showed that Amax and Fv'/Fm' could be well simulated by hyperspectral method, but hyperspectral interpretation ability to qP was low and further study was needed. The hyperspectral information was sensitive to the photosynthetic parameters of winter wheat and affected by spectral types and simulation methods. It could be used to monitor the dynamic changes of photosynthetic capacity of winter wheat and to provide a basis for understanding the growth of crops.
【Objective】Photosynthesis is the basis of crop yield and quality formation. Accurate quantitative remote sensing inversion of crop photosynthetic parameters can not only understand the growth and development of crops and the accumulation of organic matter, but also can provide reference for the ecosystem process model based on remote sensing. In order to estimate the photosynthetic characteristic parameters quickly and accurately, the hyperspectral inversion model of three photosynthetic parameters of winter wheat was constructed by combining the original spectrum, three traditional spectral transformation techniques and four simulation methods. The feasibility of hyperspectral inversion of photosynthetic parameters of winter wheat was discussed, and the applicability of different spectra and simulation methods were compared. 【Method】The maximum net photosynthetic rate(Amax),PSⅡ effective photochemical quantum yield(Fv'/Fm') of different leaf ages was obtained under the support of gas exchange and hyperspectral field experiments of winter wheat under different nitrogen application conditions. The photochemical quenching coefficient(qP) and hyperspectral reflectivity were obtained, and the reciprocal, logarithmic and first-order differential transformations of the original hyperspectrum were carried out. According to the results of correlation analysis of three photosynthetic parameters and four spectra, the band whose significant level was better than 0.01 was selected as input variable, and then the partial least square(PLS), support vector machine(SVM), multivariate linear regression(MLR) and artificial neural network(ANN) were used to establish the inversion model of photosynthetic parameters of winter wheat leaves. Based on the determination coefficient(R~2) and root mean square error(RMSE) of modeling and validation process, the simulation accuracy of different models was compared and analyzed.【Result】(1) The correlation analysis of the three photosynthetic parameters and four spectra showed that the sensitive spectral regions of the primitive, reciprocal and logarithmic spectra to the three photosynthetic parameters(Amax, Fv′/Fm′ and qP)were concentrated in the 400-750 nm spectrum range. The sensitive spectral regions of the first derivative spectrum to the three photosynthetic parameters were 470-560, 630-700 and 700-770 nm, respectively.(2) The optimal inversion model of Amax, Fv'/Fm'and qP was composed of MLR model based on reciprocal spectrum, MLR model based on first derivative spectrum and MLR model based on original spectrum, respectively. The R~2 of the modeling was 0.75, 0.65 and 0.65, respectively, and the R~2 of the validation was 0.73, 0.59 and 0.44, respectively. The results showed that the simulation of Amax and Fv'/Fm' based on hyperspectral method was feasible, the effectiveness of simulated qP needed further be verified.(3) The spectral performance of different transformations was different. Taking PLS simulation Amax as an example, the order of spectral performance was original spectrum > reciprocal spectrum > logarithmic spectrum > first derivative spectrum.(4) The estimation ability of different models was also different. Taking Amax simulation based on original spectrum as an example, the order of estimation ability of different models was MLR > PLS >ANN > SVM.【Conclusion】By comparing four spectra and four simulation methods, the hyperspectral inversion results of three photosynthetic parameters of winter wheat showed that Amax and Fv'/Fm' could be well simulated by hyperspectral method, but hyperspectral interpretation ability to qP was low and further study was needed. The hyperspectral information was sensitive to the photosynthetic parameters of winter wheat and affected by spectral types and simulation methods. It could be used to monitor the dynamic changes of photosynthetic capacity of winter wheat and to provide a basis for understanding the growth of crops.
引文
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[2]周磊,何洪林,孙晓敏,张黎,于贵瑞,任小丽,闵程程,赵凤华.基于数字相机的冬小麦物候和碳交换监测.生态学报,2012,32(16):5146-5153.ZHOU L,HE H L,SUN X M,ZHANG L,YU G R,REN X L,MIN CC,ZHAO F H.Using digital repeat photography to model winter wheat phenology and photosynthetic CO2 uptake.Acta Ecologica Sinica,2012,32(16):5146-5153.(in Chinese)
[3]李少昆.作物光合作用研究方法.石河子大学学报(自然科学版),2000,4(4):321-330.LI S K.Survey of research method of crop photosynthesis.Journal of Shihezi University(Natural Science),2000,4(4):321-330.(in Chinese)
[4]DECHANT B,CUNTZ M,VOHLAND M,SCHULZ E,DOKTOR D.Estimation of photosynthesis traits from leaf reflectance spectra:Correlation to nitrogen content as the dominant mechanism.Remote Sensing of Environment,2017,196:279-292.
[5]吕玮,李玉环,毛伟兵,宫雪,陈世更.基于高光谱的小麦旗叶净光合速率的遥感反演模型的比较研究.农业资源与环境学报,2017,34(6):582-586.LüW,LI Y H,MAO W B,GONG X,CHEN S G.Comparison of estimation methods for net photosynthetic rate of wheat’s flag leaves based on hyperspectrum.Journal of Agricultural Resources and Environment,2017,34(6):582-586.(in Chinese)
[6]王娣,佃袁勇,乐源,黄春波.基于高光谱植被指数的叶片净光合速率Pn反演.地理与地理信息科学,2016,32(4):42-48.WANG D,DIAN Y Y,LE Y,HUANG C B.Net photosynthetic rate inversion based on hyperspectral vegetation indices.Geography and Geo-Information Science,2016,32(4):42-48.(in Chinese)
[7]张峰,周广胜.玉米农田冠层光合参数的多光谱遥感反演.植物生态学报,2014,38(7):710-719.ZHANG F,ZHOU G S.Estimating canopy photosynthetic parameters in maize field based on multi-spectral remote sensing.Chinese Journal of Plant Ecology,2014,38(7):710-719.(in Chinese)
[8]SERBIN S P,SINGH A,DESAI A,DUBOIS S.Remotely estimating photosynthetic capacity,and its response to temperature,in vegetation canopies using imaging spectroscopy.Remote Sensing of Environment,2015,167:78-87.
[9]FARQUHAR G D,CAEMMERER S V,BERRY J A.A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species.Planta,1980,149:78-90.
[10]DEMMIG-ADAMS B.Using chlorophyll fluorescence to assess the fraction of absorbed light allocated to thermal dissipation of excess excitation.Physiologia Plantarum,1996,98(2):253-264.
[11]WOLD H.Estimation of principal components and related models by iterative least squares.Multivariate Analysis,1966,1:391-420.
[12]WU S,AMARI S I.Conformal transformation of Kernel functions:Adata-dependent way to improve support vector machine classifiers.Neural Process Letters,2002,15(1):59-67.
[13]ZELTERMAN D.Multivariable linear regression,applied multivariate statistics with R.Springer International Publishing,2015:231-256.
[14]PARK D C,ELSHARKAWI M A,MARKS R J I.Electric load forecasting using an artificial neural network.IEEE Transactions on Power Systems,1991,6(2):442-449.
[15]YUE J,FENG H,YANG G,LI Z.A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy.Remote Sensing,2018,10(1):66-89.
[16]DECHANT B,CUNTZ M,VOHLAND M,SCHULZ E.Estimation of photosynthesis traits from leaf reflectance spectra:Correlation to nitrogen content as the dominant mechanism.Remote Sensing of Environment,2017,196:279-292.
[17]李婷,季宇寒,张漫,沙莎,李民赞.基于PLSR和BPNN方法的番茄光合速率预测比较.农业工程学报,2015,31(S2):222-229.LI T,JI Y H,ZHANG M,SHA S,LI M Z.Comparison of photosynthesis prediction methods with BPNN and PLSR in different growth stages of tomato.Transactions of the Chinese Society of Agricultural Engineering,2015,31(S2):222-229.(in Chinese)
[18]陈俊英,陈硕博,张智韬,付秋萍,边江,崔婷.无人机多光谱遥感反演花蕾期棉花光合参数研究.农业机械学报,2018,49(10):230-239.CHEN J Y,CHEN S B,ZHANG Z T,FU Q P,BIAN J,CUI T.Investigation on photosynthetic parameters of cotton during budding period by multi-spectral remote sensing of unmanned aerial vehicle.Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):230-239.(in Chinese)
[19]DAUGHTRY C S T,WALTHALL C L,KIM M S,MCMURTREY J E.Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance.Remote Sensing of Environment,2000,74(2):229-239.
[20]赵祥,刘素红,王培娟,王锦地,田振坤.基于高光谱数据的小麦叶绿素含量反演.地理与地理信息科学,2004,20(3):36-39.ZHAO X,LIU S H,WANG P J,WANG J D,TIAN Z K.A method for inverting chlorophyll content of wheat using hyperspectral.Geography and Geo-Information Science,2004,20(3):36-39.
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