冬小麦高光谱特征及其生理生态参数估算模型研究
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摘要
精准农业是现代农业生产中实现低耗、高效、优质和环境友好目标的根本途径，遥感技术可以快速获取农田作物生长状态的实时信息，为实施精确农业提供重要的技术支撑。高光谱遥感能在特定光谱范围内以高光谱分辨率同时获取连续的地物光谱曲线，从而可以构成独特的多维光谱空间，使得遥感应用着重于在光谱维上进行空间信息展开，获得目标物更多的光谱信息。因此，应用高光谱遥感可增强对植株生理生态参数的监测能力，提高作物生长监测的精度和准确性。本研究以冬小麦为研究对象，以系列大田试验以及区域采样试验为依托，综合运用高光谱遥感、生长分析、生理生态测试及数理统计、小波变换等技术手段，分析不同播种密度、不同水分处理条件下，不同品种、不同水氮条件下冬小麦冠层高光谱特征，分生育期建立了基于植被指数、光谱特征参数、小波能量系数、小波分形的冬小麦叶绿素含量、覆盖度以及叶面积指数的高光谱估算模型，最后将植被指数估算模型应用到ETM+遥感卫星影像中，验证所建立的基于高光谱冬小麦生理生态参数估算模型应用到遥感卫星影像的可能性，从而为动态监测冬小麦生长变化和精确管理提供理论基础和关键技术。研究取得了以下主要结果：
（1）研究了不同品种、不同水氮条件下冬小麦叶绿素含量、叶面积指数的变化以及不同播种密度、不同水分处理条件下冬小麦覆盖度的变化，并分析了冬小麦冠层高光谱特征的响应。结果表明：从返青到成熟，可见光区冬小麦冠层光谱反射率先减少后增大，近红外区先增大后减少。覆盖度、叶面积指数、叶绿素含量越大，冬小麦光谱反射率在可见光波段越小，在近红外波段越大。不同种植密度下，适宜供水冬小麦在可见光波段的反射率依次小于轻度亏水、重度亏水条件下的冬小麦；在近红外波段，规律正好相反。在不同水分处理条件下，随着施氮量的增加，可见光部分反射率下降，近红外反射率呈上升趋势。此研究结果为进一步利用冬小麦冠层光谱信息监测冬小麦生长状况提供了理论基础。
（2）研究了冬小麦冠层原始光谱、导数光谱与冬小麦叶片绿度SPAD值、覆盖度和叶面积指数的相关关系。在可见光波段，返青期、拔节期、抽穗期与灌浆期冬小麦原始光谱反射率与叶片SPAD值、覆盖度、叶面积指数负相关，在“红边”处，由负相关变成正相关；返青期、拔节期、抽穗期与灌浆期导数光谱与SPAD值、覆盖度、叶面积指数之间的相关系数在一些波段处高于原始光谱与SPAD值、覆盖度、叶面积指数的相关系数；成熟期冬小麦原始光谱、导数光谱与叶片SPAD值、覆盖度、叶面积指数相关性低，建立冬小麦叶片SPAD值、覆盖度、叶面积指数高光谱估算模型时，不能使用成熟期光谱数据。
（3）应用2010年冬小麦冠层光谱与冬小麦叶片SPAD值、覆盖度、叶面积指数数据，分别建立了返青期、拔节期、抽穗期与灌浆期冬小麦基于植被指数（NDVI、RVI）、原始光谱特征参数（绿峰反射率、绿峰位置、绿峰面积、红谷反射率、红谷位置、红谷面积、绿峰反射率与红谷反射率比值与归一化值，绿峰偏度、绿峰峰度、红谷偏度、红谷峰度，绿峰偏度与红谷偏度比值与归一化值、绿峰峰度与红谷峰度比值与归一化值）、红边参数（红边位置、红边振幅、红边面积，红边峰度、红边偏度）、小波能量系数、小波分形维数的叶片SPAD、覆盖度、叶面积指数估算模型，并应用2011年冬小麦冠层光谱与冬小麦叶片SPAD值、覆盖度、叶面积指数数据对所建模型进行了精度检验。结果表明：运用NDVI、RVI植被指数反演冬小麦SPAD值时，NDVI植被指数预测精度更高。运用NDVI、RVI植被指数反演冬小麦覆盖度时，在返青期和拔节期，使用NDVI植被指数反演效果好，在抽穗期、灌浆期，使用RVI植被指数反演效果好。运用NDVI、RVI植被指数反演冬小麦叶面积指数时，优先考虑应用RVI植被指数；使用原始光谱特征参数反演冬小麦叶片SPAD值时，拔节期优先考虑红谷面积，返青期、抽穗期、灌浆期优先考虑红谷偏度。使用原始光谱特征参数反演冬小麦覆盖度时，优先考虑绿峰峰度。使用原始光谱特征参数反演冬小麦叶面积指数时，拔节期优先考虑绿峰反射率与红谷反射率比值，返青期、抽穗期、灌浆期优先考虑绿峰偏度；应用红边参数反演冬小麦叶片SPAD值时，优先考虑红边峰度。应用红边参数反演冬小麦覆盖度时，拔节期优先考虑红边峰度，其余时期优先考虑红边偏度。应用红边参数反演冬小麦叶面积指数时，优先考虑红边峰度；运用小波能量系数反演冬小麦叶片SPAD值时，优先考虑使用低频信息。运用小波能量系数反演冬小麦覆盖度时，优先考虑使用高频信息。运用小波能量系数反演冬小麦叶面积指数时，优先考虑使用低频信息。运用小波分形维数的低频信息可有效的反演返青期、拔节期、抽穗期与灌浆期冬小麦叶片SPAD值、覆盖度、叶面积指数。
（4）探讨了基于冠层植被指数冬小麦叶片SPAD值、覆盖度、叶面积指数估算模型在ETM+遥感数据上应用的可能性。结果表明：当将基于冠层光谱构建的植被指数应用于ETM+遥感数据时，预测值与实测值间相关系数均通过了显著性检验。
Precision agriculture is the basic way to realize the aims of low consumption, highefficiency, good quality, and friendly environment. Real-time information of growth state offarmland crops can be quickly obtained by remote sensing, which can provide importanttechnical support for the implementation of precision agriculture. Hyperspectral remotesensing can detect the successive spectral curve of ground objects with hyperspectralresolution in some spectral region and form the unique multi-dimensional spectrum space.Remote sensing application focuses on spreading spatial information on spectrum dimension,thereby obtaining more spectral information of object. The application of hyperspectralremote sensing can strengthen the monitoring ability of physiological ecological parametersand improve the monitoring accuracy of crop growth. Based on field experiments andregional sampling of winter wheat, this study analyzed the hyperspectral characteristics ofwheat canopy by comprehensive application of hyperspectral remote sensing, growth analysis,physiological ecology test, mathematical statistics, and wavelet transform for different sowingdensities, water treatments, species, and water and nitrogen treaments. Then the hyperspectralestimation models of chlorophyll content, percentage vegetation cover, and leaf area index(LAI) were established on the basis of vegetation index, spectral characteristic parameters,wavelet energy coefficient, and wavelet fractal at different growth stages. Finally, theestimation model of vegetation index was used to the ETM+satellite images of remotesensing. Then the actual application probability was tested, which provided the theory basisand key technology for dynamic monitoring of growth change and precise management. Themain results are as follows:
(1) Changes of chlorophyll content and LAI in different species and water and nitrogenlevels were studied as well as variation of percentage vegetation cover under different sowingdensities and water levels. Additionally, the response of hyperspectral characteristics of wheat canopy was analysed. The result showed that the spectral reflectance of visible spectrumreduced at first and then increased from wheat green-turning to maturation stage. However,the spectral reflectance of near infrared spectrum demonstrated an opposite tendency. Itdecreased under the visible spectrum and increased under near infrared spectrum as thepercentage vegetation cover, leaf area index and chlorophyll content became larger. Thespectral reflectance decreased with the increase of water shortage in the visible spectrum,while on the contrary it increased in near infrared spectrum. Following the increased nitrogenlevels, there is a declinen of spectral reflectance in visible spectrum and an increase in nearinfrared spectrum in different moisture conditions. This conclusion provides the theory basisfor monitoring the growth state of winter wheat through the canopy spectral information.
(2) The correlationships between canopy original spectrum, derivative spectrum and leafSPAD value, percentage vegetation cover, LAI were analuzed respectively. The originalspectral reflectance of green-turning, jointing, heading, and pustulation stages was negativelycorrelated with leaf SPAD value, percentage vegetation cover, and LAI in visible spectrum. Inthe “red stage”, the negative correlation turns to a positive one. The correlation coefficients ofderivative spectrum and leaf SPAD value, percentage vegetation cover, LAI were higher thanthose of original spectrum and SPAD value in some wavebands in green-turning period,jointing, heading, and pustulation stages. There were low correlationship between originalspectrum, derivative spectrum and leaf SPAD value, percentage vegetation cover and LAI atmaturation stage. However, when generating the estimation models for leaf SPAD value,percentage vegetation cover and leaf area index, the spectral data in the maturation stagecould not be used.
(3) The data including canopy spectrum of winter wheat, leaf SPAD value, percentagevegetation cover and LAI in2010, were used to establish the estimation models based onNDVI, RVI, original spectral characteristic parameters (green peak reflectance, green peakposition, green peak area, red ebb reflectance, red ebb position, red ebb area, the ratio andnormalization value of green peak reflectance and red ebb reflectance, green peak skewness,green peak kurtosis, red ebb skewness, red ebb kurtosis, the ratio and normalization value ofgreen peak skewness and red ebb skewness, the ratio and normalization value of green peakkurtosis and red ebb kurtosis), red edge parameters (red edge position, red edge amplitude,red edge area, red edge kurtosis, and red edge skewness), wavelet energy coefficient, leafSPAD of wavelet fractal dimension, percentage vegetation cover and LAI. The precision ofestablished models was tested by using the data of canopy spectrum, leaf SPAD, percentagevegetation cover and LAI in2011. The results showed that the estimation precision of leafSPAD retrieved by NDVI was greater than that retrived by RVI. The inversion effect of NDVI was better than that of RVI in the green-turning and jointing stages, when retrieving thepercentage vegetation cover of winter wheat. However, better inversion was obtained by RVIin heading and postulation stages. Therefore, RVI should have the top priority when retrievingthe LAI, but red ebb area should have the priority when retrieving the leaf SPAD inverted bythe original spectrum characteristics in the jointing stage. Red ebb skewness should be takeninto account first in reviving and heading, and postulation stages. Percentage vegetation coverinversion based on parameters of original spectrum characteristic should give priority to thegreen peak kurtosis. For the inversion of LAI, the ratio of green peak reflectance and red ebbreflectance should be the prior considerations in the jointing stage, but green peak skewnessshould be considered first in the green-turning, heading, and postulation stages. Wheninverting the leaf SPAD based on the red edge parameters, the red edge kurtosis should betaken into account preferentially. For the percentage vegetation cover inversion based on thered edge parameters, red edge kurtosis should be a prime consideration in the jointing stage.Red edge skewness should be preferentially considered at other growth stages. Red edgekurtosis should be considered first in the inversion of LAI. The inversion of leaf SPAD basedon the wavelet energy coefficients should give first consideration to low frequencyinformation. However, high frequency information should be considered first for thepercentage vegetation cover inversion. The inversion of LAI based on wavelet energycoefficients should consider low frequency information first. The leaf SPAD, percentagevegetation cover, and LAI of winter wheat could be inverted effectively by the low frequencyinformation of wavelet fractal dimension in green-turning, jointing, heading, and postulationstages.
(4) The application of estimation model was evaluated based on leaf SPAD, percentagevegetation cover, and LAI of winter wheat in remote sensing data of ETM+. The resultsuggested that there was a significant correlationship between the predicted and measuredvalues when NDVI or RVI based on the constructed canopy spectrum was used to decode theremote sensing information of ETM+.
（1）研究了不同品种、不同水氮条件下冬小麦叶绿素含量、叶面积指数的变化以及不同播种密度、不同水分处理条件下冬小麦覆盖度的变化，并分析了冬小麦冠层高光谱特征的响应。结果表明：从返青到成熟，可见光区冬小麦冠层光谱反射率先减少后增大，近红外区先增大后减少。覆盖度、叶面积指数、叶绿素含量越大，冬小麦光谱反射率在可见光波段越小，在近红外波段越大。不同种植密度下，适宜供水冬小麦在可见光波段的反射率依次小于轻度亏水、重度亏水条件下的冬小麦；在近红外波段，规律正好相反。在不同水分处理条件下，随着施氮量的增加，可见光部分反射率下降，近红外反射率呈上升趋势。此研究结果为进一步利用冬小麦冠层光谱信息监测冬小麦生长状况提供了理论基础。
（2）研究了冬小麦冠层原始光谱、导数光谱与冬小麦叶片绿度SPAD值、覆盖度和叶面积指数的相关关系。在可见光波段，返青期、拔节期、抽穗期与灌浆期冬小麦原始光谱反射率与叶片SPAD值、覆盖度、叶面积指数负相关，在“红边”处，由负相关变成正相关；返青期、拔节期、抽穗期与灌浆期导数光谱与SPAD值、覆盖度、叶面积指数之间的相关系数在一些波段处高于原始光谱与SPAD值、覆盖度、叶面积指数的相关系数；成熟期冬小麦原始光谱、导数光谱与叶片SPAD值、覆盖度、叶面积指数相关性低，建立冬小麦叶片SPAD值、覆盖度、叶面积指数高光谱估算模型时，不能使用成熟期光谱数据。
（3）应用2010年冬小麦冠层光谱与冬小麦叶片SPAD值、覆盖度、叶面积指数数据，分别建立了返青期、拔节期、抽穗期与灌浆期冬小麦基于植被指数（NDVI、RVI）、原始光谱特征参数（绿峰反射率、绿峰位置、绿峰面积、红谷反射率、红谷位置、红谷面积、绿峰反射率与红谷反射率比值与归一化值，绿峰偏度、绿峰峰度、红谷偏度、红谷峰度，绿峰偏度与红谷偏度比值与归一化值、绿峰峰度与红谷峰度比值与归一化值）、红边参数（红边位置、红边振幅、红边面积，红边峰度、红边偏度）、小波能量系数、小波分形维数的叶片SPAD、覆盖度、叶面积指数估算模型，并应用2011年冬小麦冠层光谱与冬小麦叶片SPAD值、覆盖度、叶面积指数数据对所建模型进行了精度检验。结果表明：运用NDVI、RVI植被指数反演冬小麦SPAD值时，NDVI植被指数预测精度更高。运用NDVI、RVI植被指数反演冬小麦覆盖度时，在返青期和拔节期，使用NDVI植被指数反演效果好，在抽穗期、灌浆期，使用RVI植被指数反演效果好。运用NDVI、RVI植被指数反演冬小麦叶面积指数时，优先考虑应用RVI植被指数；使用原始光谱特征参数反演冬小麦叶片SPAD值时，拔节期优先考虑红谷面积，返青期、抽穗期、灌浆期优先考虑红谷偏度。使用原始光谱特征参数反演冬小麦覆盖度时，优先考虑绿峰峰度。使用原始光谱特征参数反演冬小麦叶面积指数时，拔节期优先考虑绿峰反射率与红谷反射率比值，返青期、抽穗期、灌浆期优先考虑绿峰偏度；应用红边参数反演冬小麦叶片SPAD值时，优先考虑红边峰度。应用红边参数反演冬小麦覆盖度时，拔节期优先考虑红边峰度，其余时期优先考虑红边偏度。应用红边参数反演冬小麦叶面积指数时，优先考虑红边峰度；运用小波能量系数反演冬小麦叶片SPAD值时，优先考虑使用低频信息。运用小波能量系数反演冬小麦覆盖度时，优先考虑使用高频信息。运用小波能量系数反演冬小麦叶面积指数时，优先考虑使用低频信息。运用小波分形维数的低频信息可有效的反演返青期、拔节期、抽穗期与灌浆期冬小麦叶片SPAD值、覆盖度、叶面积指数。
（4）探讨了基于冠层植被指数冬小麦叶片SPAD值、覆盖度、叶面积指数估算模型在ETM+遥感数据上应用的可能性。结果表明：当将基于冠层光谱构建的植被指数应用于ETM+遥感数据时，预测值与实测值间相关系数均通过了显著性检验。
Precision agriculture is the basic way to realize the aims of low consumption, highefficiency, good quality, and friendly environment. Real-time information of growth state offarmland crops can be quickly obtained by remote sensing, which can provide importanttechnical support for the implementation of precision agriculture. Hyperspectral remotesensing can detect the successive spectral curve of ground objects with hyperspectralresolution in some spectral region and form the unique multi-dimensional spectrum space.Remote sensing application focuses on spreading spatial information on spectrum dimension,thereby obtaining more spectral information of object. The application of hyperspectralremote sensing can strengthen the monitoring ability of physiological ecological parametersand improve the monitoring accuracy of crop growth. Based on field experiments andregional sampling of winter wheat, this study analyzed the hyperspectral characteristics ofwheat canopy by comprehensive application of hyperspectral remote sensing, growth analysis,physiological ecology test, mathematical statistics, and wavelet transform for different sowingdensities, water treatments, species, and water and nitrogen treaments. Then the hyperspectralestimation models of chlorophyll content, percentage vegetation cover, and leaf area index(LAI) were established on the basis of vegetation index, spectral characteristic parameters,wavelet energy coefficient, and wavelet fractal at different growth stages. Finally, theestimation model of vegetation index was used to the ETM+satellite images of remotesensing. Then the actual application probability was tested, which provided the theory basisand key technology for dynamic monitoring of growth change and precise management. Themain results are as follows:
(1) Changes of chlorophyll content and LAI in different species and water and nitrogenlevels were studied as well as variation of percentage vegetation cover under different sowingdensities and water levels. Additionally, the response of hyperspectral characteristics of wheat canopy was analysed. The result showed that the spectral reflectance of visible spectrumreduced at first and then increased from wheat green-turning to maturation stage. However,the spectral reflectance of near infrared spectrum demonstrated an opposite tendency. Itdecreased under the visible spectrum and increased under near infrared spectrum as thepercentage vegetation cover, leaf area index and chlorophyll content became larger. Thespectral reflectance decreased with the increase of water shortage in the visible spectrum,while on the contrary it increased in near infrared spectrum. Following the increased nitrogenlevels, there is a declinen of spectral reflectance in visible spectrum and an increase in nearinfrared spectrum in different moisture conditions. This conclusion provides the theory basisfor monitoring the growth state of winter wheat through the canopy spectral information.
(2) The correlationships between canopy original spectrum, derivative spectrum and leafSPAD value, percentage vegetation cover, LAI were analuzed respectively. The originalspectral reflectance of green-turning, jointing, heading, and pustulation stages was negativelycorrelated with leaf SPAD value, percentage vegetation cover, and LAI in visible spectrum. Inthe “red stage”, the negative correlation turns to a positive one. The correlation coefficients ofderivative spectrum and leaf SPAD value, percentage vegetation cover, LAI were higher thanthose of original spectrum and SPAD value in some wavebands in green-turning period,jointing, heading, and pustulation stages. There were low correlationship between originalspectrum, derivative spectrum and leaf SPAD value, percentage vegetation cover and LAI atmaturation stage. However, when generating the estimation models for leaf SPAD value,percentage vegetation cover and leaf area index, the spectral data in the maturation stagecould not be used.
(3) The data including canopy spectrum of winter wheat, leaf SPAD value, percentagevegetation cover and LAI in2010, were used to establish the estimation models based onNDVI, RVI, original spectral characteristic parameters (green peak reflectance, green peakposition, green peak area, red ebb reflectance, red ebb position, red ebb area, the ratio andnormalization value of green peak reflectance and red ebb reflectance, green peak skewness,green peak kurtosis, red ebb skewness, red ebb kurtosis, the ratio and normalization value ofgreen peak skewness and red ebb skewness, the ratio and normalization value of green peakkurtosis and red ebb kurtosis), red edge parameters (red edge position, red edge amplitude,red edge area, red edge kurtosis, and red edge skewness), wavelet energy coefficient, leafSPAD of wavelet fractal dimension, percentage vegetation cover and LAI. The precision ofestablished models was tested by using the data of canopy spectrum, leaf SPAD, percentagevegetation cover and LAI in2011. The results showed that the estimation precision of leafSPAD retrieved by NDVI was greater than that retrived by RVI. The inversion effect of NDVI was better than that of RVI in the green-turning and jointing stages, when retrieving thepercentage vegetation cover of winter wheat. However, better inversion was obtained by RVIin heading and postulation stages. Therefore, RVI should have the top priority when retrievingthe LAI, but red ebb area should have the priority when retrieving the leaf SPAD inverted bythe original spectrum characteristics in the jointing stage. Red ebb skewness should be takeninto account first in reviving and heading, and postulation stages. Percentage vegetation coverinversion based on parameters of original spectrum characteristic should give priority to thegreen peak kurtosis. For the inversion of LAI, the ratio of green peak reflectance and red ebbreflectance should be the prior considerations in the jointing stage, but green peak skewnessshould be considered first in the green-turning, heading, and postulation stages. Wheninverting the leaf SPAD based on the red edge parameters, the red edge kurtosis should betaken into account preferentially. For the percentage vegetation cover inversion based on thered edge parameters, red edge kurtosis should be a prime consideration in the jointing stage.Red edge skewness should be preferentially considered at other growth stages. Red edgekurtosis should be considered first in the inversion of LAI. The inversion of leaf SPAD basedon the wavelet energy coefficients should give first consideration to low frequencyinformation. However, high frequency information should be considered first for thepercentage vegetation cover inversion. The inversion of LAI based on wavelet energycoefficients should consider low frequency information first. The leaf SPAD, percentagevegetation cover, and LAI of winter wheat could be inverted effectively by the low frequencyinformation of wavelet fractal dimension in green-turning, jointing, heading, and postulationstages.
(4) The application of estimation model was evaluated based on leaf SPAD, percentagevegetation cover, and LAI of winter wheat in remote sensing data of ETM+. The resultsuggested that there was a significant correlationship between the predicted and measuredvalues when NDVI or RVI based on the constructed canopy spectrum was used to decode theremote sensing information of ETM+.
引文
艾天成,李方敏,周治安,张敏,吴海荣.2000.作物叶片叶绿素含量与SPAD值相关性研究.湖北农学院学报,20(1):6~8
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