基于多源数据小麦白粉病遥感监测研究
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摘要
小麦白粉病是我国小麦生产中主要的病害之一,病害发生时,单叶尺度和冠层尺度均表现出一定的特征信息,能够通过高光谱特征表征和解译,适合于采用遥感方式进行的监测技术研究。本研究于2011年、2012年利用地面高光谱遥感和HJ-CCD影像对小麦田间白粉病的生理生化指标和病情严重度进行了定性或定量的反演评价。
(1)研究了小麦白粉病叶片和植株生理生化特征与病情严重度间的相关关系。小麦白粉病病情严重度与叶绿素含量、叶绿素密度间的差异达到显著或极显著水平。叶绿素含量与病情严重度间存在极显著的负相关关系,R2达到0.8466以上；叶绿素密度与病情指数间存在极显著的负相关关系,R2达到0.9725以上；叶片全氮含量与病情指数间达到极显著负相关关系(R2=0.8655)。叶片病情严重度与SPAD值呈显著负相关,其决定系数达到0.9257；叶片病情严重度与Ch1、NBI指标呈显著负相关(RCh1=-0.768*,RNBI=-0.632*),叶片病情严重度与Flav呈显著正相关(RFlav=0.727*)。通过高光谱技术对NBI指数进行反演和校正,LS-SVM建模得到的R和RMSEP分别为0.9475和1.0037,优于BPNN所建模型的预测效果。
尝试利用叶绿素成像荧光系统检测小麦白粉病叶片的生理状态和快速光响应曲线的变化规律,比较两种选择模式条件下,荧光成像特征的变化规律,比较了小麦病害叶片与健康叶片荧光成像的异质性,表明了受病害胁迫叶片,尤其是表面存在病斑的叶片,荧光参数间存在较高异质性。提出对光曲线拟合方程进行一阶求导得到曲线斜率方程,来表征快速光曲线的动态变化,明确基于荧光成像系统检测小麦白粉病叶片的合理性。
(2)分析了叶片光谱敏感度和原始光谱对不同病情等级叶片尺度的响应关系,明确了630-680nm波段作为小麦白粉病病害等级响应的最佳敏感波段。利用植被指数法、连续统去除法和小波变换处理来反演单叶尺度小麦白粉病病情严重度。初步筛选确定了16个基于高光谱数据构建的植被指数。除了RVSI和PSRI指数外,其他植被指数与病情严重度间相关系数呈显著性差异,其中指数GNDVI、NDVI、SR、PRI、 OSAVI、SAVI、SIPI、VARI、IWB、RVSI和PSRI与病情严重度呈负相关关系,其他植被指数与病情严重度呈正相关关系。以AAI为自变量建立回归模型,取得了更好的反演效果(R2=0.9195,F=251.3122)。基于db6小波系对病叶原始光谱进行降维处理,筛选出了7个通过0.01水平检验的小波能量系数,作为小麦白粉病严重度的识别监测处理。归纳总结了共48个光谱特征与病情严重度(DI)的相关分析结果并对估算模型进行精度检验。其中,41个特征参数均与DI有显著的相关性达到极显著。
(3)研究了冠层尺度小麦白粉病不同病情指数光谱响应特征,通过植被指数和病情指数相关性分析,筛选出小麦白粉病病情指数反演监测模型。选取测试的25个植被指数光谱特征中,除了冠层尺度植被指数PSRI和WI与病情指数间相关性不显著外,其余23个冠层尺度植被指数光谱特征均与小麦白粉病病情指数间显著相关。基于选用基于负熵为信号统计量的FastICA算法对高光谱数据进行降维分析,基于PLS提出多特征变量的小麦白粉病病情指数监测模型,并对可见光、近红外波段的数据进行分离,构建小麦冠层病害胁迫特征三维空间分布模型。经复相关系数和均方根误差判别,RIC1-NIRICl-λrep模拟方程具有较高的复相关系数和较低的均方根误差,其模型预测值具有较好的精度。
(4)通过变化向量分析,对冬小麦白粉病害灾情进行遥感监测,利用冬小麦染病面积与绿波段归一化植被指数的响应关系,重新定义了矢量空间,构建了冬小麦白粉病的遥感监测模型。采用多种植被指数建立空间变化向量分析,通过分析6种植被指数的变化范围,得出了绿波段归一化植被指数对小麦白粉病更为敏感,该指数可用来建立小麦白粉病的监测模型。通过分析3个地区的病害遥感监测图,得出晋州北部地区病害较严重,宁晋县中部有中度的病害,而藁城市北部和中部则有大面积的病害发生,应根据3个地区不同的病害程度,进行喷药控制,以降低病害带来的损失。
Wheat powdery mildew is one of the major diseases in wheat production in China. The leaf and canopy scale were both showed some characteristic information when this disease occurred. As such information can be interpreted by the hyperspectral feature, this disease is very suitable for monitoring by using remote sensing. In this paper, the physiological and biochemical parameters of wheat powdery mildew had been qualitative or quantitative inverted by hyperspectral data and HJ-CCD image, which were acquired in2011and2012.
(1) The correlation between physiological and biochemical characteristics and disease severity of wheat powdery mildew had been analyzed. The results showed that wheat powdery mildew disease severity and chlorophyll concentration had a significantly negative correlation (R2>0.8466), meanwhile the highly negative correlation (R2>0.9725) had also been found between disease severity and chlorophyll density. In addition, the disease severity and leaf total nitrogen, SPAD, Chi, NBI were showed negative correlation, the coefficient of determination were0.8655,0.9257,0.768and0.632, respectively. Finally, the disease severity and Flav had a significant positive correlation (RFiav=0.727*). Through the inversion and calibration for NBI index by hyperspectral technology, the R and RMSEP of LS-SVM had reached0.9475and1.0037, which is better than the BPNN.
The variation between physiological state and fast light response curve had been measured by using chlorophyll fluorescence imaging system. The fluorescence imaging feature, heterogeneity between disease and health leaves had been compared under two imaging modes. The results showed that the fluorescence parameters had high heterogeneity when the leaves had been infected, especially some disease spot leaves. The dynamic change of light response curves had been obtained by the slope equation of curves which had been fitted by first-order derivative. These results indicated the rationality of the detection of powdery mildew of wheat leaf based on fluorescence imaging system.
(2) The response between hyperspectral reflectance and different disease severity had been analyzed, the best sensitive waveband (630-680nm) had also been identified for detecting the wheat powdery mildew. The disease severity based on the leave level was inverted by using vegetation index, the continuum transformation and wavelet transformation method. Sixteen hyperspectral vegetation index had been used to invert the disease severity, the correlation coefficient all passed the significance test (P-value<0.05) except RVSI. PSRI. GNDVI、NDVI、SR、PRI、OSAVI、SAVI、SIPI、VARI、IWB、 RVSI and PSRI had showed negative correlation, whereas the other indexes had positive correlation. The regression model built by AAI has a better inversion result (R2=0.9195, F=0.9195). The original spectrum dimension had been reduced by db6wavelet. Seven wavelet energy coefficients passed the significance test (P-value<0.01). The DI and48spectral features had been analyzed, and the model precision had also been evaluated. The results show that41spectral feature parameters has significantly correlation with DI.
(3) The spectral response between disease severity and hyperspectral reflectance based on the canopy scale had also been analyzed, the inversion model of wheat powdery mildew had been built through the correlation analysis between vegetation index and the disease index.25vegetation indexes which were selected had showed significant correlation with the DI, except the PSRI and WI. The reduction of dimension of hyperspectral data were based on the FastICA algorithm, then the monitoring model of wheat powdery mildew disease index was built by PLS. This model can separate the data in visible and near infrared waveband, thus another three dimensional space distribution model of disease stress characteristics can be built. RIC1-NIRIC1-λrep simulation equation had a high correlation coefficient and low mean square error through the discrimination of these two statistical parameters, and a good accuracy can be obtained by this equation.
(4) The remote sensing monitoring model of wheat powdery mildew had been built by the change vector analysis, the method used the response relationship between wheat infected area and GNDVI, and redefined the vector space. Several vegetation indexes had been employed for the change vector analysis, the GNDVI was more sensitive to the wheat powdery mildew disease by calculating the variation range of6vegetation indexes. By means of analyzing the disease of three region, the northern region of JinZhou city had a severe disease, the central region of NingJin county had moderate disease, and the large area disease had been found in the central of GaoCheng city. These results showed that the pesticide should be applied in these area for lowing the the loss of wheat.
(1)研究了小麦白粉病叶片和植株生理生化特征与病情严重度间的相关关系。小麦白粉病病情严重度与叶绿素含量、叶绿素密度间的差异达到显著或极显著水平。叶绿素含量与病情严重度间存在极显著的负相关关系,R2达到0.8466以上；叶绿素密度与病情指数间存在极显著的负相关关系,R2达到0.9725以上；叶片全氮含量与病情指数间达到极显著负相关关系(R2=0.8655)。叶片病情严重度与SPAD值呈显著负相关,其决定系数达到0.9257；叶片病情严重度与Ch1、NBI指标呈显著负相关(RCh1=-0.768*,RNBI=-0.632*),叶片病情严重度与Flav呈显著正相关(RFlav=0.727*)。通过高光谱技术对NBI指数进行反演和校正,LS-SVM建模得到的R和RMSEP分别为0.9475和1.0037,优于BPNN所建模型的预测效果。
尝试利用叶绿素成像荧光系统检测小麦白粉病叶片的生理状态和快速光响应曲线的变化规律,比较两种选择模式条件下,荧光成像特征的变化规律,比较了小麦病害叶片与健康叶片荧光成像的异质性,表明了受病害胁迫叶片,尤其是表面存在病斑的叶片,荧光参数间存在较高异质性。提出对光曲线拟合方程进行一阶求导得到曲线斜率方程,来表征快速光曲线的动态变化,明确基于荧光成像系统检测小麦白粉病叶片的合理性。
(2)分析了叶片光谱敏感度和原始光谱对不同病情等级叶片尺度的响应关系,明确了630-680nm波段作为小麦白粉病病害等级响应的最佳敏感波段。利用植被指数法、连续统去除法和小波变换处理来反演单叶尺度小麦白粉病病情严重度。初步筛选确定了16个基于高光谱数据构建的植被指数。除了RVSI和PSRI指数外,其他植被指数与病情严重度间相关系数呈显著性差异,其中指数GNDVI、NDVI、SR、PRI、 OSAVI、SAVI、SIPI、VARI、IWB、RVSI和PSRI与病情严重度呈负相关关系,其他植被指数与病情严重度呈正相关关系。以AAI为自变量建立回归模型,取得了更好的反演效果(R2=0.9195,F=251.3122)。基于db6小波系对病叶原始光谱进行降维处理,筛选出了7个通过0.01水平检验的小波能量系数,作为小麦白粉病严重度的识别监测处理。归纳总结了共48个光谱特征与病情严重度(DI)的相关分析结果并对估算模型进行精度检验。其中,41个特征参数均与DI有显著的相关性达到极显著。
(3)研究了冠层尺度小麦白粉病不同病情指数光谱响应特征,通过植被指数和病情指数相关性分析,筛选出小麦白粉病病情指数反演监测模型。选取测试的25个植被指数光谱特征中,除了冠层尺度植被指数PSRI和WI与病情指数间相关性不显著外,其余23个冠层尺度植被指数光谱特征均与小麦白粉病病情指数间显著相关。基于选用基于负熵为信号统计量的FastICA算法对高光谱数据进行降维分析,基于PLS提出多特征变量的小麦白粉病病情指数监测模型,并对可见光、近红外波段的数据进行分离,构建小麦冠层病害胁迫特征三维空间分布模型。经复相关系数和均方根误差判别,RIC1-NIRICl-λrep模拟方程具有较高的复相关系数和较低的均方根误差,其模型预测值具有较好的精度。
(4)通过变化向量分析,对冬小麦白粉病害灾情进行遥感监测,利用冬小麦染病面积与绿波段归一化植被指数的响应关系,重新定义了矢量空间,构建了冬小麦白粉病的遥感监测模型。采用多种植被指数建立空间变化向量分析,通过分析6种植被指数的变化范围,得出了绿波段归一化植被指数对小麦白粉病更为敏感,该指数可用来建立小麦白粉病的监测模型。通过分析3个地区的病害遥感监测图,得出晋州北部地区病害较严重,宁晋县中部有中度的病害,而藁城市北部和中部则有大面积的病害发生,应根据3个地区不同的病害程度,进行喷药控制,以降低病害带来的损失。
Wheat powdery mildew is one of the major diseases in wheat production in China. The leaf and canopy scale were both showed some characteristic information when this disease occurred. As such information can be interpreted by the hyperspectral feature, this disease is very suitable for monitoring by using remote sensing. In this paper, the physiological and biochemical parameters of wheat powdery mildew had been qualitative or quantitative inverted by hyperspectral data and HJ-CCD image, which were acquired in2011and2012.
(1) The correlation between physiological and biochemical characteristics and disease severity of wheat powdery mildew had been analyzed. The results showed that wheat powdery mildew disease severity and chlorophyll concentration had a significantly negative correlation (R2>0.8466), meanwhile the highly negative correlation (R2>0.9725) had also been found between disease severity and chlorophyll density. In addition, the disease severity and leaf total nitrogen, SPAD, Chi, NBI were showed negative correlation, the coefficient of determination were0.8655,0.9257,0.768and0.632, respectively. Finally, the disease severity and Flav had a significant positive correlation (RFiav=0.727*). Through the inversion and calibration for NBI index by hyperspectral technology, the R and RMSEP of LS-SVM had reached0.9475and1.0037, which is better than the BPNN.
The variation between physiological state and fast light response curve had been measured by using chlorophyll fluorescence imaging system. The fluorescence imaging feature, heterogeneity between disease and health leaves had been compared under two imaging modes. The results showed that the fluorescence parameters had high heterogeneity when the leaves had been infected, especially some disease spot leaves. The dynamic change of light response curves had been obtained by the slope equation of curves which had been fitted by first-order derivative. These results indicated the rationality of the detection of powdery mildew of wheat leaf based on fluorescence imaging system.
(2) The response between hyperspectral reflectance and different disease severity had been analyzed, the best sensitive waveband (630-680nm) had also been identified for detecting the wheat powdery mildew. The disease severity based on the leave level was inverted by using vegetation index, the continuum transformation and wavelet transformation method. Sixteen hyperspectral vegetation index had been used to invert the disease severity, the correlation coefficient all passed the significance test (P-value<0.05) except RVSI. PSRI. GNDVI、NDVI、SR、PRI、OSAVI、SAVI、SIPI、VARI、IWB、 RVSI and PSRI had showed negative correlation, whereas the other indexes had positive correlation. The regression model built by AAI has a better inversion result (R2=0.9195, F=0.9195). The original spectrum dimension had been reduced by db6wavelet. Seven wavelet energy coefficients passed the significance test (P-value<0.01). The DI and48spectral features had been analyzed, and the model precision had also been evaluated. The results show that41spectral feature parameters has significantly correlation with DI.
(3) The spectral response between disease severity and hyperspectral reflectance based on the canopy scale had also been analyzed, the inversion model of wheat powdery mildew had been built through the correlation analysis between vegetation index and the disease index.25vegetation indexes which were selected had showed significant correlation with the DI, except the PSRI and WI. The reduction of dimension of hyperspectral data were based on the FastICA algorithm, then the monitoring model of wheat powdery mildew disease index was built by PLS. This model can separate the data in visible and near infrared waveband, thus another three dimensional space distribution model of disease stress characteristics can be built. RIC1-NIRIC1-λrep simulation equation had a high correlation coefficient and low mean square error through the discrimination of these two statistical parameters, and a good accuracy can be obtained by this equation.
(4) The remote sensing monitoring model of wheat powdery mildew had been built by the change vector analysis, the method used the response relationship between wheat infected area and GNDVI, and redefined the vector space. Several vegetation indexes had been employed for the change vector analysis, the GNDVI was more sensitive to the wheat powdery mildew disease by calculating the variation range of6vegetation indexes. By means of analyzing the disease of three region, the northern region of JinZhou city had a severe disease, the central region of NingJin county had moderate disease, and the large area disease had been found in the central of GaoCheng city. These results showed that the pesticide should be applied in these area for lowing the the loss of wheat.
引文
[1]黄文江.作物病害遥感监测机理与应用[M].北京：中国农业科学技术出版社,2009.
[2]钱拴,霍治国,叶彩玲.我国小麦白粉病发生流行的长期气象预测研究.自然灾害学报,2005,14(4)：56-63.
[3]乔红波.麦蚜和白粉病遥感监测技术研究,2007,博士学位论文.北京：中国农业科学院.
[4]曹学仁.小麦白粉病的遥感监测及捕捉器中孢子的Real-time PCR定量检测.2012,博士学位论文.北京：中国农业科学院.
[5]Bennett F. G. A. Resistance to powdery mildew in wheat:a review of its use in agriculture and breeding programmes. Plant Pathology,1984,33:279-300.
[6]李伯宁.基于GIS的小麦白粉病的越夏和越冬区划.2008,硕士学位论文.北京：中国农业科学院.
[7]全国农业技术推广服务中心病虫害测报处.2011年全国小麦重大病虫害发生趋势预报.种业导刊,2011,2：13-16.
[8]何家泌,宋玉立,张忠山等.小麦白粉病及其防治Ⅰ小麦白粉病的分布、症状和危害.河南农业科学,1998,1：17-18.
[9]Allen,T.R.,Kupfer,J.A..Application of spherical statistics to change vector analysis of Landsat data:Southern Appalachian spruce-fir forests [J]. Remote Sensing of Environment,2000,74 (3):482-493.
[10]Rabbinge R., Jorritsma I.T.M., Schans J. Damage components of powdery mildew in winter wheat. Netherand Journal of Plant Pathology,1985,91:235-247
[11]Baenziger E. S., Kilpatrick R. A., Moseman J. G. Reduced root and shoot growth caused by Erysiphe graminis tritici in related wheat grown in nutrient solution culture. Canadian Journal of Botany,1979,57:1345-1348.
[12]Royse D. J., Gregory L. V., Ayers J. E., et al. Powdery mildew of wheat:Relation of yield components to disease severity. Canadian Journal of Plant Pathology,1980,2: 131-136.
[13]Bowen K. L., Everts K. L., Leath S. Reduction in yield of winter wheat in North Carolina due to powdery mildew and leaf rust. Phytopathology,1991,81(5):503-511.
[14]Persaud R. R., Lipps P. E. Virulence genes and virulence gene frequencies of Blumeria graminis f.sp. tritici in Ohio. Plant Disease,1995,79:494-499.
[15].李振岐.麦类病害.北京：中国农业出版社,1997.
[16]曹学仁,周益林,段霞瑜,等.利用高光谱遥感估计白粉病对小麦产量及蛋白质含量的影响.植物保护学报,2009,36(1)：32-36.
[17]Mizrach, A.. Determination of avocado and mango fruit properties by ultrasonic technique [J].Ultrasonics,2000,38(1-8):717-722.
[18]Walcott, R. R., McGee, D. C., Misra, M. K.. Detection of internal defects in potato based on ultrasound attenuation [J]. Plant Disease,1998,82(5):584-589.
[19]Ogawa, Y., Kondo, N., Shibusawa, S.. Inside quality evaluation of fruit by X-ray image [C]. Advanced Intelligent Mechatronics,2003,2:1360-1365.
[20]Barcelon, E.. X-ray Computed Tomography for Internal Quality Evaluation of Peaches [J]. Journal of Agricultural Engineering Research,1999,73(4):323-330.
[21]Barcelon, E. G., Tojo, S., Watanabe, K.. Relating X-ray absorption and some quality characteristics of mango fruit (Mangifera indica L.) [J]. Journal of Agricultural and Food Chemistry,1999,47(9):3822-3825.
[22]Maharajan, A., Latha, S., Sathyanarayana, N., et al.. X-ray analysis:a non-destructive detection of stone weevil, Sternochetus mangiferae in mango fruits [J]. Indian Journal of Plant Protection,2005,33(2):164-166.
[23]Finney, E. E., Norris, K. H.. X-ray scans for detecting hollow heart in potatoes American [J]. Journal of Potato Research,1978,55(2):95-105.
[24]Galed, G., Fernandez-Valle, M. E., Martinez, A., et al.. Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions [J]. Magnetic Resonance Imaging,2004,22(1):127-137.
[25]Saltveit Jr., M. E.. Determining tomato fruit maturity with nondestructive in vivo nuclear magnetic resonance imaging [J]. Postharvest Biology and Technology,1991, 1(2):153-159.
[26]Tu, S., Choi, Y., Mccarthy, M., et al.. Tomato quality evaluation by peak force and NMR spin-spin relaxation time [J]. Postharvest Biology and Technology,2007,44(2): 157-164.
[27]Clark, C. J., Hockings, P. D., Joyce, D. C., et al.. Application of magnetic resonance imaging to pre- and post-harvest studies of fruits and vegetables [J]. Postharvest Biology and Technology,1997,1(1):1-21.
[28]Thybo, A. K., Jespersen, S. N., Laerke, P. E., et al.. Nondestructive detection of internal bruise and spraing disease symptoms in potatoes using magnetic resonance imaging [J]. Magnetic Resonance Imaging,2004,22(9):1311-1317.
[29]Barker, J. R., Haws, M. J., Brown, R. E., et al.. Magnetic resonance imaging of severe frostbite injuries [J]. Annals of Plastic Surgery.1997,38(3):275-9.
[30]Clark, C. J., Hockings, P. D., Joyce, D. C., et al.. Application of magnetic resonance imaging to pre- and post-harvest studies of fruits and vegetables[J].Postharvest Biology and Technology,1997,1(1):1-21.
[31]P. Chen, M. J. McCarthy, R. Kauten, et al.. Maturity Evaluation of Avocados by NMR Methods [J]. Journal of Agricultural Engineering Research,1993,55(3):177-187.
[32]Andaur, J. E., Guesalaga, A. R., Agosin, E. E., et al.. Magnetic resonance imaging for nondestructive analysis of wine grapes [J]. Journal of Agricultural and Food Chemistry,2004,52(2):165-170.
[33]Krause GH and Weis E. Chlorophyll fluorescence and phot osynthesis:the basics. Annu Rev Plant Physiol Plant Mol Biol,1991,42:319-349
[34]Krause GH, Wei E. Chlorophyll fluorescence and photosysthesis:The basis[J]. Ann Rev Plant Physiol Plant Mol Biol,1991,42:313-349.
[35]Li X, Jiao DM, Liu YL, et al. Chlorophyll fluorescence and membrane lipid peroxidation in the flag leaves of different high yield rice variety at late stage of development under national condition[J]. ActaBotanicaSinica,2002,44(4):413-421.
[36]董彩霞,田纪春,赵世杰.不同形态氮素对高蛋白小麦幼苗叶绿素荧光特性的影响[J].西北植物学报,2002,22(2)：229-234.
[37]郑国华.炭疽病侵染对枇杷叶片H202含量和叶绿素荧光参数的影响[J].福建农业大学学报,2001,30(3)：353-356.
[38]王北洪,黄木易,马智宏,等.条锈病对冬小麦叶绿素荧光、光合及蒸腾作用的影响[J].华北农学报,2004,19(2)：92-94.
[39]Moshou, D., Bravo, C., Oberti, R., et al.. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps [J]. Real-Time Imaging,2005,11(2):75-83.
[40]Taubenhaus J. J., Ezekiel W. N., Neblette C. B. Airplane photography in the study of cotton root rot.Phytopathology,1929,19:1025-1029.
[41]唐延林,黄敬峰.农业高光谱遥感研究的现状与发展趋势.遥感技术与应用,2001,16(4)：248-251.
[42]梅安新,彭望碌,秦其明,等.遥感导论.北京：高等教育出版社,2001.
[43]Adams, M. L., Philpot, W. D., & Norvell, W. A. Yellowness index:an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing,1999,18,3663-3675.
[44]Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., & McMurtrey, J.E. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR). In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, France:Val d'Isere,1994,299-306.
[45]Steddom K., Heidel G., Jones D., et al. Remote detection of rhizomania in sugar beets.Phytopathology,2003,93(6):720-726.
[46]黄木易,王纪华,黄文江,等.冬小麦条锈病的光谱特征及遥感监测.农业工程学报,2003,19(6)：154-158.
[47]Liu, Z.Y., Wu, H.F., & Huang, J.F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture,2010,72,99-106.
[48]Huang, W.J., Huang, M.Y., Liu, L.Y., Wang, J.H., Zhao, C.J., & Wang, J.D. Inversion of the severity of winter wheat yellow rust using proper hyper spectral index. Transactions of the CSAE,2005,21(4),97-103.
[49]Muhammed, H.H. Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat. Biosystems Engineering,2005,91 (1),9-20.
[50]蔡成静,马占鸿,王海光,等.小麦条锈病高光谱近地与高空遥感监测比较研究[J].植物病理学报,2007,37(1)：77-82
[51]Jones, C.D., Jones, J.B., & Lee, W.S. Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture,2010,74(2),329-335.
[52]Yang, C.H., Everitt, J.H., & Fernandez, C.J. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems engineering, 2010,107(2),131-139.
[53]Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., & Ramon, H. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44,173-188.
[54]Huang, W. J., David, W. L., Niu, Z., Zhang, Y. J., Liu, L.Y., & Wang, J. H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture,2007,8,187-197.
[55]徐冠华.论热红外遥感中的基础研究[J].中国科学E辑2000(S1).
[56]田国良.热红外遥感[M].电子工业出版社2006.
[57]Chaerle,L.,Van Caeneghem,W.,Messens,etal..Presymptomatic visualization of plant-virus interactions by thermography [J]. Nature Biotechnology,1999,17(8): 813-816.
[58]Chaerle,L.,Hulsen,K.,Hermans,C.,etal..Robotized time-lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation [J]. Physiologia Plantarum,2003,118(4):613-619.
[59]Chaerle, L., Hulsen, K., Hermans, C., et al.. Robotized time-lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation [J]. Physiologia Plantarum,2003,118(4):613-619.
[60]Oerke,E.C..Croplossestopests[J]. The Journal of Agricultural Science,2005,144(01): 31.
[61]赵英时.遥感应用分析原理与方法[M].北京：科学出版社,2003.
[62]黄木易.冬小麦条锈病害的高光谱遥感监测[D].安徽：安徽农业大学,2004.
[63]Polischuk V. P., Shadchina T. M., Kompanetz T. I., et al. Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection,1997,31(1):115-119.
[64]Kobayashi, T., Kanda, E., & Kitada, K. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. The American Phytopathological Society,2001,91 (3),316-323.
[65]Kobayashi T., Kanda E., Naito S., et al. Ratio of rice reflectance for estimating leaf blast severitywith a multispectral radiometer. Journal of General Plant Pathology, 2003,69:17-22.
[66]Hansen J. G. Use of multispectral radiometry in wheat yellow rust experiments. EPPO Bulletin,1991,21:651-658.
[67]Bravo C, Moshou D., West J. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering,2003,84(2):137-145.
[68]Moshou D., Bravo C., West J., et al. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44:173-188.
[69]Moshou D., Bravo C., Oberti R., et al. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering, 2011,108:311-321.
[70]Steddom K., Heidel G., Jones D., et al. Remote detection of rhizomania in sugar beets. Phytopathology,2003,93(6):720-726.
[71]Muhammed H. H., Larsolle A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosystems Engineering,2003,86(2):125-134.
[72]Larsolle A., Muhammed H. H. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture,2007,8:37-47.
[73]Nicolas H. Using remote sensing to determine of the date of a fungicide application on winter wheat. Crop Protection,2004,23(2):853-863.
[74]Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici)and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1:275-288.
[75]Huang J. F., Apan A. Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. Journal of Spatial Science, 2006,51:129-142.
[76]Bauriegel E., Giebel A., Geyer M., et al. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture,2011, 75:304-312.
[77]Ray S. S., Jain N., Arora R. K., et al. Utility of hyperspectral data for potato late blight disease detection. Journal of Indian Society of Remote Sensing,2011,39(2):161-169.
[78]阿布都瓦斯提.吾拉木.(2006).基于n维光谱特征空间的农田干旱遥感监测.北京大学,博士论文.
[79]黄敬峰,王福民,王秀珍.水稻高光谱遥感实验研究.浙江：浙江大学出版社,2010：1-7：136-152.
[80]Lillesand, T. M., Kiefer, R. W.. Remote sensing and image interpretation (third edition) [M]. John Wiley & Sons, New York:Inc.,1994.
[81]刘占宇,孙华生,黄敬峰.基于学习矢量量化神经网络的水稻白穗和正常穗的高光谱识别[J].中国水稻科学,2007,21(6)：658-666.
[82]F. Boochs, G. Kupfer, K. Dockter, W. Ktihbauch, Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing,1990,11(10): 1741-1753.
[83]马伟,赵珍梅,刘翔,闫东川.植被指数与地表温度定量关系遥感分析——以北京市TM数据为例.国土资源遥感,2010,4(3)：108-112.
[84]张竞成.多源遥感数据小麦病害信息提取方法研究[D],浙江,浙江大学,2012.
[85]Lenk, S., Chaerle, L., Pfiindel, E. E., et al.. Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications [J]. Journal of Experimental Botany,2007,58(4):807-814.
[86]West, J.S., Bravo, C., Oberit, R., et al.The potential of optical canopy measurement for targeted control of field crop diseases [J]. Annual Review of Phytopathology, 2003,41:593-614.
[87]陈维君,周启发,黄敬峰.用高光谱植被指数估算水稻乳熟后叶片和穗的色素含量.中国水稻科学,2006,20(4)：434-439.
[88]Shizhou Du, Wenjiang Huang, Rongfu Wang, Ju-hua Luo, Jinling Zhao, Lin Yuan. Application of Continuum Removal Method for Estimating Disease Severity Level of Wheat Powdery Mildew. Advanced Materials Research,Vo 396-398:2012-2017.
[89]王大成,王纪华,李存军,张东彦,黄敬峰.基于ANN和传统算法估算小麦叶绿素密度的比较.农业工程学报,2008,24 SUPP.2,196-201.
[90]袁志发,周静芋.多元统计分析.北京：科学出版社.2002.
[91]Ausmus B. S., Hilty J. W. Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves. Remote Sensing of Environment,1972,2:77-81.
[92]Nutter F. V. Detection and measurement of plant disease gradients in peanut with a multispectral radiometer. Phytopathology,1989,79:958-963.
[93]Malthus T. J., Maderia A. C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment,1993, 45:107-116.
[94]Sasaki Y, Okamoto T, Imo K, et al. Automatic diagnosis of plant disease:ecognition between healthy and diseased leaf [J]. Journal of the Japanese Society of Agricultural Machinery,1999,61 (2):119-126.
[95]Kobayashi, T., Kanda, E., & Kitada, K. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. The American Phytopathological Society,2001,91(3),316-323.
[96]Zhang M. H., Qin Z. H., Liu X., et al. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observationand Geoinformation,2003,4:295-310.
[97]Graeff, S., Link, J., & Claupein, W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici)in wheat (Triticumaestivum L.) bymeans of leaf reflectance measurements. Central European Journal of Biology,2006,1,275-288.
[98]Daughtry C. S. T., Walthall C. L., Kim M. S., et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000,74:229-239.
[99]Moshou D., Bravo C., Wahlen S., et al. Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps. Precision Agriculture,2006,7:149-164.
[100]Yang C. M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11,61-81.
[101]Naidu, R.A., Perry, E.M., Pierce, F.J., & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66, 38-45.
[102]Liu, Z.Y., Wu, H.F., & Huang, J.F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture,2010,72,99-106.
[103]Mahlein A. K., Steiner U., Dehne H. W., et al. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture,2010, 11:413-431.
[104]吴曙雯,王人潮,陈晓斌,等.稻叶瘟对水稻光谱特性的影响研究.上海交通大学学报(农业科学版),2001,20(1)：73-76.
[105]黄木易,王纪华,黄文江,等.冬小麦条锈病的光谱特征及遥感监测.农业工程学报,2003,19(6)：154-158.
[106]刘良云,黄木易,黄文江,等.利用多时相的高光谱航空图像监测冬小麦条锈病.遥感学报,2004,8(3)：275-281.
[107]蔡成静,王海光,安虎,等.小麦条锈病高光谱遥感监测技术研究.西北农林科技大学学报(自然科学版),2005,33(增刊)：31-36.
[108]郭洁滨,黄冲,王海光,等.基于高光谱遥感技术的不同小麦品种条锈病病情指数的反演.光谱学与光谱分析,2009,29(12)：3353-3357.
[109]Chen B., Li S. K.., Wang K. R. Spectrum characteristics of cotton canopy infected with verticillium wilt and applications. Agricultural Sciences in China,2008,7(5): 561-569.
[110]Chen B., Li S. K., Wang K. R., et al. Evaluating the severity level of cotton Verticillium using spectral signature analysis. International Journal of Remote Sensing,2012,33(9):2706-2724.
[111]乔红波,简桂良,邹亚飞,等.枯萎病对不同抗性棉花光谱特性的影响.棉花学报,2007,19(2)：155-158.
[112]Jordan C. F. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666.
[113]Rouse J. W., Haas R. H., Schell J. A., et al. Monitoring vegetation systems in the Great Plains with ERTS, in Third ERTS Symp., NASA SP-351, U.S. Gov. Printing Office, Washington, DC, Vol.1,1973,309-317.
[114]Kaufman Y. J., Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS.IEEE Transactions on Geoscience and Remote Sensing,1992,30, 261-270.
[115]Gitelson A. A., Zur Y., Chivkunova O. B., et al. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology,2002, 75:272-281
[116]Huete A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment,1988,25:295-309.
[117]Qi J., Chehbouni A., Huete A. R., et al. A modified soil adjusted vegetation index. Remote Sensing of Environment,1994,48:119-126.
[118]Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., & Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum,1999,106,135-141.
[119]Merton, R., & Huntington, J. Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In Summaries of the Eight JPL Airborne Earth Science Workshop. Pasadena, CA:JPL Publication,1999, 299-307.
[120]Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering,2003,84, 137-145.
[121]Yang, C.M., Cheng, C.H., & Chen, R.K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science,2007, 47(1),329-335.
[122]Naidu, R.A., Perry, E.M., Pierce, F.J., & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66, 38-45.
[123]Steddom, K., Heidel, & G., Jones, D. Remote Detection of Rhizomania in Sugar Beets, phytopathology,2003,93(6),720-726.
[124]Zhao, C.J., Huang,M.Y., Huang, W.J., Liu, L.Y., & Wang, J.H. Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. IGARSS,2004,6(20-24),4318-4320.
[125]Huang, W.J., Huang, M.Y., Liu, L.Y., Wang, J.H., Zhao, C.J., & Wang, J.D. Inversion of the severity of winter wheat yellow rust using proper hyper spectral index. Transactions of the CSAE,2005,21(4),97-103.
[126]Huang, W. J., David, W. L., Niu, Z., Zhang, Y. J., Liu, L.Y., & Wang, J. H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements , and airborne hyperspectral imaging. Precision Agriculture,2007,8,187-197.
[127]Devadas R., Lamb D. W., Simpfendorfer S., et al. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 2009,10:459-470.
[128]蒋金豹,陈云浩,黄文江.用高光谱微分指数监测冬小麦病害的研究,光谱学与光谱分析,2007,27(12),2475-2479.
[129]罗菊花.基于多源数据的小麦蚜虫遥感监测预测研究[D],北京,北京师范大学,2012.
[130]靳宁.棉花黄萎病高光谱识别及遥感监测研究[D].江苏：南京信息工程大学,2009.
[131]Malthus T. J., Maderia A. C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment,1993, 45:107-116.
[132]Adams, M. L., Philpot, W. D., & Norvell, W. A. Yellowness index:an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing,1999,18,3663-3675.
[133]蒋金豹,陈云浩,黄文江,等.冬小麦条锈病严重度高光谱遥感反演模型研究.南京农业大学学报,2007,30：63-67.
[134]王圆圆,陈云浩,李京,黄文江.指示冬小麦条锈病严重度的两个新的红边参数.遥感学报,2007,11(6),875-881.
[135]Cho M A, Skidmore A K.A new technique for extracting the red edge position from hyperspectral data:The linear extrapolation method. Remote Sensing of Environment, 2006,101:181-193.
[136]Danielsen S., Munk L. Evaluation of disease assessment methods in quinoa for their ability to predict yield loss caused by downy mildew. Crop Protection,2004,23(3): 219-228.
[137]Moshou D., Bravo C., West J., et al. Automatic detection of'yellow rust'in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44:173-188.
[138]Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, A., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging,11, 75-83.
[139]刘占宇.水稻主要病虫害胁迫遥感监测研究[D].浙江：浙江大学浙江大学环资学院,2008.
[140]Yang C. H., Everitt J. H., Fernandez C. J. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Engineering,2010, 107:131-139.
[141]张瑞英.方差分析在小麦条锈病超长期预测中的应用[J].甘肃农业科技,2002,(1)：39-40.
[142]胡小平,杨之为,李振岐,邓志勇,柯长华.汉中地区小麦条锈病的BP神经网络预测[J].西北农业学报.2000,28(2)：18-21.
[143]王信群.模糊数学在病虫害测报中的应用[J].安徽农业科学.2005,33(3)：396-398.
[144]何家泌,宋玉立,张忠山,等.小麦白粉病及其防治I小麦白粉病的分布、症状和危害.河南农业科学,1998,1：17-18.
[145]李映霞.小麦抗白粉病机理的初步研究[D].湖北：华中农业大学,2004.
[146]Lorenzen B., Jensen A. Changes in spectral properties induced in barley by cereal powdery mildew.Remote Sensing of Environment,1989,27:201-209.
[147]乔红波,简桂良,邹亚飞,等.枯萎病对不同抗性棉花光谱特性的影响.棉花学 报,2007,19(2)：155-158.
[148]Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1:275-288.
[149]乔洪波,周益林,白由路,等.地面高光谱和低空遥感监测小麦白粉病初探.植物保护学报,2006,33(4)：341-344.
[150]Franke, J., & Menz, G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agr iculture,2007,8(3),161-172.
[151]Mewes T., Franke J., Menz G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agriculture,2011,12: 795-812.
[152]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京：高等教育出版社,2003.
[153]王磊,白由路,卢艳丽,等.光谱数据变换对玉米氮素含量反演精度的影响.遥感技术与应用,2011,26(2)：220-225.
[154]Wood C W, Reeves D W, Duffield R R, Edmisten K L. Field chlorophyll measurements for evaluation of corn nitrogen status. J Plant Nutr,1992,15:487-500.
[155]Arregui L M, Lasa B, Lafarga A, Iranneta I, Baroja E, Quemada M. Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions. Eur J Agron,2006,24:140-148.
[156]Tremblay N, Belec C. Adapting nitrogen fer tilization to unpre-dictable seasonal conditions with the least impact on the envi-ronment. Horttechnology,2006,16: 408-412.
[157]Richard B. Peterson & Evelyn A. H. The multiphasic nature of nonphotochemical quenching:implications for assessment of photosynthetic electron tran sport based on chlorophyll fluorescence Photosynthesis Research 82:95-107,2004.
[158]Demmig-Adams B, Adams W W, Baker D H, Logan B A, Bowling D R, Verhoreven A S. Using chlorophyll fluorescence to assess the fraction of absorbed light allocated to thermal dissipation of excess excitation. Physiol Plant,1996,98:253-264.
[159]Li C X,Zhong Z C,Liu Y. Effect of soil water change on photosynthetic characteristics of Taxodium distichum seedlings in the hydro-fluctuation belt of the Three Gorges Reservoir Area. Acta Ecologica Sinica,2005,25(8):1953-1959.
[160]Zhang YL,Hu YY,Luo HH,et al. Two distinct strate-gies of cotton and soybean differing in leaf movement to perform photosynthesis under drought in the field. Func-tional Plant Biology,2011,38:567-575.
[161]郭峰,曲妍妍,信长朋,等.弱光下生长的高产小麦品系PH01-35旗叶光合机构对不同光强的响应[J].作物学报,2009,35(1)：179-184.
[162]金铭,刘湘南,李铁瑛.基于冠层多维光谱的水稻镉污染胁迫诊断模型研究[J].中国环境科学2011,31(1)：137-143.
[163]杨福生,洪波.独立分量分析的原理与应用[M].北京：清华大学出版社,2006.
[164]Kaufman, Y. J., Gao, B. C. Remote Sensing of Water Vapor in the Near IR from EOS/MODIS[J]. IEEE Transactionson Geoscience and Remote Sensing,1992,30(5): 871-884.
[165]Liang, S. L., Fang, H. L., Chen, M. Z.. Atmospheric correction of landsat ETM+ land surface imagery-Part1:Methods[J]. IEEE Transactions on Geoscience and Remote Sensing,2001,39(11):2490-2498.
[166]S.Z.Li, et al.,"Learing Multiview Face Subspaces and Facial Pose Estimation using Independent Component Analysis", IEEE Trans. ImageProcessing, vol-14, No.6,pp.705-712,June 2005.
[167]Du P,Zhao HJ,et al. Independent component analysis for hyperspectral imagery plant classification, applications of neural networks and machine learning in image processing 2005,5673:71-81.
[168]林婷.水稻锌污染高光谱遥感监测模型研究[D].北京：中国地质大学.硕士学位论文.
[169]Jurgens, C. The modified normalized difference vegetation index (mNDVI):a new index to determine frost damages in agriculture based on Landsat TM data, International. Journal of Remote Sensing.,1997,18(17):3583-3594.
[170]顾晓鹤,宋国宝,韩立建等.基于变化向量分析的冬小麦长势变化监测研究.农业工程学报,2008,24(4)：159-165.
[171]陈晋,何春阳,卓莉.基于变化向量分析(CVA)的土地利用／覆盖变化动态监测(II)-变化类型的确定方法.遥感学报,2001,5(5)：346-352.
[172]张晓东,李德仁,龚健雅,等.遥感影像与GIS分析相结合的变化检测方法.武汉大学学报：信息科学版,2006,3(10)：266-269.
[2]钱拴,霍治国,叶彩玲.我国小麦白粉病发生流行的长期气象预测研究.自然灾害学报,2005,14(4)：56-63.
[3]乔红波.麦蚜和白粉病遥感监测技术研究,2007,博士学位论文.北京：中国农业科学院.
[4]曹学仁.小麦白粉病的遥感监测及捕捉器中孢子的Real-time PCR定量检测.2012,博士学位论文.北京：中国农业科学院.
[5]Bennett F. G. A. Resistance to powdery mildew in wheat:a review of its use in agriculture and breeding programmes. Plant Pathology,1984,33:279-300.
[6]李伯宁.基于GIS的小麦白粉病的越夏和越冬区划.2008,硕士学位论文.北京：中国农业科学院.
[7]全国农业技术推广服务中心病虫害测报处.2011年全国小麦重大病虫害发生趋势预报.种业导刊,2011,2：13-16.
[8]何家泌,宋玉立,张忠山等.小麦白粉病及其防治Ⅰ小麦白粉病的分布、症状和危害.河南农业科学,1998,1：17-18.
[9]Allen,T.R.,Kupfer,J.A..Application of spherical statistics to change vector analysis of Landsat data:Southern Appalachian spruce-fir forests [J]. Remote Sensing of Environment,2000,74 (3):482-493.
[10]Rabbinge R., Jorritsma I.T.M., Schans J. Damage components of powdery mildew in winter wheat. Netherand Journal of Plant Pathology,1985,91:235-247
[11]Baenziger E. S., Kilpatrick R. A., Moseman J. G. Reduced root and shoot growth caused by Erysiphe graminis tritici in related wheat grown in nutrient solution culture. Canadian Journal of Botany,1979,57:1345-1348.
[12]Royse D. J., Gregory L. V., Ayers J. E., et al. Powdery mildew of wheat:Relation of yield components to disease severity. Canadian Journal of Plant Pathology,1980,2: 131-136.
[13]Bowen K. L., Everts K. L., Leath S. Reduction in yield of winter wheat in North Carolina due to powdery mildew and leaf rust. Phytopathology,1991,81(5):503-511.
[14]Persaud R. R., Lipps P. E. Virulence genes and virulence gene frequencies of Blumeria graminis f.sp. tritici in Ohio. Plant Disease,1995,79:494-499.
[15].李振岐.麦类病害.北京：中国农业出版社,1997.
[16]曹学仁,周益林,段霞瑜,等.利用高光谱遥感估计白粉病对小麦产量及蛋白质含量的影响.植物保护学报,2009,36(1)：32-36.
[17]Mizrach, A.. Determination of avocado and mango fruit properties by ultrasonic technique [J].Ultrasonics,2000,38(1-8):717-722.
[18]Walcott, R. R., McGee, D. C., Misra, M. K.. Detection of internal defects in potato based on ultrasound attenuation [J]. Plant Disease,1998,82(5):584-589.
[19]Ogawa, Y., Kondo, N., Shibusawa, S.. Inside quality evaluation of fruit by X-ray image [C]. Advanced Intelligent Mechatronics,2003,2:1360-1365.
[20]Barcelon, E.. X-ray Computed Tomography for Internal Quality Evaluation of Peaches [J]. Journal of Agricultural Engineering Research,1999,73(4):323-330.
[21]Barcelon, E. G., Tojo, S., Watanabe, K.. Relating X-ray absorption and some quality characteristics of mango fruit (Mangifera indica L.) [J]. Journal of Agricultural and Food Chemistry,1999,47(9):3822-3825.
[22]Maharajan, A., Latha, S., Sathyanarayana, N., et al.. X-ray analysis:a non-destructive detection of stone weevil, Sternochetus mangiferae in mango fruits [J]. Indian Journal of Plant Protection,2005,33(2):164-166.
[23]Finney, E. E., Norris, K. H.. X-ray scans for detecting hollow heart in potatoes American [J]. Journal of Potato Research,1978,55(2):95-105.
[24]Galed, G., Fernandez-Valle, M. E., Martinez, A., et al.. Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions [J]. Magnetic Resonance Imaging,2004,22(1):127-137.
[25]Saltveit Jr., M. E.. Determining tomato fruit maturity with nondestructive in vivo nuclear magnetic resonance imaging [J]. Postharvest Biology and Technology,1991, 1(2):153-159.
[26]Tu, S., Choi, Y., Mccarthy, M., et al.. Tomato quality evaluation by peak force and NMR spin-spin relaxation time [J]. Postharvest Biology and Technology,2007,44(2): 157-164.
[27]Clark, C. J., Hockings, P. D., Joyce, D. C., et al.. Application of magnetic resonance imaging to pre- and post-harvest studies of fruits and vegetables [J]. Postharvest Biology and Technology,1997,1(1):1-21.
[28]Thybo, A. K., Jespersen, S. N., Laerke, P. E., et al.. Nondestructive detection of internal bruise and spraing disease symptoms in potatoes using magnetic resonance imaging [J]. Magnetic Resonance Imaging,2004,22(9):1311-1317.
[29]Barker, J. R., Haws, M. J., Brown, R. E., et al.. Magnetic resonance imaging of severe frostbite injuries [J]. Annals of Plastic Surgery.1997,38(3):275-9.
[30]Clark, C. J., Hockings, P. D., Joyce, D. C., et al.. Application of magnetic resonance imaging to pre- and post-harvest studies of fruits and vegetables[J].Postharvest Biology and Technology,1997,1(1):1-21.
[31]P. Chen, M. J. McCarthy, R. Kauten, et al.. Maturity Evaluation of Avocados by NMR Methods [J]. Journal of Agricultural Engineering Research,1993,55(3):177-187.
[32]Andaur, J. E., Guesalaga, A. R., Agosin, E. E., et al.. Magnetic resonance imaging for nondestructive analysis of wine grapes [J]. Journal of Agricultural and Food Chemistry,2004,52(2):165-170.
[33]Krause GH and Weis E. Chlorophyll fluorescence and phot osynthesis:the basics. Annu Rev Plant Physiol Plant Mol Biol,1991,42:319-349
[34]Krause GH, Wei E. Chlorophyll fluorescence and photosysthesis:The basis[J]. Ann Rev Plant Physiol Plant Mol Biol,1991,42:313-349.
[35]Li X, Jiao DM, Liu YL, et al. Chlorophyll fluorescence and membrane lipid peroxidation in the flag leaves of different high yield rice variety at late stage of development under national condition[J]. ActaBotanicaSinica,2002,44(4):413-421.
[36]董彩霞,田纪春,赵世杰.不同形态氮素对高蛋白小麦幼苗叶绿素荧光特性的影响[J].西北植物学报,2002,22(2)：229-234.
[37]郑国华.炭疽病侵染对枇杷叶片H202含量和叶绿素荧光参数的影响[J].福建农业大学学报,2001,30(3)：353-356.
[38]王北洪,黄木易,马智宏,等.条锈病对冬小麦叶绿素荧光、光合及蒸腾作用的影响[J].华北农学报,2004,19(2)：92-94.
[39]Moshou, D., Bravo, C., Oberti, R., et al.. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps [J]. Real-Time Imaging,2005,11(2):75-83.
[40]Taubenhaus J. J., Ezekiel W. N., Neblette C. B. Airplane photography in the study of cotton root rot.Phytopathology,1929,19:1025-1029.
[41]唐延林,黄敬峰.农业高光谱遥感研究的现状与发展趋势.遥感技术与应用,2001,16(4)：248-251.
[42]梅安新,彭望碌,秦其明,等.遥感导论.北京：高等教育出版社,2001.
[43]Adams, M. L., Philpot, W. D., & Norvell, W. A. Yellowness index:an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing,1999,18,3663-3675.
[44]Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., & McMurtrey, J.E. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR). In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, France:Val d'Isere,1994,299-306.
[45]Steddom K., Heidel G., Jones D., et al. Remote detection of rhizomania in sugar beets.Phytopathology,2003,93(6):720-726.
[46]黄木易,王纪华,黄文江,等.冬小麦条锈病的光谱特征及遥感监测.农业工程学报,2003,19(6)：154-158.
[47]Liu, Z.Y., Wu, H.F., & Huang, J.F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture,2010,72,99-106.
[48]Huang, W.J., Huang, M.Y., Liu, L.Y., Wang, J.H., Zhao, C.J., & Wang, J.D. Inversion of the severity of winter wheat yellow rust using proper hyper spectral index. Transactions of the CSAE,2005,21(4),97-103.
[49]Muhammed, H.H. Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat. Biosystems Engineering,2005,91 (1),9-20.
[50]蔡成静,马占鸿,王海光,等.小麦条锈病高光谱近地与高空遥感监测比较研究[J].植物病理学报,2007,37(1)：77-82
[51]Jones, C.D., Jones, J.B., & Lee, W.S. Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture,2010,74(2),329-335.
[52]Yang, C.H., Everitt, J.H., & Fernandez, C.J. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems engineering, 2010,107(2),131-139.
[53]Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., & Ramon, H. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44,173-188.
[54]Huang, W. J., David, W. L., Niu, Z., Zhang, Y. J., Liu, L.Y., & Wang, J. H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture,2007,8,187-197.
[55]徐冠华.论热红外遥感中的基础研究[J].中国科学E辑2000(S1).
[56]田国良.热红外遥感[M].电子工业出版社2006.
[57]Chaerle,L.,Van Caeneghem,W.,Messens,etal..Presymptomatic visualization of plant-virus interactions by thermography [J]. Nature Biotechnology,1999,17(8): 813-816.
[58]Chaerle,L.,Hulsen,K.,Hermans,C.,etal..Robotized time-lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation [J]. Physiologia Plantarum,2003,118(4):613-619.
[59]Chaerle, L., Hulsen, K., Hermans, C., et al.. Robotized time-lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation [J]. Physiologia Plantarum,2003,118(4):613-619.
[60]Oerke,E.C..Croplossestopests[J]. The Journal of Agricultural Science,2005,144(01): 31.
[61]赵英时.遥感应用分析原理与方法[M].北京：科学出版社,2003.
[62]黄木易.冬小麦条锈病害的高光谱遥感监测[D].安徽：安徽农业大学,2004.
[63]Polischuk V. P., Shadchina T. M., Kompanetz T. I., et al. Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection,1997,31(1):115-119.
[64]Kobayashi, T., Kanda, E., & Kitada, K. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. The American Phytopathological Society,2001,91 (3),316-323.
[65]Kobayashi T., Kanda E., Naito S., et al. Ratio of rice reflectance for estimating leaf blast severitywith a multispectral radiometer. Journal of General Plant Pathology, 2003,69:17-22.
[66]Hansen J. G. Use of multispectral radiometry in wheat yellow rust experiments. EPPO Bulletin,1991,21:651-658.
[67]Bravo C, Moshou D., West J. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering,2003,84(2):137-145.
[68]Moshou D., Bravo C., West J., et al. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44:173-188.
[69]Moshou D., Bravo C., Oberti R., et al. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engineering, 2011,108:311-321.
[70]Steddom K., Heidel G., Jones D., et al. Remote detection of rhizomania in sugar beets. Phytopathology,2003,93(6):720-726.
[71]Muhammed H. H., Larsolle A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosystems Engineering,2003,86(2):125-134.
[72]Larsolle A., Muhammed H. H. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture,2007,8:37-47.
[73]Nicolas H. Using remote sensing to determine of the date of a fungicide application on winter wheat. Crop Protection,2004,23(2):853-863.
[74]Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici)and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1:275-288.
[75]Huang J. F., Apan A. Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. Journal of Spatial Science, 2006,51:129-142.
[76]Bauriegel E., Giebel A., Geyer M., et al. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture,2011, 75:304-312.
[77]Ray S. S., Jain N., Arora R. K., et al. Utility of hyperspectral data for potato late blight disease detection. Journal of Indian Society of Remote Sensing,2011,39(2):161-169.
[78]阿布都瓦斯提.吾拉木.(2006).基于n维光谱特征空间的农田干旱遥感监测.北京大学,博士论文.
[79]黄敬峰,王福民,王秀珍.水稻高光谱遥感实验研究.浙江：浙江大学出版社,2010：1-7：136-152.
[80]Lillesand, T. M., Kiefer, R. W.. Remote sensing and image interpretation (third edition) [M]. John Wiley & Sons, New York:Inc.,1994.
[81]刘占宇,孙华生,黄敬峰.基于学习矢量量化神经网络的水稻白穗和正常穗的高光谱识别[J].中国水稻科学,2007,21(6)：658-666.
[82]F. Boochs, G. Kupfer, K. Dockter, W. Ktihbauch, Shape of the red edge as vitality indicator for plants. International Journal of Remote Sensing,1990,11(10): 1741-1753.
[83]马伟,赵珍梅,刘翔,闫东川.植被指数与地表温度定量关系遥感分析——以北京市TM数据为例.国土资源遥感,2010,4(3)：108-112.
[84]张竞成.多源遥感数据小麦病害信息提取方法研究[D],浙江,浙江大学,2012.
[85]Lenk, S., Chaerle, L., Pfiindel, E. E., et al.. Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications [J]. Journal of Experimental Botany,2007,58(4):807-814.
[86]West, J.S., Bravo, C., Oberit, R., et al.The potential of optical canopy measurement for targeted control of field crop diseases [J]. Annual Review of Phytopathology, 2003,41:593-614.
[87]陈维君,周启发,黄敬峰.用高光谱植被指数估算水稻乳熟后叶片和穗的色素含量.中国水稻科学,2006,20(4)：434-439.
[88]Shizhou Du, Wenjiang Huang, Rongfu Wang, Ju-hua Luo, Jinling Zhao, Lin Yuan. Application of Continuum Removal Method for Estimating Disease Severity Level of Wheat Powdery Mildew. Advanced Materials Research,Vo 396-398:2012-2017.
[89]王大成,王纪华,李存军,张东彦,黄敬峰.基于ANN和传统算法估算小麦叶绿素密度的比较.农业工程学报,2008,24 SUPP.2,196-201.
[90]袁志发,周静芋.多元统计分析.北京：科学出版社.2002.
[91]Ausmus B. S., Hilty J. W. Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves. Remote Sensing of Environment,1972,2:77-81.
[92]Nutter F. V. Detection and measurement of plant disease gradients in peanut with a multispectral radiometer. Phytopathology,1989,79:958-963.
[93]Malthus T. J., Maderia A. C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment,1993, 45:107-116.
[94]Sasaki Y, Okamoto T, Imo K, et al. Automatic diagnosis of plant disease:ecognition between healthy and diseased leaf [J]. Journal of the Japanese Society of Agricultural Machinery,1999,61 (2):119-126.
[95]Kobayashi, T., Kanda, E., & Kitada, K. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. The American Phytopathological Society,2001,91(3),316-323.
[96]Zhang M. H., Qin Z. H., Liu X., et al. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observationand Geoinformation,2003,4:295-310.
[97]Graeff, S., Link, J., & Claupein, W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici)in wheat (Triticumaestivum L.) bymeans of leaf reflectance measurements. Central European Journal of Biology,2006,1,275-288.
[98]Daughtry C. S. T., Walthall C. L., Kim M. S., et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 2000,74:229-239.
[99]Moshou D., Bravo C., Wahlen S., et al. Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps. Precision Agriculture,2006,7:149-164.
[100]Yang C. M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11,61-81.
[101]Naidu, R.A., Perry, E.M., Pierce, F.J., & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66, 38-45.
[102]Liu, Z.Y., Wu, H.F., & Huang, J.F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Computers and Electronics in Agriculture,2010,72,99-106.
[103]Mahlein A. K., Steiner U., Dehne H. W., et al. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture,2010, 11:413-431.
[104]吴曙雯,王人潮,陈晓斌,等.稻叶瘟对水稻光谱特性的影响研究.上海交通大学学报(农业科学版),2001,20(1)：73-76.
[105]黄木易,王纪华,黄文江,等.冬小麦条锈病的光谱特征及遥感监测.农业工程学报,2003,19(6)：154-158.
[106]刘良云,黄木易,黄文江,等.利用多时相的高光谱航空图像监测冬小麦条锈病.遥感学报,2004,8(3)：275-281.
[107]蔡成静,王海光,安虎,等.小麦条锈病高光谱遥感监测技术研究.西北农林科技大学学报(自然科学版),2005,33(增刊)：31-36.
[108]郭洁滨,黄冲,王海光,等.基于高光谱遥感技术的不同小麦品种条锈病病情指数的反演.光谱学与光谱分析,2009,29(12)：3353-3357.
[109]Chen B., Li S. K.., Wang K. R. Spectrum characteristics of cotton canopy infected with verticillium wilt and applications. Agricultural Sciences in China,2008,7(5): 561-569.
[110]Chen B., Li S. K., Wang K. R., et al. Evaluating the severity level of cotton Verticillium using spectral signature analysis. International Journal of Remote Sensing,2012,33(9):2706-2724.
[111]乔红波,简桂良,邹亚飞,等.枯萎病对不同抗性棉花光谱特性的影响.棉花学报,2007,19(2)：155-158.
[112]Jordan C. F. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666.
[113]Rouse J. W., Haas R. H., Schell J. A., et al. Monitoring vegetation systems in the Great Plains with ERTS, in Third ERTS Symp., NASA SP-351, U.S. Gov. Printing Office, Washington, DC, Vol.1,1973,309-317.
[114]Kaufman Y. J., Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS.IEEE Transactions on Geoscience and Remote Sensing,1992,30, 261-270.
[115]Gitelson A. A., Zur Y., Chivkunova O. B., et al. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology,2002, 75:272-281
[116]Huete A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment,1988,25:295-309.
[117]Qi J., Chehbouni A., Huete A. R., et al. A modified soil adjusted vegetation index. Remote Sensing of Environment,1994,48:119-126.
[118]Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., & Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum,1999,106,135-141.
[119]Merton, R., & Huntington, J. Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. In Summaries of the Eight JPL Airborne Earth Science Workshop. Pasadena, CA:JPL Publication,1999, 299-307.
[120]Bravo, C., Moshou, D., West, J., McCartney, A., & Ramon, H. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering,2003,84, 137-145.
[121]Yang, C.M., Cheng, C.H., & Chen, R.K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science,2007, 47(1),329-335.
[122]Naidu, R.A., Perry, E.M., Pierce, F.J., & Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66, 38-45.
[123]Steddom, K., Heidel, & G., Jones, D. Remote Detection of Rhizomania in Sugar Beets, phytopathology,2003,93(6),720-726.
[124]Zhao, C.J., Huang,M.Y., Huang, W.J., Liu, L.Y., & Wang, J.H. Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. IGARSS,2004,6(20-24),4318-4320.
[125]Huang, W.J., Huang, M.Y., Liu, L.Y., Wang, J.H., Zhao, C.J., & Wang, J.D. Inversion of the severity of winter wheat yellow rust using proper hyper spectral index. Transactions of the CSAE,2005,21(4),97-103.
[126]Huang, W. J., David, W. L., Niu, Z., Zhang, Y. J., Liu, L.Y., & Wang, J. H. Identification of yellow rust in wheat using in-situ spectral reflectance measurements , and airborne hyperspectral imaging. Precision Agriculture,2007,8,187-197.
[127]Devadas R., Lamb D. W., Simpfendorfer S., et al. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture, 2009,10:459-470.
[128]蒋金豹,陈云浩,黄文江.用高光谱微分指数监测冬小麦病害的研究,光谱学与光谱分析,2007,27(12),2475-2479.
[129]罗菊花.基于多源数据的小麦蚜虫遥感监测预测研究[D],北京,北京师范大学,2012.
[130]靳宁.棉花黄萎病高光谱识别及遥感监测研究[D].江苏：南京信息工程大学,2009.
[131]Malthus T. J., Maderia A. C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment,1993, 45:107-116.
[132]Adams, M. L., Philpot, W. D., & Norvell, W. A. Yellowness index:an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing,1999,18,3663-3675.
[133]蒋金豹,陈云浩,黄文江,等.冬小麦条锈病严重度高光谱遥感反演模型研究.南京农业大学学报,2007,30：63-67.
[134]王圆圆,陈云浩,李京,黄文江.指示冬小麦条锈病严重度的两个新的红边参数.遥感学报,2007,11(6),875-881.
[135]Cho M A, Skidmore A K.A new technique for extracting the red edge position from hyperspectral data:The linear extrapolation method. Remote Sensing of Environment, 2006,101:181-193.
[136]Danielsen S., Munk L. Evaluation of disease assessment methods in quinoa for their ability to predict yield loss caused by downy mildew. Crop Protection,2004,23(3): 219-228.
[137]Moshou D., Bravo C., West J., et al. Automatic detection of'yellow rust'in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture,2004,44:173-188.
[138]Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, A., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging,11, 75-83.
[139]刘占宇.水稻主要病虫害胁迫遥感监测研究[D].浙江：浙江大学浙江大学环资学院,2008.
[140]Yang C. H., Everitt J. H., Fernandez C. J. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot. Biosystems Engineering,2010, 107:131-139.
[141]张瑞英.方差分析在小麦条锈病超长期预测中的应用[J].甘肃农业科技,2002,(1)：39-40.
[142]胡小平,杨之为,李振岐,邓志勇,柯长华.汉中地区小麦条锈病的BP神经网络预测[J].西北农业学报.2000,28(2)：18-21.
[143]王信群.模糊数学在病虫害测报中的应用[J].安徽农业科学.2005,33(3)：396-398.
[144]何家泌,宋玉立,张忠山,等.小麦白粉病及其防治I小麦白粉病的分布、症状和危害.河南农业科学,1998,1：17-18.
[145]李映霞.小麦抗白粉病机理的初步研究[D].湖北：华中农业大学,2004.
[146]Lorenzen B., Jensen A. Changes in spectral properties induced in barley by cereal powdery mildew.Remote Sensing of Environment,1989,27:201-209.
[147]乔红波,简桂良,邹亚飞,等.枯萎病对不同抗性棉花光谱特性的影响.棉花学 报,2007,19(2)：155-158.
[148]Graeff S., Link J., Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Central European Journal of Biology,2006,1:275-288.
[149]乔洪波,周益林,白由路,等.地面高光谱和低空遥感监测小麦白粉病初探.植物保护学报,2006,33(4)：341-344.
[150]Franke, J., & Menz, G. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agr iculture,2007,8(3),161-172.
[151]Mewes T., Franke J., Menz G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agriculture,2011,12: 795-812.
[152]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京：高等教育出版社,2003.
[153]王磊,白由路,卢艳丽,等.光谱数据变换对玉米氮素含量反演精度的影响.遥感技术与应用,2011,26(2)：220-225.
[154]Wood C W, Reeves D W, Duffield R R, Edmisten K L. Field chlorophyll measurements for evaluation of corn nitrogen status. J Plant Nutr,1992,15:487-500.
[155]Arregui L M, Lasa B, Lafarga A, Iranneta I, Baroja E, Quemada M. Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions. Eur J Agron,2006,24:140-148.
[156]Tremblay N, Belec C. Adapting nitrogen fer tilization to unpre-dictable seasonal conditions with the least impact on the envi-ronment. Horttechnology,2006,16: 408-412.
[157]Richard B. Peterson & Evelyn A. H. The multiphasic nature of nonphotochemical quenching:implications for assessment of photosynthetic electron tran sport based on chlorophyll fluorescence Photosynthesis Research 82:95-107,2004.
[158]Demmig-Adams B, Adams W W, Baker D H, Logan B A, Bowling D R, Verhoreven A S. Using chlorophyll fluorescence to assess the fraction of absorbed light allocated to thermal dissipation of excess excitation. Physiol Plant,1996,98:253-264.
[159]Li C X,Zhong Z C,Liu Y. Effect of soil water change on photosynthetic characteristics of Taxodium distichum seedlings in the hydro-fluctuation belt of the Three Gorges Reservoir Area. Acta Ecologica Sinica,2005,25(8):1953-1959.
[160]Zhang YL,Hu YY,Luo HH,et al. Two distinct strate-gies of cotton and soybean differing in leaf movement to perform photosynthesis under drought in the field. Func-tional Plant Biology,2011,38:567-575.
[161]郭峰,曲妍妍,信长朋,等.弱光下生长的高产小麦品系PH01-35旗叶光合机构对不同光强的响应[J].作物学报,2009,35(1)：179-184.
[162]金铭,刘湘南,李铁瑛.基于冠层多维光谱的水稻镉污染胁迫诊断模型研究[J].中国环境科学2011,31(1)：137-143.
[163]杨福生,洪波.独立分量分析的原理与应用[M].北京：清华大学出版社,2006.
[164]Kaufman, Y. J., Gao, B. C. Remote Sensing of Water Vapor in the Near IR from EOS/MODIS[J]. IEEE Transactionson Geoscience and Remote Sensing,1992,30(5): 871-884.
[165]Liang, S. L., Fang, H. L., Chen, M. Z.. Atmospheric correction of landsat ETM+ land surface imagery-Part1:Methods[J]. IEEE Transactions on Geoscience and Remote Sensing,2001,39(11):2490-2498.
[166]S.Z.Li, et al.,"Learing Multiview Face Subspaces and Facial Pose Estimation using Independent Component Analysis", IEEE Trans. ImageProcessing, vol-14, No.6,pp.705-712,June 2005.
[167]Du P,Zhao HJ,et al. Independent component analysis for hyperspectral imagery plant classification, applications of neural networks and machine learning in image processing 2005,5673:71-81.
[168]林婷.水稻锌污染高光谱遥感监测模型研究[D].北京：中国地质大学.硕士学位论文.
[169]Jurgens, C. The modified normalized difference vegetation index (mNDVI):a new index to determine frost damages in agriculture based on Landsat TM data, International. Journal of Remote Sensing.,1997,18(17):3583-3594.
[170]顾晓鹤,宋国宝,韩立建等.基于变化向量分析的冬小麦长势变化监测研究.农业工程学报,2008,24(4)：159-165.
[171]陈晋,何春阳,卓莉.基于变化向量分析(CVA)的土地利用／覆盖变化动态监测(II)-变化类型的确定方法.遥感学报,2001,5(5)：346-352.
[172]张晓东,李德仁,龚健雅,等.遥感影像与GIS分析相结合的变化检测方法.武汉大学学报：信息科学版,2006,3(10)：266-269.
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