稻纵卷叶螟和褐飞虱为害水稻的光谱监测
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摘要
本文采用便携式光谱仪在叶片、小区和大田水平上测定了不同生育期(分蘖期、孕穗期和扬花期)水稻受稻纵卷叶螟和褐飞虱为害后的光谱反射率,建立了光谱反射率与虫害程度间的线性回归模型,以及利用径向基函数神经网络方法监测卷叶率和褐飞虱虫量的方法,获得了以下主要结果：
(1)稻纵卷叶螟为害后水稻的叶片光谱反射率表现为,随着稻叶受害程度的升高,分蘖期在400-420nm及610-700nm波段内升高,在530-570nn和700-1050nm波段内降低；孕穗期在659-678nm波段内升高,在426-475nm、496-630nnm和690-1000nm波段内降低；扬花期在632-691nm波段内升高,510-616nm和697-1000nm波段内降低。
利用健康叶和受害卷叶平铺组成的不同卷叶率组合叶片,其光谱反射率随卷叶率的升高,分蘖期和孕穗期均在450-500nm及610-700nm波段内升高,在530-570nm和700-1000nm波段内降低。
小区水稻受稻纵卷叶螟为害后的冠层光谱反射率表现为,随着卷叶率级别的升高,孕穗期水稻在717-1000nm波段内降低,扬花期在530-600nn1和717-1000nm波段内降低。
大田水稻受稻纵卷叶螟为害后的冠层光谱反射率表现为,随着卷叶率的升高,分蘖期在515-596nm和698-1000nm波段内降低；孕穗期在664-684nm波段内升高,在714-1000nm波段内降低；扬花期在623-692nm波段内升高,在725-1000nm波段内降低。
(2)建立了基于叶片光谱指数的叶片受害程度的一元线性回归监测模型,分蘖期以741nn1处的反射率监测效果较好,进行100次监测的准确率为90%；孕穗期以NDVI的监测效果较好,进行86次监测的准确率为76%；扬花期以Rgreen/Bred的监测效果较好,进行80次监测的准确率为69%。
建立了基于光谱指数对平铺组合叶片卷叶率监测的一元线性回归模型,分蘖期以Syellow的监测效果较好,进行14次监测的准确率为86%；孕穗期以Sred的效果较好,进行14次监测的准确率为79%。
建立了基于小区水稻冠层光谱指数的卷叶率级别(Y,0-5级)的逐步回归监测模型,孕穗期以Y=14.47-0.60DVI542-500-142.98RVSI-6254.44FD540-16.76(Sred-Sblue)效果较好,模型的诊断误差RMSE为0.216；扬花期以Y=8.7356-44.7232DVI730-670+40.3448TCARI+539.1316FD715+44.3701SD695的效果较好,模型诊断误差为0.3035。
对400-1000nm内大田水稻受稻纵卷叶螟为害后的光谱反射率采用逐步回归、因子分析和偏最小二乘法进行降维,并以逐步回归筛选出的各反射率、因子分析得到的两个主要因子的得分值、偏最小二乘法得到的各因子的得分值、以及筛选出的18个光谱指数作为神经网络的输入层因子,卷叶率作为输出层,进行大田卷叶率的径向基函数神经网络的学习和监测,结果表明,对分蘖期、孕穗期和扬花期水稻的31、23和27个卷叶率进行监测时,逐步回归方法所得因子为输入时最大的监测准确率分别为61%、65%和63%；因子分析所得的两个因子值为输入时最大的监测准确率分别为68%、69%和63%；偏最小二乘法得到的最少因子值为输入时最大的监测准确率分别为74%、78%和70%；18个光谱指数为输入因子时最大的监测准确率分别为81%、83%和78%。
(3)水稻受褐飞虱为害后的光谱特征表明,随着虫量的增加,孕穗期水稻叶片的光谱反射率在570-690nm内上升,而在700-1000nm波段内下降,扬花期在710-1000nm波段内下降。水稻冠层光谱反射率随虫量增加,拔节-孕穗期在484-515nm和570-707nm波段内升高,在720-1000nm波段内下降；孕穗期在525-565nm和700-1000nm波段内下降；扬花期在725-1000nm波段内下降。
建立了基于叶片光谱指数的褐飞虱虫量(Y)的一元线性回归监测模型,孕穗期(褐飞虱0-106.25头)以Chl Index和NPQI组建的模型监测效果较好,误差分别为17.3和17.33头；扬花期(褐飞虱0-17.5头)以DMRnir-green构建的模型效果较好,误差为3.37头。对25个叶片光谱指数与褐飞虱虫量进行逐步回归建模,孕穗期和扬花期水稻上褐飞虱虫量的监测分别以Y=174.831-79.361Chllndex-1655.217NPQI和Y=-39.903+491.616DMnir-green-791.617DMRnir-red+120.714G(Lich)模型的效果较好,监测误差分别为13.71和2.87头。
建立了水稻冠层光谱指数的褐飞虱虫量(Y)监测的一元线性回归模型,拔节-孕穗期(褐飞虱虫量0-112.5)和孕穗期(褐飞虱虫量0-106.25)水稻褐飞虱虫量的监测均以指数MRnir构建的模型效果较好,模型的误差分别为19.28和18.22头；扬花期(褐飞虱0-17.5)以DVI935-670构建的模型效果较好,误差为3.81头。利用25个冠层光谱指数构建褐飞虱虫量的逐步回归模型,拔节-孕穗期、孕穗期和扬花期监测褐飞虱虫量较好的模型分别为：Y=179.85-1481.037MRnir+2341.975DVI730-542,Y=353.754-472.028MRnir-108.122WI+88.052DVI542-500,Y=158.46-1.251WI-65.499DFI935-670-133.89AI,模型的误差分别为15.06、10.6和3.29头。
对400-1000nm波段内的水稻冠层光谱反射率,采用逐步回归筛选出主要的反射率、因子分析得到两个主要因子的得分、偏最小二乘方法得到各因子的得分、以及筛选的25个冠层光谱指数,作为径向基函数神经网络的输入层因子,褐飞虱虫量作为输出层,进行褐飞虱数量的神经网络学习和监测,结果表明,对拔节-孕穗期、孕穗期和扬花期水稻的6、7和7个褐飞虱虫量进行监测,逐步回归方法所得因子最大的监测准确率分别为67%、57%和57%；因子分析所得两个因子的最大监测准确率分别为50%、57%和57%；偏最小二乘法得到的最少因子监测的最大准确率分别为83%、71%和71%；25个光谱指数的监测的最大准确率分别为83%、71%和71%。
(4)不同氮肥施用量水稻被褐飞虱为害后的光谱特征表现为,随着氮肥用量的升高,水稻冠层光谱反射率在790-1000nm内上升,650-690nm内下降。水稻叶片SPAD值随氮肥用量的增加显著升高。随着褐飞虱虫量的增加,低氮肥用量下的水稻在褐飞虱为害14天后,冠层光谱反射率在近红外区域显著降低,但在高氮肥用量水稻上,褐飞虱为害35天后在近红外区域的冠层光谱反射率才显著降低。水稻倒四叶的SPAD值与褐飞虱的数量相关较好,而倒二叶与倒四叶SPAD的差值与施氮水平相对不敏感。水稻冠层光谱指数和SPAD指数的结合可对不同施N水平下水稻中的褐飞虱数量进行监测。
The spectral reflectance from the leaves and canopy of rice damaged by rice leaf folder (RLF) Cnaphalocrocis medinalis (Guenee) and brown planthopper (BPH) Nilaparvata lugens (Stal) were measured in plot and field at different growth stages (tillering, booting and flowering) using ASD Hand-held Spectroradiometer. The linear models for diagnosing leaf-roll rate and the number of BPH based on spectral reflectance and radial basis function (RBF) neural network method were established to supply some effective method for monitoring RLF and BPH in rice field. The main results were list as followings.
(1) At tillering stage, the reflectance of rice leaf decreased at530-570nm and700-1050nm with the increase of RLF damage, whereas it increased at400-420nm and610-700nm. At booting stage, spectral reflectance of rice leaf decreased at426-475nm,496-630nm and690-1000nm and it increased at659-678nm with the increase of RLF damage. At flowering stage, the spectral reflectance of rice leaf increased at632-691nm and decreased at510-616nm and697-1000nm.
The leaf-roll rates were simulated by combining different pieces of injured and healthy leaves at booting and flowering stage of rice on a black cloth in laboratory and reflectance from the combined rice leaves was measured. The results indicated that spectral reflectance decreased at530-570nm and700-1000nm and increased at450-500nm and610-700nm with the increase of leaf-roll rates.
Canopy reflectance from a plot of rice decreased significantly at717-1000nm at booting, and it decreased at530-600nm and717-1000nm at flowering stage as the infestation scales of RLF increased.
In rice field, canopy reflectance of rice decreased significantly at515-596nm and698-1000nm at tillering stage as the leaf-roll rates increased. At booting stage, spectral reflectance increased at664-684nm and decreased at714-1000nm. At flowering stage, spectral reflectance increased at623-692nm and decreased at725-1000nm as the leaf-roll rates increased.
(2) Linear regression models based on spectral indices were established for detecting the damage degrees of rice at different grow stages. At tillering stage, The model based on the reflectance at741nm was better, and its accuracy was90%for100times of prediction. At booting stage, the model based on NDVI was better which had76%accuracy for86times of prediction. At flowering stage, the model based on Rgreen/Rred was the better which accuracy was69%for80times of prediction.
Linear regression models for diagnosing the leaf-roll rates of combined leaves of rice were built. For the tillering stage leaves, the model based on Syellow was better, and its diagnostic accuracy was86%for14times prediction. For the booting stage leaves, the model based on Sred was better, and the diagnostic accuracy was79%for14times prediction.
Linear Regression models based on spectral indices were built using multiple stepwise regression (MSR) method to detect infestation scales (Y,0-5) in plot of rice. At booting stage, the model Y=14.47-0.60DVI542.500-142.98RVSI-6254.44FD540-16.76(Sred-Sblue) was better and it root mean square error (RMSE) was0.216. At flowering stage, the model Y=8.7356-44.7232DVI730-670+40.3448TCARI+539.1316FD715+44.3701SD695was better with a smaller RMSE of0.3035.
In rice field, diagnostic methods for leaf-roll rates were established using the RBF neural network method based on18spectral indices and reflectance at400-1000nm after reducing dimensions by MSR, factor analysis, and partial least squares (PLS) method. Factors or spectral indices entered into the RBF neural network as the input vectors, and the leaf-roll rates entered into RBF neural network as the target vectors. After31,23,27times prediction at tillering, booting and flowering stage, the predicting correction rate by the built network based on these factors from MSR was61%,65%and63%, respectively, based on the principal factors was68%,69%and63%, based on PLS factors was74%,78%and70%, and based on18spectral indices was81%,83%and78%, respectively.
(3) When rice was infested by BPH, reflectance from leaves increased at570-690nm and decreased at700-1000nm at booting stage of rice, and at flowering stage, reflectance decreased significantly at710-1000nm regions. At canopy-level of rice, reflectance decreased at720-1000nm and increased at484-515nm and570-707nm at joint-booting stage of rice. At booting stage, reflectance decreased at525-565nm and700-1000nm, and at flowering stage, it decreased significantly at725-1000nm.
Linear regression models for diagnosing the number of BPH infested rice were established based on the reflectance indices from rice leaves. At the booting stage (the number of BPH was0-106.25), the better models were Y=381.928-135.891CHl Index and Y=-71.254-2842.01NPQI with RMSE17.3and17.33, respectively. At the flowering stage (the number of BPH was0-17.5), the model based on DMRnir-green (Y=33.162-199.721DMRnir-green) was better with RMSE3.37. The multiple stepwise regression models based on all these25spectral indices were built, and the Y=174.831-79.361Chl Index-1655.217NPQI was better to detect the number of BPH at booting stage of rice, and its RMSE was13.71. The model Y=-39.903+491.616D MRnir-green-791.617DMRnir-red+120.714G(Lich) was better to detect the number of BPH at flowering stage with RMSE of2.87.
At canopy-level, linear regression models for diagnosing the number of BPH (7) were built. The model based on MRnir was better to diagnose the number of BPH on joint-booting stage and booting stage of rice and their RMSE were18.22and19.28, respectively. At flowering stage of rice, the model based on DVI935-670was better with3.81RMSE. The stepwise regression models for diagnosing the number of BPH at joint-booting, booting and flowering stage of rice using all25indices were Y=179.85-1481.037MRnir+2341.975DVI730-542,Y=353.754-472.028MRnir-108.122WI+88.052DVI542-500, and Y=158.46-1.251WI-65.499DVI935-670-133.89AI, which had RMSE15.06,10.6and3.29, respectively.
The diagnostic method for the number of BPH in rice based on RBF were studied using the MSR, factor analysis, and PLS to reduce the dimensions of reflectance at400-1000nm and25spectral indices. For6,7,7times prediction of BPH numbers at joint-booting, booting and flowering stage, the correction rates of RBF network built here based on factors from MSR were67%,57%and57%, respectively, and they were50%,57%and57%based on principal factors, and they were83%,71%and71%based on factors from PLS. The predicting correction rates of RBF network based on25spectral indices were83%,71%and71%for BPH numbers at joint-booting, booting and flowering stage, respectively.
(4) Reflectance and SPAD readings were measured in rice using different levels of nitrogen-fertilizer and infested by different number of BPH. The results showed that the reflectance from rice canopy at790-1000nm and SPAD values from leaves increased significantly, and they decreased at650-690nm regions as N fertilizer rates increased. In the pots used low N fertilizer rate, Canopy reflectance of rice in the near-infrared region and SPAD value from leaves decreased significantly as the number of BPH increased when the BPH infested for14days, but in the high N fertilizer rate, canopy reflectance of near-infrared region decreased significantly when BPH infested for35days. SPAD value of the fourth fully expanded leaf (4LFT) had significant correlation with the number of BPH, and the relative ratio between SPAD reading of the second full expanded leaf (2LFT) and the fourth fully expanded leaf (4LFT) was relatively insensitive to N fertilizer rate but was sensitive to the number of BPH in rice used different N fertilizer rates. Spectral indices and SPAD index had great potential for detecting BPH damage in rice which was used different N fertilizer.
(1)稻纵卷叶螟为害后水稻的叶片光谱反射率表现为,随着稻叶受害程度的升高,分蘖期在400-420nm及610-700nm波段内升高,在530-570nn和700-1050nm波段内降低；孕穗期在659-678nm波段内升高,在426-475nm、496-630nnm和690-1000nm波段内降低；扬花期在632-691nm波段内升高,510-616nm和697-1000nm波段内降低。
利用健康叶和受害卷叶平铺组成的不同卷叶率组合叶片,其光谱反射率随卷叶率的升高,分蘖期和孕穗期均在450-500nm及610-700nm波段内升高,在530-570nm和700-1000nm波段内降低。
小区水稻受稻纵卷叶螟为害后的冠层光谱反射率表现为,随着卷叶率级别的升高,孕穗期水稻在717-1000nm波段内降低,扬花期在530-600nn1和717-1000nm波段内降低。
大田水稻受稻纵卷叶螟为害后的冠层光谱反射率表现为,随着卷叶率的升高,分蘖期在515-596nm和698-1000nm波段内降低；孕穗期在664-684nm波段内升高,在714-1000nm波段内降低；扬花期在623-692nm波段内升高,在725-1000nm波段内降低。
(2)建立了基于叶片光谱指数的叶片受害程度的一元线性回归监测模型,分蘖期以741nn1处的反射率监测效果较好,进行100次监测的准确率为90%；孕穗期以NDVI的监测效果较好,进行86次监测的准确率为76%；扬花期以Rgreen/Bred的监测效果较好,进行80次监测的准确率为69%。
建立了基于光谱指数对平铺组合叶片卷叶率监测的一元线性回归模型,分蘖期以Syellow的监测效果较好,进行14次监测的准确率为86%；孕穗期以Sred的效果较好,进行14次监测的准确率为79%。
建立了基于小区水稻冠层光谱指数的卷叶率级别(Y,0-5级)的逐步回归监测模型,孕穗期以Y=14.47-0.60DVI542-500-142.98RVSI-6254.44FD540-16.76(Sred-Sblue)效果较好,模型的诊断误差RMSE为0.216；扬花期以Y=8.7356-44.7232DVI730-670+40.3448TCARI+539.1316FD715+44.3701SD695的效果较好,模型诊断误差为0.3035。
对400-1000nm内大田水稻受稻纵卷叶螟为害后的光谱反射率采用逐步回归、因子分析和偏最小二乘法进行降维,并以逐步回归筛选出的各反射率、因子分析得到的两个主要因子的得分值、偏最小二乘法得到的各因子的得分值、以及筛选出的18个光谱指数作为神经网络的输入层因子,卷叶率作为输出层,进行大田卷叶率的径向基函数神经网络的学习和监测,结果表明,对分蘖期、孕穗期和扬花期水稻的31、23和27个卷叶率进行监测时,逐步回归方法所得因子为输入时最大的监测准确率分别为61%、65%和63%；因子分析所得的两个因子值为输入时最大的监测准确率分别为68%、69%和63%；偏最小二乘法得到的最少因子值为输入时最大的监测准确率分别为74%、78%和70%；18个光谱指数为输入因子时最大的监测准确率分别为81%、83%和78%。
(3)水稻受褐飞虱为害后的光谱特征表明,随着虫量的增加,孕穗期水稻叶片的光谱反射率在570-690nm内上升,而在700-1000nm波段内下降,扬花期在710-1000nm波段内下降。水稻冠层光谱反射率随虫量增加,拔节-孕穗期在484-515nm和570-707nm波段内升高,在720-1000nm波段内下降；孕穗期在525-565nm和700-1000nm波段内下降；扬花期在725-1000nm波段内下降。
建立了基于叶片光谱指数的褐飞虱虫量(Y)的一元线性回归监测模型,孕穗期(褐飞虱0-106.25头)以Chl Index和NPQI组建的模型监测效果较好,误差分别为17.3和17.33头；扬花期(褐飞虱0-17.5头)以DMRnir-green构建的模型效果较好,误差为3.37头。对25个叶片光谱指数与褐飞虱虫量进行逐步回归建模,孕穗期和扬花期水稻上褐飞虱虫量的监测分别以Y=174.831-79.361Chllndex-1655.217NPQI和Y=-39.903+491.616DMnir-green-791.617DMRnir-red+120.714G(Lich)模型的效果较好,监测误差分别为13.71和2.87头。
建立了水稻冠层光谱指数的褐飞虱虫量(Y)监测的一元线性回归模型,拔节-孕穗期(褐飞虱虫量0-112.5)和孕穗期(褐飞虱虫量0-106.25)水稻褐飞虱虫量的监测均以指数MRnir构建的模型效果较好,模型的误差分别为19.28和18.22头；扬花期(褐飞虱0-17.5)以DVI935-670构建的模型效果较好,误差为3.81头。利用25个冠层光谱指数构建褐飞虱虫量的逐步回归模型,拔节-孕穗期、孕穗期和扬花期监测褐飞虱虫量较好的模型分别为：Y=179.85-1481.037MRnir+2341.975DVI730-542,Y=353.754-472.028MRnir-108.122WI+88.052DVI542-500,Y=158.46-1.251WI-65.499DFI935-670-133.89AI,模型的误差分别为15.06、10.6和3.29头。
对400-1000nm波段内的水稻冠层光谱反射率,采用逐步回归筛选出主要的反射率、因子分析得到两个主要因子的得分、偏最小二乘方法得到各因子的得分、以及筛选的25个冠层光谱指数,作为径向基函数神经网络的输入层因子,褐飞虱虫量作为输出层,进行褐飞虱数量的神经网络学习和监测,结果表明,对拔节-孕穗期、孕穗期和扬花期水稻的6、7和7个褐飞虱虫量进行监测,逐步回归方法所得因子最大的监测准确率分别为67%、57%和57%；因子分析所得两个因子的最大监测准确率分别为50%、57%和57%；偏最小二乘法得到的最少因子监测的最大准确率分别为83%、71%和71%；25个光谱指数的监测的最大准确率分别为83%、71%和71%。
(4)不同氮肥施用量水稻被褐飞虱为害后的光谱特征表现为,随着氮肥用量的升高,水稻冠层光谱反射率在790-1000nm内上升,650-690nm内下降。水稻叶片SPAD值随氮肥用量的增加显著升高。随着褐飞虱虫量的增加,低氮肥用量下的水稻在褐飞虱为害14天后,冠层光谱反射率在近红外区域显著降低,但在高氮肥用量水稻上,褐飞虱为害35天后在近红外区域的冠层光谱反射率才显著降低。水稻倒四叶的SPAD值与褐飞虱的数量相关较好,而倒二叶与倒四叶SPAD的差值与施氮水平相对不敏感。水稻冠层光谱指数和SPAD指数的结合可对不同施N水平下水稻中的褐飞虱数量进行监测。
The spectral reflectance from the leaves and canopy of rice damaged by rice leaf folder (RLF) Cnaphalocrocis medinalis (Guenee) and brown planthopper (BPH) Nilaparvata lugens (Stal) were measured in plot and field at different growth stages (tillering, booting and flowering) using ASD Hand-held Spectroradiometer. The linear models for diagnosing leaf-roll rate and the number of BPH based on spectral reflectance and radial basis function (RBF) neural network method were established to supply some effective method for monitoring RLF and BPH in rice field. The main results were list as followings.
(1) At tillering stage, the reflectance of rice leaf decreased at530-570nm and700-1050nm with the increase of RLF damage, whereas it increased at400-420nm and610-700nm. At booting stage, spectral reflectance of rice leaf decreased at426-475nm,496-630nm and690-1000nm and it increased at659-678nm with the increase of RLF damage. At flowering stage, the spectral reflectance of rice leaf increased at632-691nm and decreased at510-616nm and697-1000nm.
The leaf-roll rates were simulated by combining different pieces of injured and healthy leaves at booting and flowering stage of rice on a black cloth in laboratory and reflectance from the combined rice leaves was measured. The results indicated that spectral reflectance decreased at530-570nm and700-1000nm and increased at450-500nm and610-700nm with the increase of leaf-roll rates.
Canopy reflectance from a plot of rice decreased significantly at717-1000nm at booting, and it decreased at530-600nm and717-1000nm at flowering stage as the infestation scales of RLF increased.
In rice field, canopy reflectance of rice decreased significantly at515-596nm and698-1000nm at tillering stage as the leaf-roll rates increased. At booting stage, spectral reflectance increased at664-684nm and decreased at714-1000nm. At flowering stage, spectral reflectance increased at623-692nm and decreased at725-1000nm as the leaf-roll rates increased.
(2) Linear regression models based on spectral indices were established for detecting the damage degrees of rice at different grow stages. At tillering stage, The model based on the reflectance at741nm was better, and its accuracy was90%for100times of prediction. At booting stage, the model based on NDVI was better which had76%accuracy for86times of prediction. At flowering stage, the model based on Rgreen/Rred was the better which accuracy was69%for80times of prediction.
Linear regression models for diagnosing the leaf-roll rates of combined leaves of rice were built. For the tillering stage leaves, the model based on Syellow was better, and its diagnostic accuracy was86%for14times prediction. For the booting stage leaves, the model based on Sred was better, and the diagnostic accuracy was79%for14times prediction.
Linear Regression models based on spectral indices were built using multiple stepwise regression (MSR) method to detect infestation scales (Y,0-5) in plot of rice. At booting stage, the model Y=14.47-0.60DVI542.500-142.98RVSI-6254.44FD540-16.76(Sred-Sblue) was better and it root mean square error (RMSE) was0.216. At flowering stage, the model Y=8.7356-44.7232DVI730-670+40.3448TCARI+539.1316FD715+44.3701SD695was better with a smaller RMSE of0.3035.
In rice field, diagnostic methods for leaf-roll rates were established using the RBF neural network method based on18spectral indices and reflectance at400-1000nm after reducing dimensions by MSR, factor analysis, and partial least squares (PLS) method. Factors or spectral indices entered into the RBF neural network as the input vectors, and the leaf-roll rates entered into RBF neural network as the target vectors. After31,23,27times prediction at tillering, booting and flowering stage, the predicting correction rate by the built network based on these factors from MSR was61%,65%and63%, respectively, based on the principal factors was68%,69%and63%, based on PLS factors was74%,78%and70%, and based on18spectral indices was81%,83%and78%, respectively.
(3) When rice was infested by BPH, reflectance from leaves increased at570-690nm and decreased at700-1000nm at booting stage of rice, and at flowering stage, reflectance decreased significantly at710-1000nm regions. At canopy-level of rice, reflectance decreased at720-1000nm and increased at484-515nm and570-707nm at joint-booting stage of rice. At booting stage, reflectance decreased at525-565nm and700-1000nm, and at flowering stage, it decreased significantly at725-1000nm.
Linear regression models for diagnosing the number of BPH infested rice were established based on the reflectance indices from rice leaves. At the booting stage (the number of BPH was0-106.25), the better models were Y=381.928-135.891CHl Index and Y=-71.254-2842.01NPQI with RMSE17.3and17.33, respectively. At the flowering stage (the number of BPH was0-17.5), the model based on DMRnir-green (Y=33.162-199.721DMRnir-green) was better with RMSE3.37. The multiple stepwise regression models based on all these25spectral indices were built, and the Y=174.831-79.361Chl Index-1655.217NPQI was better to detect the number of BPH at booting stage of rice, and its RMSE was13.71. The model Y=-39.903+491.616D MRnir-green-791.617DMRnir-red+120.714G(Lich) was better to detect the number of BPH at flowering stage with RMSE of2.87.
At canopy-level, linear regression models for diagnosing the number of BPH (7) were built. The model based on MRnir was better to diagnose the number of BPH on joint-booting stage and booting stage of rice and their RMSE were18.22and19.28, respectively. At flowering stage of rice, the model based on DVI935-670was better with3.81RMSE. The stepwise regression models for diagnosing the number of BPH at joint-booting, booting and flowering stage of rice using all25indices were Y=179.85-1481.037MRnir+2341.975DVI730-542,Y=353.754-472.028MRnir-108.122WI+88.052DVI542-500, and Y=158.46-1.251WI-65.499DVI935-670-133.89AI, which had RMSE15.06,10.6and3.29, respectively.
The diagnostic method for the number of BPH in rice based on RBF were studied using the MSR, factor analysis, and PLS to reduce the dimensions of reflectance at400-1000nm and25spectral indices. For6,7,7times prediction of BPH numbers at joint-booting, booting and flowering stage, the correction rates of RBF network built here based on factors from MSR were67%,57%and57%, respectively, and they were50%,57%and57%based on principal factors, and they were83%,71%and71%based on factors from PLS. The predicting correction rates of RBF network based on25spectral indices were83%,71%and71%for BPH numbers at joint-booting, booting and flowering stage, respectively.
(4) Reflectance and SPAD readings were measured in rice using different levels of nitrogen-fertilizer and infested by different number of BPH. The results showed that the reflectance from rice canopy at790-1000nm and SPAD values from leaves increased significantly, and they decreased at650-690nm regions as N fertilizer rates increased. In the pots used low N fertilizer rate, Canopy reflectance of rice in the near-infrared region and SPAD value from leaves decreased significantly as the number of BPH increased when the BPH infested for14days, but in the high N fertilizer rate, canopy reflectance of near-infrared region decreased significantly when BPH infested for35days. SPAD value of the fourth fully expanded leaf (4LFT) had significant correlation with the number of BPH, and the relative ratio between SPAD reading of the second full expanded leaf (2LFT) and the fourth fully expanded leaf (4LFT) was relatively insensitive to N fertilizer rate but was sensitive to the number of BPH in rice used different N fertilizer rates. Spectral indices and SPAD index had great potential for detecting BPH damage in rice which was used different N fertilizer.
引文
1. Abdel-Rahman E M, Ahmed F B, van den Berg M, Way M J. Potential of spectroscopic data sets for sugarcane thrips(Fulmekiola serrata Kobus) damage detection. International Journal of Remote Sensing,2010,31:4199-4216.
2. Abou-Ismail O, Huang J F, Wang R C. Rice yield estimation by integrating remote sensing with rice growth simulation model. Pedosphere,2004,14:519-526.
3. 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,1999,20:3663-3675.
4. Aggarwal P K, Banerjee B, Daryaei M G, Pathak H. InfoCrop:A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. Ⅱ. Performance of the model. Agricultural Systems,2006,89:47-67.
5. Aldea M, Hamilton J, Resti J, Zangerl A R, Berenbaum M R, Frank T D, DeLucia E H. Comparison of photosynthetic damage from arthropod herbivory and pathogen infection in understory hardwood saplings. Oecologia,2006,149:221-232.
6. Aldea M, Hamilton J G, Resti J P, Zangerl A R, Berenbaum M R. Indirect effects of insect herbivory on leaf gas exchange in soybean. Plant, Cell & Environment,2005,28:402-411.
7. Apan A, Dart B, Kelly R. Detection of pests and diseases in vegetable crops using hyperspectral sensing:a comparison of reflectance data for different sets of symptoms. SSC2005 Spatial Sciences Institute Biennial Conference. Spatial Sciences Institute, Melbourne, Australia, September 2005: 12-16.
8. Asner G P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment,1998,64:234-253.
9. Backoulou G F, Elliott N C, Giles K, Phoofolo M, Catana V. Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia. Computers and Electronics in Agriculture,2010,75:64-70.
10. Bae S H and Pathak M D. Life history of Nilaparvata lugens (Homoptera:Delphacidae) and susceptibility of rice varieties to its attacks. Annals of the Entomological Society of America,1969, 63:149-155.
11. Barnes J, Balaguer L, Manrique E, Elvira S, Davison A W. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls (a and b) in lichens and higher plants. Environmental and Experimental Botany,1992,32:85-100.
12. Bautista R C, Heinrichs E A, Rejesus R S. Economic injury levels for the rice leaffolder Cnaphalocrocis medinalis (Lepidoptera:Pyralidae):Insect infestation and artificial leaf removal. Environmental Entomology,1984,13:439-443.
13. Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales:An evaluation of some hyperspectral approaches. Remote Sensing of Environment,1998,66:273-285.
14. Blanchfield A L, Robinson S A, Renzullo L J, Powell K S. Phylloxera-infested grapevines have reduced chlorophyll and increased photoprotective pigment content:Can leaf pigment composition aid pest detection? Functional plant biology,2006,33:507-514.
15. Boote K J, Jones J W, Mishoe J W, Berger R D. Coupling pests to crop growth simulators to predict yield reduction. Phytopathology,1983,73:1581-1587.
16. Bottrell D G and Schoenly K G. Resurrecting the ghost of green revolutions past:The brown planthopper as a recurring threat to high yielding rice production in tropical Asia. Journal of Asia-Pacific Entomology,2011,15:122-140.
17. Capucho A S, Zambolim L, Duarte H S S, Parreira D F, Ferreira P A, Lanza F E, Cota L V. Influence of leaf position that correspond to whole plant severity and diagrammatic scale for white spot of corn. Crop Protection,2010,29:1015-1020.
18. Carter G A. Responses of leaf spectral reflectance to plant stress. American Journal of Botany,1993, 80:239-243.
19. Carter G A, Miller R L. Early Detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment,1994,50:295-302.
20. Carvalho S, Macel M, Schlerf M, Skidmore A K, Putten W H. Soil biotic impact on plant species shoot chemistry and hyperspectral reflectance patterns. New Phytologist,2012,196:1133-1144.
21. Casanova D, Epema G F, Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research,1998,55:83-92.
22. Chappelle E W, Kim M S, McMurtrey I J E. Ratio analysis of reflectance spectra (RARS):An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment,1992,39:239-247.
23. Choudhary R, Mahesh S, Paliwal J, Jayas D S. Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering,2009, 102:115-127.
24. Cohen Y and Shoshany M. A national knowledge-based crop recognition in Mediterranean environment. International Journal of Applied Earth Observation and Geoinformation,2002,4: 75-87.
25. Dai Y J, Shao M M, Hannaway D, Wang L L, Liang J P, Hu L N, Lu H F. Effect of Thrips tabaci on anatomical features, photosynthetic characteristics and chlorophyll fluorescence of Hypericum sampsonii leaves. Crop Protection,2009,28:327-332.
26. Daughtry C S T, Walthall C L, Kim M S, de Colstoun E B, McMurtrey Iii J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment,2000, 74:229-239.
27. Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus Leaves. Remote Sensing of Environment,1998,66:111-121.
28. de Kraker J, Rabbinge R, van Huis A, van Lenteren J C, Heong K L. Impact of nitrogenous-fertilization on the population dynamics and natural control of rice leaffolders (Lep.: Pyralidae). International Journal of Pest Management,2000,46:225-235.
29. Delalieux S, Somers B, Verstraeten W W, van Aardt J A N, Keulemans W, Coppin P. Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology. International Journal of Remote Sensing,2009,30:1887-1912.
30. Delegido J, Alonso L, Gonzalez G, Moreno J. Estimating chlorophyll content of crops from hyperspectral data using a Normalized Area Over reflectance Curve (NAOC). International Journal of Applied Earth Observation and Geoinformation,2010,12:165-174.
31. Diaz-montano J, Reese J C, Schapaugh W T, Campbell L R. Chlorophyll loss caused by soybean aphid (Hemiptera:Aphididae) feeding on soybean. Journal of Economic Entomology,2007,100: 1657-1662.
32. Drake J M and Griffen B D. Early warning signals of extinction in deteriorating environments. Nature,2010,467:456-459.
33. Elliott N, Mirik M, Yang Z, Dvorak T, Rao M, Michels J, Walker T, Catana V, Phoofolo M, Giles K, Royer T. Airborne multi-spectral remote sensing of Russian wheat aphid injury to wheat. Southwestern Entomologist,2009,32:213-219.
34. Elvidge C D and Chen Z K. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment,1995,54:38-48.
35. Fitzgerald G J, Maas S J, Detar W R. Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Precision Agriculture,2004,5: 275-289.
36. Fitzgerald G J, Maas S J, Detar W R. Early detection of spider mites in cotton using multispectral remote sensing, Proceedings of the Beltwide Cotton Conference,03-07 January, Orlando, Florida, 1999,1022-1024.
37. Franzen L D, Gutsche A R, Heng-Moss T M, Higley L G, Sarath G, Burd J D. Physiological and biochemical responses of resistant and susceptible wheat to onjury by Russian wheat aphid. Journal of Economic Entomology,2007,100:1692-1703.
38. Fraulo A B, Cohen M, Liburd O E. Visible/Near infrared reflectance (VNIR) spectroscopy for detecting twospotted spider mite (Acari:Tetranychidae) damage in strawberries. Environmental Entomology,2009,38:137-142.
39. Gamon J A and Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytologist,1999,143:105-117.
40. Gamon J A, Penuelas J, Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment,1992,41:35-44.
41. Garbulsky M, Penuelas J, Gamon J, Inoue Y, Filella I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies:A review and meta-analysis. Remote Sensing of Environment,2010,115:281-297.
42. Gemmell F and Varjo J. Utility of reflectance model inversion versus two spectral indices for estimating biophysical characteristics in a boreal forest test site. Remote Sensing of Environment, 1999,68:95-111.
43. Genc H, Genc L, Turhan H, Smith S E, Nation J L. Vegetation indices as indicators of damage by the sunn pest (Hemiptera:Scutelleridae) to field grown wheat. African Journal of Biotechnology, 2008,7:173-180.
44. Gerloff E D and Ortman E E. Physiological changes in barley induced by greenbug feeding stress. Crop Science,1971,11:174-176.
45. Gholizadeh A, Amin M S M, Anuar A R, Aimrun W. Evaluation of SPAD chlorophyll meter in two Ddifferent rice growth stages and its temporal variability. European Journal of Scientific Research, 2009,37:591-598.
46. Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology,2003,160:271-282.
47. Gitelson A A, Vina A, Ciganda V, Rundquist D C, Arkebauer T J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters,2005,32:L08403.
48. Gottfried F, Faheemah, F. The spinning (Stitching) behaviour of the rice leaf folder, Cnaphalocrocis medinalis. Entomologia Experimentalis et Applicata,1981,29:138-146.
49. Gutierrez M, Norton R, Thorp K R, Wang G. Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Science,2012,52:849-857.
50. Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment,2002,81:416-426.
51. Ham F M, Kostanic I. Principles of neurocomputing for science and engineering. New York: McGraw-Hill Higher Education,2000, p:140-152.
52. Heng-moss T, Macedo T, Franzen L, Baxendale F, Higley L, Sarath G. Physiological Responses of Resistant and Susceptible Buffalograsses to Blissus Occiduus (Hemiptera:Blissidae) Feeding. Journal of Economic Entomology,2006,99:222-228.
53. Hibino H, Usugi T, Omura T, Tsuchizaki T, Shohara K, Iwasaki M. Rice grassy stunt virus:a planthopper-borne circular filament. Phytopathology,1985,75:894-899.
54. Hoque E, Hutzler P J S, Hiendl H. Reflectance, colour, and histological features as parameters for the early assessment of forest damages. Canadian Journal of Remote Sensing,1992,18:104-110.
55. Houborg R, Anderson M, Daughtry C. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sensing of Environment,2009,113:259-274.
56. INGER Genetic Resources Center. Standard evaluation system for rice. IRRI, Manila, Philippines, 1996,29.
57. Inoue Y, Sakaiya E, Zhu Y, Takahashi W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment,2012,126:210-221.
58. Jong P D. Effect of folding and feeding by Cnaphalocrocis medinalis on photosynthesis and transpiration of rice leaves. Entomologia Experimentalis et Applicata,1992,63:101-102.
59. Ju C H, Tian Y C, Yao X, Cao W X, Zhu Y, Hannaway D. Estimating leaf chlorophyll content using red edge parameters. Pedosphere,2010,20:633-644.
60. Kieckhefer R W, Gellner J L, Riedell W E. Evaluation of the aphid-day standard as a predictor of yield loss caused by cereal aphids. Agronomy Journal,1995,87:785-788.
61. Kochubey S M and Kazantsev T A. Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology,2007,164: 1648-1655.
62. Krause G H and Weis E. Chlorophyll fluorescence and photosynthesis:The basics. Annual Review of Plant Physiology and Plant Molecular Biology,1991,42:313-349.
63. Kumar J, Vashisth A, Sehgal V, Gupta V K. Assessment of aphid infestation in mustard by hyperspectral remote sensing. Journal of the Indian Society of Remote Sensing,2012,1-8.
64. Li W G, Li H, Zhao L H. Estimating rice yield by HJ-1 A satellite images. Rice Science,2011,18: 142-147.
65. Lin F F, Qiu L F, Deng J S, Shi Y Y, Chen L S, Wang K. Investigation of SPAD meter-based indices for estimating rice nitrogen status. Computers and Electronics in Agriculture,2010,71:60-65.
66. Lu Y L, Li S K, Bai Y L, Jones C L, Wang J H. Canopy spatial distribution and identification using hyperspectal data in winter wheat. Communications in Soil Science and Plant Analysis,2009,40: 1240-1253.
67. Lu Z X, Heong K L, Yu X P, Hu C. Effects of plant nitrogen on ecological fitness of the brown planthopper, Nilaparvata lugens Stal. in rice. Journal of Asia-Pacific Entomology,2004,7:97-104.
68. Lu Z X, Heong K L, Yu X P, Hu C. Effects of nitrogen on the tolerance of brown planthopper, Nilaparvata Lugens, to adverse environmental factors. Insect Science,2005,12:121-128.
69. Lu Z X, Heong K L. Effects of nitrogen-enriched rice plants on ecological fitness of planthoppers. Planthoppers:New threats to the sustainability of intensive rice production systems in Asia. K L Heong, Hardy, B. IRRI, Los Banos, Philippines,2009,247-256.
70. Lu Z X, Yu X P, Heong K L, Hu C. Effect of nitrogen fertilizer on herbivores and its stimulation to major insect pests in rice. Rice Science,2007,14:56-66.
71. Luedeling E, Hale A, Zhang M, Bentley W J, Dharmasri L C. Remote sensing of spider mite damage in California peach orchards. International Journal of Applied Earth Observation and Geoinformation,2009,11:244-255.
72. Luo J H, Huang W J, Yuan L, Zhao C J, Du S Z, Zhang J C. Zhao J L. Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat. Precision Agriculture,2013, 14:151-161.
73. Luquet D, Begue A, Vidal A, Clouvel P, Dauzat J, Olioso A, Gu X F, Tao Y. Using multidirectional thermography to characterize water status of cotton. Remote Sensing of Environment,2003,34: 189-193.
74. Macedo T B, Weaver D K, Peterson R K D. Photosynthesis in wheat at the grain filling stage is altered by larval wheat stem sawfly (Hymenoptera:Cephidae) injury and reduced water availability. Journal of Entomological Science,2007,42:228-238.
75. Mahlein A K, Rumpf T, Welke P, Dehne H W, Plumer L, Steiner U, Oerke E C. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment,2013, 128:21-30.
76. Mahlein A K, Steiner U, Dehne H W, Oerke E C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture,2010,11:413-431.
77. Malthus T J and Madeira A C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by botrytis fabae. Remote Sensing of Environment,1993,45:107-116.
78. Manners J G Principles of plant pathology. Cambridge:Cambridge University Press,1982.
79. Maxwell S K, Nuckols J R, Ward M H, Hoffer R M. An automated approach to mapping corn from Landsat imagery. Computers and Electronics in Agriculture,2004,43:43-54.
80. Mello M O and Silva-Filho M C. Plant-insect interactions:An evolutionary arms race between two distinct defense mechanisms. Brazilian Journal of Plant Physiology,2002,14:71-81.
81. Merton R N. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, California, USA,1998:12-16.
82. Meyer D, Verstracete M, Pinty B. The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR. Remote Sensing of Environment,1995,12:3-27.
83. Mirik M, Ansley R, Michels G, Elliott N C. Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.). Precision Agriculture,2012,13:501-516.
84. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C. Reflectance characteristics of Russian wheat aphid (Hemiptera:Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture,2007,57:123-134.
85. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Bowling R. Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera:Aphididae). Journal of Economic Entomology,2006a,99:1682-1690.
86. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Catana V R. Spectral sensing of aphid (Hemiptera:Aphididae) density using field spectrometry and radiometry. Turkish journal of agriculture & forestry,2006b,30:421-428.
87. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Catana V R, Jiones D B, Bowling R. Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera:Aphididae) in winter wheat. Computers and Electronics in Agriculture,2006c,51: 86-98.
88. Mutanga O, Skidmore A K, van Wieren S. Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry and Remote Sensing,2003,57:263-272.
89. 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-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66:38-45.
90. Nathan S, Chung P, Murugan K. Effect of botanical insecticides and bacterial toxins on the gut enzyme of the rice leaf folder Cnaphalocrocis medinalis. Phytoparasitica,2004,32:433-443.
91. Nathan S S. Effects of Melia azedarach on nutritional physiology and enzyme activities of the rice leaffolder Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae). Pesticide Biochemistry and Physiology,2006,84:98-108.
92. Nathan S S, Kalaivani K, Murugan K, Gene Chung P. Efficacy of neem limonoids on Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae) the rice leaffolder. Crop Protection, 2005,24:760-763.
93. Nault B A, Taylor A G, Urwiler M, Rabaey T, Hutchison W D. Neonicotinoid seed treatments for managing potato leafhopper infestations in snap bean. Crop Protection,2004,23:147-154.
94. Nguyen H T and Lee B W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy,2006,24: 349-356.
95. Nilsson H E. Remote sensing and image analysis in plant pathology. Canadian Journal of Plant Pathology,1995,17:154-166.
96. Nuessly G S, Meyerdirk D E, Hart W G, Davis M R. Evaluation of color-infrared aerial photography as a tool for the identification of sweetpotato whitefly induced fungal and viral infestations of cotton and lettuce. Proceedings of the 11th biennial workshop on color aerial photography and videography in the plant sciences and related field, American Sociey of photogrammetry and remote sensing, Waslaco, Texas, USA,1987,141-148.
97. Pacumbaba J R O and Beyl C A. Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research,2011,48:32-42.
98. Padmavathi C, Katti G, Padmakumari A P. The effect of leaffolder Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae) injury on the plant physiology and yield loss in rice. Journal of Applied Entomology,2012,137:249-256.
99. Patel N K, Patnaik C, Dutta S, Shekh A M, Dave A. J. Study of crop growth parameters using airborne imaging spectrometer data. International Journal of Remote Sensing,2001,22:2401-2411.
100. Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica,1995,31:221-230.
101. Penuelas J, Filella I, Biel C, Serrano L, Save R. The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing,1993,14:1887-1905.
102. Penuelas J, Gamon J A, Fredeen A L, Merino J, Field C B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment,1994,48:135-146.
103. Pearson R L and Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Preceedings of 8th International Symposium on Remote Sensing of the Environment, Pawnee National Grasslands, Colorado,1972,1355-1379.
104. Peng Y and Gitelson A A. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment,2012,117:440-448.
105. Perry E M and Davenport J R. Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple. Computers and Electronics in Agriculture,2007,59:56-65.
106. Prabhakar M, Prasad Y G, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera:Cicadellidae). Computers and Electronics in Agriculture,2011,79:189-198.
107. Rabiei B A H EM and Kavousi M. Improvement of nitrogen management in rice paddy fields using chlorophyll meter (SPAD). Paddy and Water Environment,2008,6:181-188.
108. Rao N R, Garg P K,Ghosh S K. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture,2007,8: 173-185.
109. Reisig D and Godfrey L. Remote sensing for detection of cotton aphid-(Homoptera:Aphididae) and spider mite-(Acari:Tetranychidae) infested cotton in the San Joaquin Valley. Environmental Entomology,2006,35:1635-1646.
110. Reisig D and Godfrey L. Spectral response of cotton aphid-(Homoptera:Aphididae) and spider mite-(Acari:Tetranychidae) infested cotton:controlled studies. Environmental Entomology,2007, 36:1466-1474.
111. Riedell W E and Blackmer T M. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Science,1999,39:1835-1840.
112. Riedell W E, Osborne S L, Hesler L S. Insect pest and disease detection using remote sensing techniques. Proceedings of the 7th International Conference on Precision Agriculture, Hyatt Regency, Minneapolis, MN, USA.2005,1380-1387.
113. Riley J R. Remote sensing in entomology. Annual Review of Entomology,1989,34:247-271.
114. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment,1996,55:95-107.
115. Rothschild G. Rice Genetics Ⅲ, GS Khush, IRRI, Manila, Philippines,1996.
116. Rubia-Sanchez E, Suzuki Y, Miyamoto K, Watanabe T. The potentialfor compensation of the effects of the brown planthopper Nilaparvata lugens Stal (Homoptera:Delphacidae) feeding on rice. Crop Protection,1999,18:39-45.
117. Ryu C, Suguri M, Umeda M. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Field Crops Research,2011,122: 214-224.
118. Salisbury J W. Spectral measurements field guide. In:Defense Intelligense Agency Central Measurement and Signature Intelligence (MASINT) Office (CMO) Report No. AD362372, Fort belvoir,USA,1998,1-82.
119. Seek M, Roelfs A P, Teng P S. Influence of leaf position on yield loss caused by wheat leaf rust in single tillers. Crop Protection,1991,10:222-228.
120. Serrano L, Filella I, Penuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Protection,2000,40:723-731.
121. Shen S H, Yang S B, Li B B, Tan B X, Li Z Y, Toan T L. A scheme for regional rice yield estimation using ENVISATASAR data. Science in China Series D:Earth Sciences,2009,52:1183-1194.
122. Shibayama M and Akiyama T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989,27:119-127.
123. Shibayama M, Sakamoto T, Homma K, Okada S, Yamamoto H. Daytime and nighttime field spectral imagery of ripening paddy rice for determining leaf greenness and 1000-grain weight. Plant Production Science,2009,12:307-318.
124. Shibayama M, Takahashi W, Morinaga S, Akiyama T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sensing of Environment,1993,45: 117-126.
125. Sims D A and Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment,2003,84:526-537.
126. Singh D, Sao R, Singh K P. A remote sensing assessment of pest infestation on sorghum. Advances in Space Research,2007,39:155-163.
127. Singh K, Agrawal K N, Bora G C. Advanced techniques for weed and crop identification for site specific weed management. Biosystems Engineering,2011,109:52-64.
128. Singh M, Niwas R, Khichar M, Yadav M K. Spectral models for estimation of chlorophyll content, growth and yield of wheat crop. Journal of the Indian Society of Remote Sensing,2006,34:1-5.
129. Sogawa K. Studies on feeding habits of the brown planthopper. I. Effects of nitrogen-deficiency of host plant on insect feeding. Journal of Applied Entomology and Zoology,1970,14:101-106.
130. Sogawa K, Feeding behavior and damage mechanisms of the rice planthoppers. In:Elings, A., Rubia, E. (Eds.), Analysis of Damage Mechanisms by Pests and Diseases and Their Effects on Rice Yield. SARP Research Proceedings. Department of Theoretical Production Ecology, DLO Research Institute for Agrobiology and Soil Fertility, Wageningen, IRRI, Manila, Philippines,1994,143-154.
131. Sogawa K. Effects of feeding of the brown planthopper on the components in the leaf blade of rice plants. Japanese Journal of Applied Entomology and Zoology,1971,15:175-179.
132. Sogawa K. The rice brown planthopper:feeding physiology and host plant interactions. Annual Review of Entomology,1982,27:49-73.
133. Suarez L, Zarco-Tejada P J, Sepulcre-Canto G, Perez-Priego O, Miller J R, Jimenez-Munoz J C, Sobrino J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sensing of Environment,2008,112:560-575.
134. Suarez L, Zarco-Tejada P J, Berni J A J, Gonzalez-Dugo V, Fereres E. Modelling PRI for water stress, detection using radiative transfer models. Remote Sensing of Environment,2009,113: 730-744.
135. Sudbrink D L, Harris F A, Robbins J T, English P J, Willers J L. Evaluation of remote sensing to identify variability in cotton plant growth and correlation with larval densities of beet armyworm and cabbage looper(Lepidoptera:Noctuidae). Florida Entomologist,2003,86:290-294.
136. Tian Y C, Yao X, Yang J, Cao W X, Hannaway D B, Zhu Y. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research,2011,120:299-310.
137. Vogelmann J E, Rock B N, Moss D M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing,1993,14:1563-1575.
138. Wahid D A, Ishiguro E, Shimotashiro T, Hirayama S, Ueda K. Study on relationships among LAI, DW, fPAR and spectral reflectance in paddy rice. Journal of Agricultural Meteorology,2003,59: 13-21.
139. Wang F M, Huang J F, Wang Y, Liu Z Y, Zhang F Y. Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines. Precision Agriculture,2013,14: 172-183.
140. Wang S H, Zhu Y, Jiang H D, Cao W X. Positional differences in nitrogen and sugar concentrations of upper leaves relate to plant N status in rice under different N rates. Field Crops Research,2006, 96:224-234.
141.Watanabe T and Kitagawa H. Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera:Delphacidae). Journal of Economic Entomology,2000,93:1192-1198.
142. Welter S C. Arthropod impact on plant gas exchange. Insect-plant interactions,1989,1:135-150.
143. Wu C Y, Niu Z, Tang Q, Huang W J, Rivard B, Feng J L. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology,2009,149:1015-1021.
144. Wu C Y, Niu Z, Tang Q, Huang W J. Estimating chlorophyll content from hyperspectral vegetation indices:Modeling and validation. Agricultural and Forest Meteorology,2008,148:1230-1241.
145. Xu H R, Ying Y B, Fu X P, Zhu S P. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering,2007,96:447-454.
146. Yamagata Y, Wiegand C, Akiyama T, Shibayama M. Water turbidity and perpendicular vegetation indices for paddy rice flood damage analyses. Remote Sensing of Environment,1988,26:241-251.
147. Yang C H. A high-resolution airborne four-camera imaging system for agricultural remote sensing. Computers and Electronics in Agriculture,2012,88:13-24.
148. Yang C H and Everitt J H. Remote sensing for detecting and mapping whitefly(Bemisia tabaci) infestations, The Whitefly, Bemisia tabaci (Homoptera:Aleyrodidae) Interaction with Geminivirus-infected Host Plants. W M O Thompson, Springer Netherlands,2011,357-381.
149. Yang C M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11:61-81.
150. Yang C M and Cheng C H. Spectral characteristics of rice plants infested by brown planthoppers. Proc. Natl. Sci. Counc. ROC(B),2001,25:180-186.
151. Yang C M, Cheng C H, Chen R K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science,2007,47:329-335.
152. Yang C M and Cheng R K. Modeling rice growth using hyperspectral reflectance data. Crop Science,2004,44:1283-1290.
153. Yang F, Li J L, Gan X Y, Qian Y R, Wu X L, Yang Q. Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Computers and Electronics in Agriculture,2010,70:52-59.
154. Yang Z, Rao M N, Elliott N C, Kindler S D, Popham T W. Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture,2009,67:64-70.
155. Zangerl A R, Hamilton J G, Miller T J, Crofts A R, Oxborough K, Berenbaum M R, De Lucia E H. Impact of folivory on photosynthesis is greater than the sum of its holes. Proceedings of the National Academy of Sciences,2002,99:1088-1091.
156. Zarco-Tejada P J, Miller J R, Mohammed G H, Noland T L, Sampson P H. Estimation of chlorophyll fluorescence under natural illumination from hyperspectral data. International Journal of Applied Earth Observation and Geoinformation,2001,3:321-327.
157. Zhang Y Q, Chen J M, Miller J R, Noland T L. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment,2008,112:3234-3247.
158. Zhao J L, Zhang D Y, Luo J H, Wang D C, Huang W J. Identifying leaf-scale wheat aphids using the near-ground hyperspectral pushbroom imaging spectrometer, Springer Berlin Heidelberg, Computer and Computing Technologies in Agriculture V,5th IFIP TC 5/SIG 5.1 Conference, CCTA, Beijing, China,2012,275-282.
159. Zhou Q F and Wang J H. Comparison of upper leaf and lower leaf of rice plants in response to supplemental nitrogen levels. Journal of Plant Nutrition,2003,26:607-617.
160.安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘技术.农业工程学报,2007,23：181-186.
161.包刚,覃志豪,周义,包玉海,辛晓平,红雨,海全胜.基于高光谱数据和RBF神经网络方法的草地叶面积指数反演.国土资源遥感,2012,73：7-11.
162.崔应峰,王世平.葡萄病害智能诊断网络模型的比较.上海交通大学学报(农业科学版),2011,29：79-86.
163.陈鹏程,张建华,李眉眉,雷勇辉.土耳其斯坦叶螨为害棉叶的生理变化及光谱特征分析.昆虫知识,2007,44：61-65.
164.陈兵.棉花黄萎病高光谱特征及其光谱诊断模型研究.石河子大学,2007.
165.程家安,祝增荣.2005年长江流域稻区褐飞虱暴发成灾原因分析.植物保护,2006,32：1-4.
166.程乾,巫坤.受褐飞虱破坏的水稻光谱特性研究.安徽农业科学,2011,39：20553-20554.
167.程遐年,陈若篪,习学,杨联民,朱子龙,吴进才,钱仁贵,杨金生.稻褐飞虱迁飞规律的研究.昆虫学报,1979,22：1-21.
168.何国金,胡德永,金小华,杨建国,李国扬.北京麦蚜虫害的光谱测量与分析.遥感技术与应用,2002,17：199-205.
169.黄春燕,王登伟,陈冠文,袁杰,祁亚琴,陈燕,程诚.基于高光谱植被指数的棉花干物质积累估算模型研究.棉花学报,2006,18：115-119.
170.黄敬峰,王渊,王福民,刘占宇.油菜红边特征及其叶面积指数的高光谱估算模型.农业工程学报,2006,22：22-26.
171.黄文江,王纪华,刘良云,王锦地,谭昌伟,李存军,王之杰,宋晓宇.基于多时相和多角度光谱信息的作物株型遥感识别初探.农业工程学报,2005,21：82-86.
172.龚敏庆,孙明伟,金明仲.PLS分析与RBF神经网络耦合环境模型.数理统计与管理,2011,5：840-845.
173.季荣,张霞,谢宝瑜,李哲,刘团结,刘闯,李典谟.用MODIS遥感数据检测东亚飞蝗灾害-以河北省南大港为例.昆虫学报,2003,46：713-719.
174.李波,刘占宇,黄敬峰,张莉丽,周湾,石晶晶.基于PCA和PNN的水稻病虫害高光谱识别.农业工程学报,2009,25：143-147.
175.李世欣,温建,邵孝侯,王晓亚,王玉英.偏最小二乘法与人工神经网络耦合的小流域产沙模型.河海大学学报(自然科学版),2010,38：149-153.
176.李云梅,王人潮,王秀珍,沈掌泉.水稻冠层二向反射率的动态变化.浙江大学学报(农业与生命科学版),2001,27：37-42.
177.李云梅,王人潮,王秀珍.水稻冠层二向反射率的模拟及其反演.中国水稻科学,2002,16：291-294.
178.梁雪,吉海彦,王鹏新,饶震红,申兵辉.用MSC-ANN方法建立冬小麦叶片叶绿素与反射光谱的定量分析模型研究.光谱学与光谱分析,2010,30：188-191.
179.林萍,陈永明,何勇.基于可见近红外光谱的糖类别快速鉴别研究.光谱学与光谱分析,2009,29：382-385.
180.刘国顺,李向阳,刘大双,喻奇伟.利用冠层光谱估测烟草叶面积指数和地上生物量.生态学报,2007,27：1763-1771.
181.刘伟东,项月琴,郑兰芬,童庆禧,吴长山.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析.遥感学报,2000,4：279-283.
182.刘占宇,黄敬峰,陶荣祥,张红志.基于主成分分析和径向基网络的水稻胡麻斑病严重度估测.光谱学与光谱分析,2008,28：2156-2160.
183.刘占宇.水稻主要病虫害胁迫遥感监测研究.浙江大学,2008.
184.卢辉,韩建国,张录达.高光谱遥感模型对亚洲小车蝗危害程度研究.光谱学与光谱分析,2009,29：745-748.
185.卢艳丽,王纪华,李少昆,谢瑞芝,高世菊,马达灵.冬小麦不同群体冠层结构的高光谱响应研究.中国农业科学,2005,38：911-915.
186.孔薇,杨杰.基于径向基神经网络的聚丙烯熔融指数预报.化工学报,2003,54：1160-1163.
187.马建文,韩秀珍,哈斯巴干,张跃进,汤金仪,谢志庾.东亚飞蝗灾害的遥感监测实验.国土资源遥感,2003,55：51-55.
188.浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000.
189.千怀遂.农作物遥感估产最佳时相的选择研究-以中国主要粮食作物为例.生态学报,1998,18：50-57.
190.乔红波,蒋金炜,程登发,陈胜利,刘建安,马继盛.烟蚜为害特征的高光谱比较.昆虫知识,2007,44：57-61.
191.乔红波,陈登发,孙京瑞,田喆,陈林,林芙蓉.麦蚜对小麦冠层光谱特性的影响研究.植物保护,2005,31：21-26.
192.邱白晶,陈国平,程麒文.水稻白背飞虱虫害的冠层光谱特性与虫量反演.农业机械学报,2008,39：92-95.
193.石晶晶,刘占宇,张莉丽,周湾,黄敬峰.基于支持向量机(SVM)的稻纵卷叶螟危害水稻高光谱遥感识别.中国水稻科学,2009,23：331-337.
194.孙红,李民赞,周志艳,刘刚,罗锡文.基于光谱技术的水稻稻纵卷叶螟受害区域检测.光谱学与光谱分析,2010,30：1080-1083.
195.孙启花.褐飞虱和稻纵卷叶螟为害后水稻的光谱特征.南京农业大学,2010.
196.孙启花,刘向东.褐飞虱危害在水稻植株光谱反射率上的表现.中国水稻科学,2010,24：203-209.
197.孙启花,刘向东.稻纵卷叶螟危害孕穗期水稻的光谱诊断.中国农业科学,2012,45：5040-5048.
198.谭琨,杜培军.基于径向基函数神经网络的高光谱遥感图像分类.光谱学与光谱分析,2008,28：2009-2013.
199.汤旭光,宋开山,刘殿伟,王宗明,张柏,杜嘉,曾丽红,姜广甲,王远东.基于可见/近红外反射光谱的大豆叶绿素含量估算方法比较.光谱学与光谱分析,2011,31：371-374.
200.唐延林,王秀珍,王福民,王人潮.农作物LA I和生物量的高光谱法测定.西北农林科技大学学报(自然科学版),2004,32：100-104.
201.王大成,王纪华,靳宁,王芊,李存军,黄敬峰,王渊,黄芳.用神经网络和高光谱植被指数估算小麦生物量.农业工程学报,2008,24：197-201
202.王芳,卓莉,黎夏,夏丽华.基于高光谱特征选择和RBFNN的城市植被胁迫程度监测.地理科学,2008,28：77-82.
203.王惠文.偏最小二乘回归方法及其应用,北京：国防工业出版社,1999.
204.王新忠,李大鹏.基于主成分回归法的稻飞虱百穴虫量冠层光谱检测.安徽农业科学,2010,38：12695-12696.
205.王秀珍,黄敬峰,李云梅,王人潮.水稻生物化学参数与高光谱遥感特征参数的相关分析.农业工程学报,2003a,19：144-148.
206.王秀珍,黄敬峰,李云梅,王人潮.水稻地上鲜生物量的高光谱遥感估算模型研究.作物学报,2003b,29：815-821.
207.王秀珍,黄敬峰,李云梅,王人潮.水稻叶面积指数的高光谱遥感估算模型.遥感学报,2004,8：81-88.
208.王渊.不同水平油菜氮素含量遥感信息提取方法研究.浙江大学,2008.
209.翁欣欣,陆峰,王传现,亓云鹏.近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析.光谱学与光谱分析,2009,29：3283-3287.
210.吴达科,马承伟,杜尚丰.斑潜蝇虫害叶片受害程度对其近红外反射光谱的影响.农业工程学报,2007,23：156-159.
211.吴昊,邱白晶,陈国平,刘继承.水稻白背飞虱虫害的单叶高光谱特征分析.农机化研究,2009,4：117-119.
212.吴继友,倪健.松毛虫危害的光谱特征与虫害早期探测模式.环境遥感,1995,10：250-251.
213.吴彤,倪绍祥,李云梅,周欣欣,陈建.基于高光谱数据的东亚飞蝗危害程度监测.遥感学报,2007,11：103-108.
214.肖艳芳,宫辉力,周德民.基于因子分析的苜蓿叶片叶绿素高光谱反演研究.生态学报,2012,32：3098-3106.
215.谢传奇,冯雷,冯斌,李晓丽,刘飞,何勇.茄子灰霉病叶片过氧化氢酶活性与高光谱图像特征关联方法.农业工程学报,2012,28：177-184.
216.邢东兴,常庆瑞.基于光谱反射率的果树病虫害级别定量化测评-以红富士苹果树黄叶病害、红蜘蛛虫害为例.西北农林科技大学学报(自然科学版),2009,37：143-155.
217.熊勤学,吴涛.2006年荆州市稻飞虱危害的遥感分析及评估.遥感应用,2008,5：41-44.
218.许章华,刘健,余坤勇,龚从宏,谢婉君,唐梦雅,赖日文,李增禄.松毛虫危害马尾松光谱特征分析与等级检测.光谱学与光谱分析,2013,33：428-433.
219.杨建国,金晓华,郭永旺,石克强,严文胜.遥感技术麦蚜监测应用研究.中国农学通报,2001,17：4-7.
220.杨晓华,黄敬峰,王秀珍,王福民.基于支持向量机的水稻叶面积指数高光谱估算模型研究.光谱学与光谱分析,2008,28：1837-1841.
221.姚付启,张振华,杨润亚,张燕,王海江,崔素芳.基于高光谱的法国梧桐和毛白杨叶片叶绿素含量反演模型研究.鲁东大学学报(自然科学版),2008,24：353-357.
222.张晓阳,李劲峰.利用垂直植被指数推算作物叶面积系数的理论模式.遥感技术与应用,1995,10：13-18.
223.赵德华,李建龙.高光谱技术提取不同作物叶片类胡萝卜素信息.遥感信息,2004,3：13-17.
224.朱登胜,邵咏妮,潘家志,何勇.应用多光谱数字图像识别苗期作物与杂草.浙江大学学报(农业与生命科学版),2008,34：418-422.
225.赵英时.遥感应用分析原理与方法.北京：科学出版社,2002.
2. Abou-Ismail O, Huang J F, Wang R C. Rice yield estimation by integrating remote sensing with rice growth simulation model. Pedosphere,2004,14:519-526.
3. 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,1999,20:3663-3675.
4. Aggarwal P K, Banerjee B, Daryaei M G, Pathak H. InfoCrop:A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. Ⅱ. Performance of the model. Agricultural Systems,2006,89:47-67.
5. Aldea M, Hamilton J, Resti J, Zangerl A R, Berenbaum M R, Frank T D, DeLucia E H. Comparison of photosynthetic damage from arthropod herbivory and pathogen infection in understory hardwood saplings. Oecologia,2006,149:221-232.
6. Aldea M, Hamilton J G, Resti J P, Zangerl A R, Berenbaum M R. Indirect effects of insect herbivory on leaf gas exchange in soybean. Plant, Cell & Environment,2005,28:402-411.
7. Apan A, Dart B, Kelly R. Detection of pests and diseases in vegetable crops using hyperspectral sensing:a comparison of reflectance data for different sets of symptoms. SSC2005 Spatial Sciences Institute Biennial Conference. Spatial Sciences Institute, Melbourne, Australia, September 2005: 12-16.
8. Asner G P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment,1998,64:234-253.
9. Backoulou G F, Elliott N C, Giles K, Phoofolo M, Catana V. Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia. Computers and Electronics in Agriculture,2010,75:64-70.
10. Bae S H and Pathak M D. Life history of Nilaparvata lugens (Homoptera:Delphacidae) and susceptibility of rice varieties to its attacks. Annals of the Entomological Society of America,1969, 63:149-155.
11. Barnes J, Balaguer L, Manrique E, Elvira S, Davison A W. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls (a and b) in lichens and higher plants. Environmental and Experimental Botany,1992,32:85-100.
12. Bautista R C, Heinrichs E A, Rejesus R S. Economic injury levels for the rice leaffolder Cnaphalocrocis medinalis (Lepidoptera:Pyralidae):Insect infestation and artificial leaf removal. Environmental Entomology,1984,13:439-443.
13. Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales:An evaluation of some hyperspectral approaches. Remote Sensing of Environment,1998,66:273-285.
14. Blanchfield A L, Robinson S A, Renzullo L J, Powell K S. Phylloxera-infested grapevines have reduced chlorophyll and increased photoprotective pigment content:Can leaf pigment composition aid pest detection? Functional plant biology,2006,33:507-514.
15. Boote K J, Jones J W, Mishoe J W, Berger R D. Coupling pests to crop growth simulators to predict yield reduction. Phytopathology,1983,73:1581-1587.
16. Bottrell D G and Schoenly K G. Resurrecting the ghost of green revolutions past:The brown planthopper as a recurring threat to high yielding rice production in tropical Asia. Journal of Asia-Pacific Entomology,2011,15:122-140.
17. Capucho A S, Zambolim L, Duarte H S S, Parreira D F, Ferreira P A, Lanza F E, Cota L V. Influence of leaf position that correspond to whole plant severity and diagrammatic scale for white spot of corn. Crop Protection,2010,29:1015-1020.
18. Carter G A. Responses of leaf spectral reflectance to plant stress. American Journal of Botany,1993, 80:239-243.
19. Carter G A, Miller R L. Early Detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment,1994,50:295-302.
20. Carvalho S, Macel M, Schlerf M, Skidmore A K, Putten W H. Soil biotic impact on plant species shoot chemistry and hyperspectral reflectance patterns. New Phytologist,2012,196:1133-1144.
21. Casanova D, Epema G F, Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research,1998,55:83-92.
22. Chappelle E W, Kim M S, McMurtrey I J E. Ratio analysis of reflectance spectra (RARS):An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment,1992,39:239-247.
23. Choudhary R, Mahesh S, Paliwal J, Jayas D S. Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering,2009, 102:115-127.
24. Cohen Y and Shoshany M. A national knowledge-based crop recognition in Mediterranean environment. International Journal of Applied Earth Observation and Geoinformation,2002,4: 75-87.
25. Dai Y J, Shao M M, Hannaway D, Wang L L, Liang J P, Hu L N, Lu H F. Effect of Thrips tabaci on anatomical features, photosynthetic characteristics and chlorophyll fluorescence of Hypericum sampsonii leaves. Crop Protection,2009,28:327-332.
26. Daughtry C S T, Walthall C L, Kim M S, de Colstoun E B, McMurtrey Iii J E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment,2000, 74:229-239.
27. Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus Leaves. Remote Sensing of Environment,1998,66:111-121.
28. de Kraker J, Rabbinge R, van Huis A, van Lenteren J C, Heong K L. Impact of nitrogenous-fertilization on the population dynamics and natural control of rice leaffolders (Lep.: Pyralidae). International Journal of Pest Management,2000,46:225-235.
29. Delalieux S, Somers B, Verstraeten W W, van Aardt J A N, Keulemans W, Coppin P. Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology. International Journal of Remote Sensing,2009,30:1887-1912.
30. Delegido J, Alonso L, Gonzalez G, Moreno J. Estimating chlorophyll content of crops from hyperspectral data using a Normalized Area Over reflectance Curve (NAOC). International Journal of Applied Earth Observation and Geoinformation,2010,12:165-174.
31. Diaz-montano J, Reese J C, Schapaugh W T, Campbell L R. Chlorophyll loss caused by soybean aphid (Hemiptera:Aphididae) feeding on soybean. Journal of Economic Entomology,2007,100: 1657-1662.
32. Drake J M and Griffen B D. Early warning signals of extinction in deteriorating environments. Nature,2010,467:456-459.
33. Elliott N, Mirik M, Yang Z, Dvorak T, Rao M, Michels J, Walker T, Catana V, Phoofolo M, Giles K, Royer T. Airborne multi-spectral remote sensing of Russian wheat aphid injury to wheat. Southwestern Entomologist,2009,32:213-219.
34. Elvidge C D and Chen Z K. Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment,1995,54:38-48.
35. Fitzgerald G J, Maas S J, Detar W R. Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Precision Agriculture,2004,5: 275-289.
36. Fitzgerald G J, Maas S J, Detar W R. Early detection of spider mites in cotton using multispectral remote sensing, Proceedings of the Beltwide Cotton Conference,03-07 January, Orlando, Florida, 1999,1022-1024.
37. Franzen L D, Gutsche A R, Heng-Moss T M, Higley L G, Sarath G, Burd J D. Physiological and biochemical responses of resistant and susceptible wheat to onjury by Russian wheat aphid. Journal of Economic Entomology,2007,100:1692-1703.
38. Fraulo A B, Cohen M, Liburd O E. Visible/Near infrared reflectance (VNIR) spectroscopy for detecting twospotted spider mite (Acari:Tetranychidae) damage in strawberries. Environmental Entomology,2009,38:137-142.
39. Gamon J A and Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytologist,1999,143:105-117.
40. Gamon J A, Penuelas J, Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment,1992,41:35-44.
41. Garbulsky M, Penuelas J, Gamon J, Inoue Y, Filella I. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies:A review and meta-analysis. Remote Sensing of Environment,2010,115:281-297.
42. Gemmell F and Varjo J. Utility of reflectance model inversion versus two spectral indices for estimating biophysical characteristics in a boreal forest test site. Remote Sensing of Environment, 1999,68:95-111.
43. Genc H, Genc L, Turhan H, Smith S E, Nation J L. Vegetation indices as indicators of damage by the sunn pest (Hemiptera:Scutelleridae) to field grown wheat. African Journal of Biotechnology, 2008,7:173-180.
44. Gerloff E D and Ortman E E. Physiological changes in barley induced by greenbug feeding stress. Crop Science,1971,11:174-176.
45. Gholizadeh A, Amin M S M, Anuar A R, Aimrun W. Evaluation of SPAD chlorophyll meter in two Ddifferent rice growth stages and its temporal variability. European Journal of Scientific Research, 2009,37:591-598.
46. Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology,2003,160:271-282.
47. Gitelson A A, Vina A, Ciganda V, Rundquist D C, Arkebauer T J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters,2005,32:L08403.
48. Gottfried F, Faheemah, F. The spinning (Stitching) behaviour of the rice leaf folder, Cnaphalocrocis medinalis. Entomologia Experimentalis et Applicata,1981,29:138-146.
49. Gutierrez M, Norton R, Thorp K R, Wang G. Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Science,2012,52:849-857.
50. Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment,2002,81:416-426.
51. Ham F M, Kostanic I. Principles of neurocomputing for science and engineering. New York: McGraw-Hill Higher Education,2000, p:140-152.
52. Heng-moss T, Macedo T, Franzen L, Baxendale F, Higley L, Sarath G. Physiological Responses of Resistant and Susceptible Buffalograsses to Blissus Occiduus (Hemiptera:Blissidae) Feeding. Journal of Economic Entomology,2006,99:222-228.
53. Hibino H, Usugi T, Omura T, Tsuchizaki T, Shohara K, Iwasaki M. Rice grassy stunt virus:a planthopper-borne circular filament. Phytopathology,1985,75:894-899.
54. Hoque E, Hutzler P J S, Hiendl H. Reflectance, colour, and histological features as parameters for the early assessment of forest damages. Canadian Journal of Remote Sensing,1992,18:104-110.
55. Houborg R, Anderson M, Daughtry C. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale. Remote Sensing of Environment,2009,113:259-274.
56. INGER Genetic Resources Center. Standard evaluation system for rice. IRRI, Manila, Philippines, 1996,29.
57. Inoue Y, Sakaiya E, Zhu Y, Takahashi W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment,2012,126:210-221.
58. Jong P D. Effect of folding and feeding by Cnaphalocrocis medinalis on photosynthesis and transpiration of rice leaves. Entomologia Experimentalis et Applicata,1992,63:101-102.
59. Ju C H, Tian Y C, Yao X, Cao W X, Zhu Y, Hannaway D. Estimating leaf chlorophyll content using red edge parameters. Pedosphere,2010,20:633-644.
60. Kieckhefer R W, Gellner J L, Riedell W E. Evaluation of the aphid-day standard as a predictor of yield loss caused by cereal aphids. Agronomy Journal,1995,87:785-788.
61. Kochubey S M and Kazantsev T A. Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology,2007,164: 1648-1655.
62. Krause G H and Weis E. Chlorophyll fluorescence and photosynthesis:The basics. Annual Review of Plant Physiology and Plant Molecular Biology,1991,42:313-349.
63. Kumar J, Vashisth A, Sehgal V, Gupta V K. Assessment of aphid infestation in mustard by hyperspectral remote sensing. Journal of the Indian Society of Remote Sensing,2012,1-8.
64. Li W G, Li H, Zhao L H. Estimating rice yield by HJ-1 A satellite images. Rice Science,2011,18: 142-147.
65. Lin F F, Qiu L F, Deng J S, Shi Y Y, Chen L S, Wang K. Investigation of SPAD meter-based indices for estimating rice nitrogen status. Computers and Electronics in Agriculture,2010,71:60-65.
66. Lu Y L, Li S K, Bai Y L, Jones C L, Wang J H. Canopy spatial distribution and identification using hyperspectal data in winter wheat. Communications in Soil Science and Plant Analysis,2009,40: 1240-1253.
67. Lu Z X, Heong K L, Yu X P, Hu C. Effects of plant nitrogen on ecological fitness of the brown planthopper, Nilaparvata lugens Stal. in rice. Journal of Asia-Pacific Entomology,2004,7:97-104.
68. Lu Z X, Heong K L, Yu X P, Hu C. Effects of nitrogen on the tolerance of brown planthopper, Nilaparvata Lugens, to adverse environmental factors. Insect Science,2005,12:121-128.
69. Lu Z X, Heong K L. Effects of nitrogen-enriched rice plants on ecological fitness of planthoppers. Planthoppers:New threats to the sustainability of intensive rice production systems in Asia. K L Heong, Hardy, B. IRRI, Los Banos, Philippines,2009,247-256.
70. Lu Z X, Yu X P, Heong K L, Hu C. Effect of nitrogen fertilizer on herbivores and its stimulation to major insect pests in rice. Rice Science,2007,14:56-66.
71. Luedeling E, Hale A, Zhang M, Bentley W J, Dharmasri L C. Remote sensing of spider mite damage in California peach orchards. International Journal of Applied Earth Observation and Geoinformation,2009,11:244-255.
72. Luo J H, Huang W J, Yuan L, Zhao C J, Du S Z, Zhang J C. Zhao J L. Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat. Precision Agriculture,2013, 14:151-161.
73. Luquet D, Begue A, Vidal A, Clouvel P, Dauzat J, Olioso A, Gu X F, Tao Y. Using multidirectional thermography to characterize water status of cotton. Remote Sensing of Environment,2003,34: 189-193.
74. Macedo T B, Weaver D K, Peterson R K D. Photosynthesis in wheat at the grain filling stage is altered by larval wheat stem sawfly (Hymenoptera:Cephidae) injury and reduced water availability. Journal of Entomological Science,2007,42:228-238.
75. Mahlein A K, Rumpf T, Welke P, Dehne H W, Plumer L, Steiner U, Oerke E C. Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment,2013, 128:21-30.
76. Mahlein A K, Steiner U, Dehne H W, Oerke E C. Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture,2010,11:413-431.
77. Malthus T J and Madeira A C. High resolution spectroradiometry:Spectral reflectance of field bean leaves infected by botrytis fabae. Remote Sensing of Environment,1993,45:107-116.
78. Manners J G Principles of plant pathology. Cambridge:Cambridge University Press,1982.
79. Maxwell S K, Nuckols J R, Ward M H, Hoffer R M. An automated approach to mapping corn from Landsat imagery. Computers and Electronics in Agriculture,2004,43:43-54.
80. Mello M O and Silva-Filho M C. Plant-insect interactions:An evolutionary arms race between two distinct defense mechanisms. Brazilian Journal of Plant Physiology,2002,14:71-81.
81. Merton R N. Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, California, USA,1998:12-16.
82. Meyer D, Verstracete M, Pinty B. The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR. Remote Sensing of Environment,1995,12:3-27.
83. Mirik M, Ansley R, Michels G, Elliott N C. Spectral vegetation indices selected for quantifying Russian wheat aphid (Diuraphis noxia) feeding damage in wheat (Triticum aestivum L.). Precision Agriculture,2012,13:501-516.
84. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C. Reflectance characteristics of Russian wheat aphid (Hemiptera:Aphididae) stress and abundance in winter wheat. Computers and Electronics in Agriculture,2007,57:123-134.
85. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Bowling R. Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera:Aphididae). Journal of Economic Entomology,2006a,99:1682-1690.
86. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Catana V R. Spectral sensing of aphid (Hemiptera:Aphididae) density using field spectrometry and radiometry. Turkish journal of agriculture & forestry,2006b,30:421-428.
87. Mirik M, Michels J G J, Kassymzhanova-Mirik S, Elliott N C., Catana V R, Jiones D B, Bowling R. Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera:Aphididae) in winter wheat. Computers and Electronics in Agriculture,2006c,51: 86-98.
88. Mutanga O, Skidmore A K, van Wieren S. Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry and Remote Sensing,2003,57:263-272.
89. 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-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture,2009,66:38-45.
90. Nathan S, Chung P, Murugan K. Effect of botanical insecticides and bacterial toxins on the gut enzyme of the rice leaf folder Cnaphalocrocis medinalis. Phytoparasitica,2004,32:433-443.
91. Nathan S S. Effects of Melia azedarach on nutritional physiology and enzyme activities of the rice leaffolder Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae). Pesticide Biochemistry and Physiology,2006,84:98-108.
92. Nathan S S, Kalaivani K, Murugan K, Gene Chung P. Efficacy of neem limonoids on Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae) the rice leaffolder. Crop Protection, 2005,24:760-763.
93. Nault B A, Taylor A G, Urwiler M, Rabaey T, Hutchison W D. Neonicotinoid seed treatments for managing potato leafhopper infestations in snap bean. Crop Protection,2004,23:147-154.
94. Nguyen H T and Lee B W. Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy,2006,24: 349-356.
95. Nilsson H E. Remote sensing and image analysis in plant pathology. Canadian Journal of Plant Pathology,1995,17:154-166.
96. Nuessly G S, Meyerdirk D E, Hart W G, Davis M R. Evaluation of color-infrared aerial photography as a tool for the identification of sweetpotato whitefly induced fungal and viral infestations of cotton and lettuce. Proceedings of the 11th biennial workshop on color aerial photography and videography in the plant sciences and related field, American Sociey of photogrammetry and remote sensing, Waslaco, Texas, USA,1987,141-148.
97. Pacumbaba J R O and Beyl C A. Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research,2011,48:32-42.
98. Padmavathi C, Katti G, Padmakumari A P. The effect of leaffolder Cnaphalocrocis medinalis (Guenee) (Lepidoptera:Pyralidae) injury on the plant physiology and yield loss in rice. Journal of Applied Entomology,2012,137:249-256.
99. Patel N K, Patnaik C, Dutta S, Shekh A M, Dave A. J. Study of crop growth parameters using airborne imaging spectrometer data. International Journal of Remote Sensing,2001,22:2401-2411.
100. Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica,1995,31:221-230.
101. Penuelas J, Filella I, Biel C, Serrano L, Save R. The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing,1993,14:1887-1905.
102. Penuelas J, Gamon J A, Fredeen A L, Merino J, Field C B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment,1994,48:135-146.
103. Pearson R L and Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Preceedings of 8th International Symposium on Remote Sensing of the Environment, Pawnee National Grasslands, Colorado,1972,1355-1379.
104. Peng Y and Gitelson A A. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment,2012,117:440-448.
105. Perry E M and Davenport J R. Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple. Computers and Electronics in Agriculture,2007,59:56-65.
106. Prabhakar M, Prasad Y G, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B. Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera:Cicadellidae). Computers and Electronics in Agriculture,2011,79:189-198.
107. Rabiei B A H EM and Kavousi M. Improvement of nitrogen management in rice paddy fields using chlorophyll meter (SPAD). Paddy and Water Environment,2008,6:181-188.
108. Rao N R, Garg P K,Ghosh S K. Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture,2007,8: 173-185.
109. Reisig D and Godfrey L. Remote sensing for detection of cotton aphid-(Homoptera:Aphididae) and spider mite-(Acari:Tetranychidae) infested cotton in the San Joaquin Valley. Environmental Entomology,2006,35:1635-1646.
110. Reisig D and Godfrey L. Spectral response of cotton aphid-(Homoptera:Aphididae) and spider mite-(Acari:Tetranychidae) infested cotton:controlled studies. Environmental Entomology,2007, 36:1466-1474.
111. Riedell W E and Blackmer T M. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Science,1999,39:1835-1840.
112. Riedell W E, Osborne S L, Hesler L S. Insect pest and disease detection using remote sensing techniques. Proceedings of the 7th International Conference on Precision Agriculture, Hyatt Regency, Minneapolis, MN, USA.2005,1380-1387.
113. Riley J R. Remote sensing in entomology. Annual Review of Entomology,1989,34:247-271.
114. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment,1996,55:95-107.
115. Rothschild G. Rice Genetics Ⅲ, GS Khush, IRRI, Manila, Philippines,1996.
116. Rubia-Sanchez E, Suzuki Y, Miyamoto K, Watanabe T. The potentialfor compensation of the effects of the brown planthopper Nilaparvata lugens Stal (Homoptera:Delphacidae) feeding on rice. Crop Protection,1999,18:39-45.
117. Ryu C, Suguri M, Umeda M. Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Field Crops Research,2011,122: 214-224.
118. Salisbury J W. Spectral measurements field guide. In:Defense Intelligense Agency Central Measurement and Signature Intelligence (MASINT) Office (CMO) Report No. AD362372, Fort belvoir,USA,1998,1-82.
119. Seek M, Roelfs A P, Teng P S. Influence of leaf position on yield loss caused by wheat leaf rust in single tillers. Crop Protection,1991,10:222-228.
120. Serrano L, Filella I, Penuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Protection,2000,40:723-731.
121. Shen S H, Yang S B, Li B B, Tan B X, Li Z Y, Toan T L. A scheme for regional rice yield estimation using ENVISATASAR data. Science in China Series D:Earth Sciences,2009,52:1183-1194.
122. Shibayama M and Akiyama T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sensing of Environment, 1989,27:119-127.
123. Shibayama M, Sakamoto T, Homma K, Okada S, Yamamoto H. Daytime and nighttime field spectral imagery of ripening paddy rice for determining leaf greenness and 1000-grain weight. Plant Production Science,2009,12:307-318.
124. Shibayama M, Takahashi W, Morinaga S, Akiyama T. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sensing of Environment,1993,45: 117-126.
125. Sims D A and Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment,2003,84:526-537.
126. Singh D, Sao R, Singh K P. A remote sensing assessment of pest infestation on sorghum. Advances in Space Research,2007,39:155-163.
127. Singh K, Agrawal K N, Bora G C. Advanced techniques for weed and crop identification for site specific weed management. Biosystems Engineering,2011,109:52-64.
128. Singh M, Niwas R, Khichar M, Yadav M K. Spectral models for estimation of chlorophyll content, growth and yield of wheat crop. Journal of the Indian Society of Remote Sensing,2006,34:1-5.
129. Sogawa K. Studies on feeding habits of the brown planthopper. I. Effects of nitrogen-deficiency of host plant on insect feeding. Journal of Applied Entomology and Zoology,1970,14:101-106.
130. Sogawa K, Feeding behavior and damage mechanisms of the rice planthoppers. In:Elings, A., Rubia, E. (Eds.), Analysis of Damage Mechanisms by Pests and Diseases and Their Effects on Rice Yield. SARP Research Proceedings. Department of Theoretical Production Ecology, DLO Research Institute for Agrobiology and Soil Fertility, Wageningen, IRRI, Manila, Philippines,1994,143-154.
131. Sogawa K. Effects of feeding of the brown planthopper on the components in the leaf blade of rice plants. Japanese Journal of Applied Entomology and Zoology,1971,15:175-179.
132. Sogawa K. The rice brown planthopper:feeding physiology and host plant interactions. Annual Review of Entomology,1982,27:49-73.
133. Suarez L, Zarco-Tejada P J, Sepulcre-Canto G, Perez-Priego O, Miller J R, Jimenez-Munoz J C, Sobrino J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sensing of Environment,2008,112:560-575.
134. Suarez L, Zarco-Tejada P J, Berni J A J, Gonzalez-Dugo V, Fereres E. Modelling PRI for water stress, detection using radiative transfer models. Remote Sensing of Environment,2009,113: 730-744.
135. Sudbrink D L, Harris F A, Robbins J T, English P J, Willers J L. Evaluation of remote sensing to identify variability in cotton plant growth and correlation with larval densities of beet armyworm and cabbage looper(Lepidoptera:Noctuidae). Florida Entomologist,2003,86:290-294.
136. Tian Y C, Yao X, Yang J, Cao W X, Hannaway D B, Zhu Y. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research,2011,120:299-310.
137. Vogelmann J E, Rock B N, Moss D M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing,1993,14:1563-1575.
138. Wahid D A, Ishiguro E, Shimotashiro T, Hirayama S, Ueda K. Study on relationships among LAI, DW, fPAR and spectral reflectance in paddy rice. Journal of Agricultural Meteorology,2003,59: 13-21.
139. Wang F M, Huang J F, Wang Y, Liu Z Y, Zhang F Y. Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines. Precision Agriculture,2013,14: 172-183.
140. Wang S H, Zhu Y, Jiang H D, Cao W X. Positional differences in nitrogen and sugar concentrations of upper leaves relate to plant N status in rice under different N rates. Field Crops Research,2006, 96:224-234.
141.Watanabe T and Kitagawa H. Photosynthesis and translocation of assimilates in rice plants following phloem feeding by the planthopper Nilaparvata lugens (Homoptera:Delphacidae). Journal of Economic Entomology,2000,93:1192-1198.
142. Welter S C. Arthropod impact on plant gas exchange. Insect-plant interactions,1989,1:135-150.
143. Wu C Y, Niu Z, Tang Q, Huang W J, Rivard B, Feng J L. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology,2009,149:1015-1021.
144. Wu C Y, Niu Z, Tang Q, Huang W J. Estimating chlorophyll content from hyperspectral vegetation indices:Modeling and validation. Agricultural and Forest Meteorology,2008,148:1230-1241.
145. Xu H R, Ying Y B, Fu X P, Zhu S P. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering,2007,96:447-454.
146. Yamagata Y, Wiegand C, Akiyama T, Shibayama M. Water turbidity and perpendicular vegetation indices for paddy rice flood damage analyses. Remote Sensing of Environment,1988,26:241-251.
147. Yang C H. A high-resolution airborne four-camera imaging system for agricultural remote sensing. Computers and Electronics in Agriculture,2012,88:13-24.
148. Yang C H and Everitt J H. Remote sensing for detecting and mapping whitefly(Bemisia tabaci) infestations, The Whitefly, Bemisia tabaci (Homoptera:Aleyrodidae) Interaction with Geminivirus-infected Host Plants. W M O Thompson, Springer Netherlands,2011,357-381.
149. Yang C M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precision Agriculture,2010,11:61-81.
150. Yang C M and Cheng C H. Spectral characteristics of rice plants infested by brown planthoppers. Proc. Natl. Sci. Counc. ROC(B),2001,25:180-186.
151. Yang C M, Cheng C H, Chen R K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science,2007,47:329-335.
152. Yang C M and Cheng R K. Modeling rice growth using hyperspectral reflectance data. Crop Science,2004,44:1283-1290.
153. Yang F, Li J L, Gan X Y, Qian Y R, Wu X L, Yang Q. Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Computers and Electronics in Agriculture,2010,70:52-59.
154. Yang Z, Rao M N, Elliott N C, Kindler S D, Popham T W. Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture,2009,67:64-70.
155. Zangerl A R, Hamilton J G, Miller T J, Crofts A R, Oxborough K, Berenbaum M R, De Lucia E H. Impact of folivory on photosynthesis is greater than the sum of its holes. Proceedings of the National Academy of Sciences,2002,99:1088-1091.
156. Zarco-Tejada P J, Miller J R, Mohammed G H, Noland T L, Sampson P H. Estimation of chlorophyll fluorescence under natural illumination from hyperspectral data. International Journal of Applied Earth Observation and Geoinformation,2001,3:321-327.
157. Zhang Y Q, Chen J M, Miller J R, Noland T L. Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment,2008,112:3234-3247.
158. Zhao J L, Zhang D Y, Luo J H, Wang D C, Huang W J. Identifying leaf-scale wheat aphids using the near-ground hyperspectral pushbroom imaging spectrometer, Springer Berlin Heidelberg, Computer and Computing Technologies in Agriculture V,5th IFIP TC 5/SIG 5.1 Conference, CCTA, Beijing, China,2012,275-282.
159. Zhou Q F and Wang J H. Comparison of upper leaf and lower leaf of rice plants in response to supplemental nitrogen levels. Journal of Plant Nutrition,2003,26:607-617.
160.安琼,杨邦杰,焦险峰.作物遥感识别中的数据挖掘技术.农业工程学报,2007,23：181-186.
161.包刚,覃志豪,周义,包玉海,辛晓平,红雨,海全胜.基于高光谱数据和RBF神经网络方法的草地叶面积指数反演.国土资源遥感,2012,73：7-11.
162.崔应峰,王世平.葡萄病害智能诊断网络模型的比较.上海交通大学学报(农业科学版),2011,29：79-86.
163.陈鹏程,张建华,李眉眉,雷勇辉.土耳其斯坦叶螨为害棉叶的生理变化及光谱特征分析.昆虫知识,2007,44：61-65.
164.陈兵.棉花黄萎病高光谱特征及其光谱诊断模型研究.石河子大学,2007.
165.程家安,祝增荣.2005年长江流域稻区褐飞虱暴发成灾原因分析.植物保护,2006,32：1-4.
166.程乾,巫坤.受褐飞虱破坏的水稻光谱特性研究.安徽农业科学,2011,39：20553-20554.
167.程遐年,陈若篪,习学,杨联民,朱子龙,吴进才,钱仁贵,杨金生.稻褐飞虱迁飞规律的研究.昆虫学报,1979,22：1-21.
168.何国金,胡德永,金小华,杨建国,李国扬.北京麦蚜虫害的光谱测量与分析.遥感技术与应用,2002,17：199-205.
169.黄春燕,王登伟,陈冠文,袁杰,祁亚琴,陈燕,程诚.基于高光谱植被指数的棉花干物质积累估算模型研究.棉花学报,2006,18：115-119.
170.黄敬峰,王渊,王福民,刘占宇.油菜红边特征及其叶面积指数的高光谱估算模型.农业工程学报,2006,22：22-26.
171.黄文江,王纪华,刘良云,王锦地,谭昌伟,李存军,王之杰,宋晓宇.基于多时相和多角度光谱信息的作物株型遥感识别初探.农业工程学报,2005,21：82-86.
172.龚敏庆,孙明伟,金明仲.PLS分析与RBF神经网络耦合环境模型.数理统计与管理,2011,5：840-845.
173.季荣,张霞,谢宝瑜,李哲,刘团结,刘闯,李典谟.用MODIS遥感数据检测东亚飞蝗灾害-以河北省南大港为例.昆虫学报,2003,46：713-719.
174.李波,刘占宇,黄敬峰,张莉丽,周湾,石晶晶.基于PCA和PNN的水稻病虫害高光谱识别.农业工程学报,2009,25：143-147.
175.李世欣,温建,邵孝侯,王晓亚,王玉英.偏最小二乘法与人工神经网络耦合的小流域产沙模型.河海大学学报(自然科学版),2010,38：149-153.
176.李云梅,王人潮,王秀珍,沈掌泉.水稻冠层二向反射率的动态变化.浙江大学学报(农业与生命科学版),2001,27：37-42.
177.李云梅,王人潮,王秀珍.水稻冠层二向反射率的模拟及其反演.中国水稻科学,2002,16：291-294.
178.梁雪,吉海彦,王鹏新,饶震红,申兵辉.用MSC-ANN方法建立冬小麦叶片叶绿素与反射光谱的定量分析模型研究.光谱学与光谱分析,2010,30：188-191.
179.林萍,陈永明,何勇.基于可见近红外光谱的糖类别快速鉴别研究.光谱学与光谱分析,2009,29：382-385.
180.刘国顺,李向阳,刘大双,喻奇伟.利用冠层光谱估测烟草叶面积指数和地上生物量.生态学报,2007,27：1763-1771.
181.刘伟东,项月琴,郑兰芬,童庆禧,吴长山.高光谱数据与水稻叶面积指数及叶绿素密度的相关分析.遥感学报,2000,4：279-283.
182.刘占宇,黄敬峰,陶荣祥,张红志.基于主成分分析和径向基网络的水稻胡麻斑病严重度估测.光谱学与光谱分析,2008,28：2156-2160.
183.刘占宇.水稻主要病虫害胁迫遥感监测研究.浙江大学,2008.
184.卢辉,韩建国,张录达.高光谱遥感模型对亚洲小车蝗危害程度研究.光谱学与光谱分析,2009,29：745-748.
185.卢艳丽,王纪华,李少昆,谢瑞芝,高世菊,马达灵.冬小麦不同群体冠层结构的高光谱响应研究.中国农业科学,2005,38：911-915.
186.孔薇,杨杰.基于径向基神经网络的聚丙烯熔融指数预报.化工学报,2003,54：1160-1163.
187.马建文,韩秀珍,哈斯巴干,张跃进,汤金仪,谢志庾.东亚飞蝗灾害的遥感监测实验.国土资源遥感,2003,55：51-55.
188.浦瑞良,宫鹏.高光谱遥感及其应用.北京：高等教育出版社,2000.
189.千怀遂.农作物遥感估产最佳时相的选择研究-以中国主要粮食作物为例.生态学报,1998,18：50-57.
190.乔红波,蒋金炜,程登发,陈胜利,刘建安,马继盛.烟蚜为害特征的高光谱比较.昆虫知识,2007,44：57-61.
191.乔红波,陈登发,孙京瑞,田喆,陈林,林芙蓉.麦蚜对小麦冠层光谱特性的影响研究.植物保护,2005,31：21-26.
192.邱白晶,陈国平,程麒文.水稻白背飞虱虫害的冠层光谱特性与虫量反演.农业机械学报,2008,39：92-95.
193.石晶晶,刘占宇,张莉丽,周湾,黄敬峰.基于支持向量机(SVM)的稻纵卷叶螟危害水稻高光谱遥感识别.中国水稻科学,2009,23：331-337.
194.孙红,李民赞,周志艳,刘刚,罗锡文.基于光谱技术的水稻稻纵卷叶螟受害区域检测.光谱学与光谱分析,2010,30：1080-1083.
195.孙启花.褐飞虱和稻纵卷叶螟为害后水稻的光谱特征.南京农业大学,2010.
196.孙启花,刘向东.褐飞虱危害在水稻植株光谱反射率上的表现.中国水稻科学,2010,24：203-209.
197.孙启花,刘向东.稻纵卷叶螟危害孕穗期水稻的光谱诊断.中国农业科学,2012,45：5040-5048.
198.谭琨,杜培军.基于径向基函数神经网络的高光谱遥感图像分类.光谱学与光谱分析,2008,28：2009-2013.
199.汤旭光,宋开山,刘殿伟,王宗明,张柏,杜嘉,曾丽红,姜广甲,王远东.基于可见/近红外反射光谱的大豆叶绿素含量估算方法比较.光谱学与光谱分析,2011,31：371-374.
200.唐延林,王秀珍,王福民,王人潮.农作物LA I和生物量的高光谱法测定.西北农林科技大学学报(自然科学版),2004,32：100-104.
201.王大成,王纪华,靳宁,王芊,李存军,黄敬峰,王渊,黄芳.用神经网络和高光谱植被指数估算小麦生物量.农业工程学报,2008,24：197-201
202.王芳,卓莉,黎夏,夏丽华.基于高光谱特征选择和RBFNN的城市植被胁迫程度监测.地理科学,2008,28：77-82.
203.王惠文.偏最小二乘回归方法及其应用,北京：国防工业出版社,1999.
204.王新忠,李大鹏.基于主成分回归法的稻飞虱百穴虫量冠层光谱检测.安徽农业科学,2010,38：12695-12696.
205.王秀珍,黄敬峰,李云梅,王人潮.水稻生物化学参数与高光谱遥感特征参数的相关分析.农业工程学报,2003a,19：144-148.
206.王秀珍,黄敬峰,李云梅,王人潮.水稻地上鲜生物量的高光谱遥感估算模型研究.作物学报,2003b,29：815-821.
207.王秀珍,黄敬峰,李云梅,王人潮.水稻叶面积指数的高光谱遥感估算模型.遥感学报,2004,8：81-88.
208.王渊.不同水平油菜氮素含量遥感信息提取方法研究.浙江大学,2008.
209.翁欣欣,陆峰,王传现,亓云鹏.近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析.光谱学与光谱分析,2009,29：3283-3287.
210.吴达科,马承伟,杜尚丰.斑潜蝇虫害叶片受害程度对其近红外反射光谱的影响.农业工程学报,2007,23：156-159.
211.吴昊,邱白晶,陈国平,刘继承.水稻白背飞虱虫害的单叶高光谱特征分析.农机化研究,2009,4：117-119.
212.吴继友,倪健.松毛虫危害的光谱特征与虫害早期探测模式.环境遥感,1995,10：250-251.
213.吴彤,倪绍祥,李云梅,周欣欣,陈建.基于高光谱数据的东亚飞蝗危害程度监测.遥感学报,2007,11：103-108.
214.肖艳芳,宫辉力,周德民.基于因子分析的苜蓿叶片叶绿素高光谱反演研究.生态学报,2012,32：3098-3106.
215.谢传奇,冯雷,冯斌,李晓丽,刘飞,何勇.茄子灰霉病叶片过氧化氢酶活性与高光谱图像特征关联方法.农业工程学报,2012,28：177-184.
216.邢东兴,常庆瑞.基于光谱反射率的果树病虫害级别定量化测评-以红富士苹果树黄叶病害、红蜘蛛虫害为例.西北农林科技大学学报(自然科学版),2009,37：143-155.
217.熊勤学,吴涛.2006年荆州市稻飞虱危害的遥感分析及评估.遥感应用,2008,5：41-44.
218.许章华,刘健,余坤勇,龚从宏,谢婉君,唐梦雅,赖日文,李增禄.松毛虫危害马尾松光谱特征分析与等级检测.光谱学与光谱分析,2013,33：428-433.
219.杨建国,金晓华,郭永旺,石克强,严文胜.遥感技术麦蚜监测应用研究.中国农学通报,2001,17：4-7.
220.杨晓华,黄敬峰,王秀珍,王福民.基于支持向量机的水稻叶面积指数高光谱估算模型研究.光谱学与光谱分析,2008,28：1837-1841.
221.姚付启,张振华,杨润亚,张燕,王海江,崔素芳.基于高光谱的法国梧桐和毛白杨叶片叶绿素含量反演模型研究.鲁东大学学报(自然科学版),2008,24：353-357.
222.张晓阳,李劲峰.利用垂直植被指数推算作物叶面积系数的理论模式.遥感技术与应用,1995,10：13-18.
223.赵德华,李建龙.高光谱技术提取不同作物叶片类胡萝卜素信息.遥感信息,2004,3：13-17.
224.朱登胜,邵咏妮,潘家志,何勇.应用多光谱数字图像识别苗期作物与杂草.浙江大学学报(农业与生命科学版),2008,34：418-422.
225.赵英时.遥感应用分析原理与方法.北京：科学出版社,2002.