麦蚜和白粉病遥感监测技术研究
|
摘要
农作物病虫害是影响粮食生产安全和产量的重要因素之一，防治病虫害大量使用的杀虫和杀菌剂可导致生态环境恶化，影响食品安全。病虫害频繁发生除与作物品种，气候变暖等因素有关外，病虫害监测及预警技术手段滞后仍是病虫害大面积发生的主要原因。遥感实时、动态、无损的技术特点在病虫害监测预警中具有重要作用。本文在实地调查和田间高光谱测量、对麦蚜和白粉病发生特点及不同源遥感机理深入理解的基础上，研究遥感反演病虫害发生环境要素可行性，探讨遥感影像光谱与实测病虫害光谱的关系，利用麦蚜和白粉病地物光谱特性在卫星遥感图像上的反映进行危害信息提取。主要内容如下：
利用ASD手持式高光谱仪测定不同病虫害的光谱反射率，对测得的反射率数据进行一阶微分变换、对数变换、归一化变换等变换，利用逐步判别分析、线性判别分析和分层聚类法对不同病虫害进行识别。结果表明：逐步判别分析法选择的波段主要位于红、绿、蓝、和近红外区；分层聚类法选择的波段除了红、绿、蓝、和近红外波段外，还增加了蓝-绿边缘、绿-红边缘和红边区的波段。所选择的波段比原始波段在病虫害识别时具有更高的精度，最高识别精度达90.6％；边缘区波段对病虫害的识别有重要作用；用对数微分变换处理较其他方法处理对病虫害识别有更好的效果。
利用手持式高光谱仪和基于数字技术的低空遥感系统，对不同严重度小麦白粉病冠层光谱反射率进行了测定，同时调查病情指数，分析不同时期地面平台光谱反射率与病情指数及低空遥感平台反射率与病情指数、归一化植被指数相关性。结果表明，地面光谱测量冠层光谱反射率和低空遥感数字图像反射率与小麦白粉病病情指数在灌浆期有显著的相关关系，就地面测量结果而言，近红外波段的相关性高于绿光波段，相关系数分别为-0.79和-0.54；低空遥感数字图像红、绿、蓝三波段中，相关性依次降低，相关系数分别为-0.79、-0.75和-0.62；而且低空遥感图像与归一化植被指数也存在较好的相关关系，蓝、红、绿波段，相关系数依次为0.70、0.68和0.54。
使用分裂窗算法对NOAA图像进行地表温度反演，将地面实测获得的日最高温度与反演地面温度一一对应进行比较，结果表明遥感反演地温与地面实测的线性关系相关系数最高为0.89，最低为0.51，说明遥感反演地温数据与地面实测地温有显著的相关关系，但反演的值普遍高。对时间序列NDVI分析表明，冬小麦生育期序列曲线具有明显的规律。根据调查地面蚜虫数量和实地光谱测量，建立蚜虫危害与NDVI相关方程：NDVI=-3×1.0~(-3)X+0.623，R=0.918(P<0.01)，达到极显著水平，X为百株蚜量。根据此方程对NOAA-NDVI图像进行分析，对蚜虫危害进行监测。并利用NOAA图像对其它植被要素和冬小麦分布进行研究。
根据分裂窗算法对MODIS遥感图像进行地表温度和各种植被要素反演，并与NOAA反演结果对比分析，同时对植被要素和冬小麦面积分布进行监测。从反演的系列变化可以看出，河南省地表温度变化在260-320K之间。MODIS反演地表温度与实测温度相关性分析结果表明，MODIS反演结果更接近于实测值。并对反演结果进行精度分析，结果NOAA和MODIS卫星反演温度的
平均误差分别为0.90℃和0.41℃。MODIS-NDVI和NOAA-NDVI直方图分析结果表明，MODIS-NDVI的最大值和平均值均比NOAA-NDVI的大，而且，NOAA-NDVI的动态范围小，因此MODIS-NDVI对植被的响应比NOAA-NDVI更敏感。
根据麦蚜和白粉病的发生机理与光谱特征，分别采用TM-NDVI和MPH技术提取危害信息。根据调查点GPS信息在TM-NDVI图像上分别定位健康和受害区两个调查点，信息提取结果表明受害前小麦田块的NDVI相近，而受害后小麦田NDVI值明显降低。对TM图像的DN值进行统计分析，发现健康小麦在第4波段近红外谱段会出现一个反射峰，到第5波段下降，而受病虫害为害的小麦在近红外谱段的光谱值降低，反射峰值出现在第5波段。根据主成分变换的特征向量矩阵，利用第3主分量来提取病虫害危害的特征信息。掩膜图像经主成分变换后的第3主分量在4月30日灰度值接近，而到5月16日健康小麦田的灰度值为比受害小麦田灰度值明显降低。主成分变换的第3分量比较集中地表现病虫为害的特征信息。
本论文在对麦蚜和白粉病发生机理和为害特征深入研究基础上，通过实测地面高光谱数据和病虫害发生实地调查，利用不同源的遥感数据对麦蚜和白粉病的危害特征和遥感信息提取技术进行研究。该研究对于运用遥感等高新技术快速、及时获取大面积病虫害发生动态信息，建立病虫害的预测及监测体系，提高病虫灾害防治能力具有重要意义。
Plant diseases and insect pests is one of the important factors influencing the agricultural product safety and yield. Excessive application of pesticide and fungicide to control the plant diseases and insect pests led to the deterioration of the ecological and food problems. In addition to the crop varieties and the global warming, one of the main factors for the recent increase in frequency and severity of plant diseases and insect pests is still lacking of adequate surveys to detect incipient damage and plan appropriate control operations. The remote sensing technology has great advantage of plant diseases and insect pests forecasting due to the characteristic of real time, dynamic and non-touch. On the basis of field survey and spectrum measurement, understanding to remote sensing mechanism and plant diseases and insect pests feature spectrum theory thoroughly, this paper focused on the feasibility of retrieval of environment elements, the relationship between the feature spectrum in the field and the remote sensing digital image, and using the plant diseases and insect pests feature spectrum to extract the damage information from the satellite remote sensing digital image. The main contents are as follows:
The canopy reflectance of different plant diseases and insect pests was measured by using ASD hand-held spectroradiometers. The reflectance data was transformed by the method of first differential coefficient, logarithm and normalization, then stepwise discriminate analysis and hierarchical clustering were used to identify the different plant diseases and insect pests. The results suggested that bands selected from stepwise discrimination analysis mainly lied along the blue, green, red and near-infrared bands. In addition to the blue, green, red and near-infrared bands, the spectral bands along the blue-green edge, green-red edge and red curves were selected by the hierarchical clustering. Plant diseases and insect pests could be identified more accurate by using selected bands than the original data, the highest recognition accuracy of up to 90.6%, and the bands lying along the edges had important information for discrimination of plant diseases and pests. The spectral data, dealt with the transformation of logarithm and differential coefficient, could achieve better accuracy than others.
Using ASD Hand-held Spectroradiometers and low altitude remote sensing system measured the canopy reflectance of wheat damaged by wheat powdery mildew and, at the same time, scored the disease index (DI). The correlation between reflectance and DI and normalized difference vegetable index (NDVI) and DI was calculated. The results showed that the correlation between reflectance and DI was significant during the stage of milk-filling. The results of in-field measurement showed that the correlation between near infrared reflectance and DI was higher than that of green band with the correlation coefficient of -0.79 and -0.54, respectively. The reflectance correlation between red, green and blue bands decreased in the order with the correlation coefficient of -0.79, -0.75 and -0.62, respectively. The reflectance of low altitude remote sensing was well correlated with NDVI moreover. The correlation coefficients were 0.70, 0.68 and 0.54 for blue, red and green band, respectively.
The land surface temperature (LST) was retrieved from NOAA image by the method of split window algorithm, and it was found that there were correlation coefficients maximal to 0.93 (P<0.01) between LSTs from remote sensing and those from ground observation, but the LST generally higher than observations. Analysis of the time sequence NOAA-NDVI image suggested that winter wheat NDVI had the obvious regulation. On the basis of in field investigation and hyperspectral reflectance data established the correlation equation: NDVI = -3×10~(-3)x + 0.623, R =0.918 (P<0.01). The aphid damage level from NOAA-NDVI image was retrieved based on above equation, at the same time; the other vegetation elements and winter wheat distribution were retrieved by the NOAA-NDVI image.
The LST was retrieved from MODIS 1 B image by the method of split window algorithm and other vegetation elements also retrieved, moreover, the retrieval results were compared to NOAA. The retrieval LSTs sequence results showed the LST ranged from 260k to 320k. The correlation analysis suggested its retrieval result from MODIS was more accurate than that from NOAA, and more close to the ground observation. The error was 0.41°C and 0.90°C, respectively. Analysis of the NOAA-NDVI and MODIS-NDVI histogram showed that their shapes were the same, and the range of their values was wider than that of NOAA, so MODIS-NDVI had more sensitivity to vegetation.
Based on the occurrence mechanism and feature spectrum of wheat aphid and wheat powdery mildew, the methods of NDVI and Masking, Principal component transformation and Hue adjust (MPH) technique were applied in extraction of the damage information from TM multi-spectral data. The healthy and damaged field samples were located in terms of the GPS information, the extraction information results showed that the NDVI value almost the same before injury, but the damaged sample NDVI value obviously decrease after injury. The statistics of DN value from TM image suggested that the healthy sample winter wheat had a peak at TM4 and decrease at TM5, but the damaged sample was contrary to healthy. From the PCA eigenvector matrix, the healthy and damaged samples in PC 3 of masked imageries before injury were almost the same, but after injury obviously increased. The results indicated that the PC 3 of masked imagery could reveal wheat aphid and wheat powdery mildew feature information apparently.
This paper on the basis of understanding to wheat aphid and powdery mildew occurrence mechanism and their feature spectrum thoroughly and in field investigation, using of multi-source remote sensing image retrieved the LST, vegetation and other elements and extraction the damage information. It is prospective for using remote sensing technology archive large scale and dynamic plant diseases and insect pests occurrence information in real time and quickly, at the same time, it is also important to establish the forecasting system and increase the ability of plant diseases and insect pests control.
利用ASD手持式高光谱仪测定不同病虫害的光谱反射率，对测得的反射率数据进行一阶微分变换、对数变换、归一化变换等变换，利用逐步判别分析、线性判别分析和分层聚类法对不同病虫害进行识别。结果表明：逐步判别分析法选择的波段主要位于红、绿、蓝、和近红外区；分层聚类法选择的波段除了红、绿、蓝、和近红外波段外，还增加了蓝-绿边缘、绿-红边缘和红边区的波段。所选择的波段比原始波段在病虫害识别时具有更高的精度，最高识别精度达90.6％；边缘区波段对病虫害的识别有重要作用；用对数微分变换处理较其他方法处理对病虫害识别有更好的效果。
利用手持式高光谱仪和基于数字技术的低空遥感系统，对不同严重度小麦白粉病冠层光谱反射率进行了测定，同时调查病情指数，分析不同时期地面平台光谱反射率与病情指数及低空遥感平台反射率与病情指数、归一化植被指数相关性。结果表明，地面光谱测量冠层光谱反射率和低空遥感数字图像反射率与小麦白粉病病情指数在灌浆期有显著的相关关系，就地面测量结果而言，近红外波段的相关性高于绿光波段，相关系数分别为-0.79和-0.54；低空遥感数字图像红、绿、蓝三波段中，相关性依次降低，相关系数分别为-0.79、-0.75和-0.62；而且低空遥感图像与归一化植被指数也存在较好的相关关系，蓝、红、绿波段，相关系数依次为0.70、0.68和0.54。
使用分裂窗算法对NOAA图像进行地表温度反演，将地面实测获得的日最高温度与反演地面温度一一对应进行比较，结果表明遥感反演地温与地面实测的线性关系相关系数最高为0.89，最低为0.51，说明遥感反演地温数据与地面实测地温有显著的相关关系，但反演的值普遍高。对时间序列NDVI分析表明，冬小麦生育期序列曲线具有明显的规律。根据调查地面蚜虫数量和实地光谱测量，建立蚜虫危害与NDVI相关方程：NDVI=-3×1.0~(-3)X+0.623，R=0.918(P<0.01)，达到极显著水平，X为百株蚜量。根据此方程对NOAA-NDVI图像进行分析，对蚜虫危害进行监测。并利用NOAA图像对其它植被要素和冬小麦分布进行研究。
根据分裂窗算法对MODIS遥感图像进行地表温度和各种植被要素反演，并与NOAA反演结果对比分析，同时对植被要素和冬小麦面积分布进行监测。从反演的系列变化可以看出，河南省地表温度变化在260-320K之间。MODIS反演地表温度与实测温度相关性分析结果表明，MODIS反演结果更接近于实测值。并对反演结果进行精度分析，结果NOAA和MODIS卫星反演温度的
平均误差分别为0.90℃和0.41℃。MODIS-NDVI和NOAA-NDVI直方图分析结果表明，MODIS-NDVI的最大值和平均值均比NOAA-NDVI的大，而且，NOAA-NDVI的动态范围小，因此MODIS-NDVI对植被的响应比NOAA-NDVI更敏感。
根据麦蚜和白粉病的发生机理与光谱特征，分别采用TM-NDVI和MPH技术提取危害信息。根据调查点GPS信息在TM-NDVI图像上分别定位健康和受害区两个调查点，信息提取结果表明受害前小麦田块的NDVI相近，而受害后小麦田NDVI值明显降低。对TM图像的DN值进行统计分析，发现健康小麦在第4波段近红外谱段会出现一个反射峰，到第5波段下降，而受病虫害为害的小麦在近红外谱段的光谱值降低，反射峰值出现在第5波段。根据主成分变换的特征向量矩阵，利用第3主分量来提取病虫害危害的特征信息。掩膜图像经主成分变换后的第3主分量在4月30日灰度值接近，而到5月16日健康小麦田的灰度值为比受害小麦田灰度值明显降低。主成分变换的第3分量比较集中地表现病虫为害的特征信息。
本论文在对麦蚜和白粉病发生机理和为害特征深入研究基础上，通过实测地面高光谱数据和病虫害发生实地调查，利用不同源的遥感数据对麦蚜和白粉病的危害特征和遥感信息提取技术进行研究。该研究对于运用遥感等高新技术快速、及时获取大面积病虫害发生动态信息，建立病虫害的预测及监测体系，提高病虫灾害防治能力具有重要意义。
Plant diseases and insect pests is one of the important factors influencing the agricultural product safety and yield. Excessive application of pesticide and fungicide to control the plant diseases and insect pests led to the deterioration of the ecological and food problems. In addition to the crop varieties and the global warming, one of the main factors for the recent increase in frequency and severity of plant diseases and insect pests is still lacking of adequate surveys to detect incipient damage and plan appropriate control operations. The remote sensing technology has great advantage of plant diseases and insect pests forecasting due to the characteristic of real time, dynamic and non-touch. On the basis of field survey and spectrum measurement, understanding to remote sensing mechanism and plant diseases and insect pests feature spectrum theory thoroughly, this paper focused on the feasibility of retrieval of environment elements, the relationship between the feature spectrum in the field and the remote sensing digital image, and using the plant diseases and insect pests feature spectrum to extract the damage information from the satellite remote sensing digital image. The main contents are as follows:
The canopy reflectance of different plant diseases and insect pests was measured by using ASD hand-held spectroradiometers. The reflectance data was transformed by the method of first differential coefficient, logarithm and normalization, then stepwise discriminate analysis and hierarchical clustering were used to identify the different plant diseases and insect pests. The results suggested that bands selected from stepwise discrimination analysis mainly lied along the blue, green, red and near-infrared bands. In addition to the blue, green, red and near-infrared bands, the spectral bands along the blue-green edge, green-red edge and red curves were selected by the hierarchical clustering. Plant diseases and insect pests could be identified more accurate by using selected bands than the original data, the highest recognition accuracy of up to 90.6%, and the bands lying along the edges had important information for discrimination of plant diseases and pests. The spectral data, dealt with the transformation of logarithm and differential coefficient, could achieve better accuracy than others.
Using ASD Hand-held Spectroradiometers and low altitude remote sensing system measured the canopy reflectance of wheat damaged by wheat powdery mildew and, at the same time, scored the disease index (DI). The correlation between reflectance and DI and normalized difference vegetable index (NDVI) and DI was calculated. The results showed that the correlation between reflectance and DI was significant during the stage of milk-filling. The results of in-field measurement showed that the correlation between near infrared reflectance and DI was higher than that of green band with the correlation coefficient of -0.79 and -0.54, respectively. The reflectance correlation between red, green and blue bands decreased in the order with the correlation coefficient of -0.79, -0.75 and -0.62, respectively. The reflectance of low altitude remote sensing was well correlated with NDVI moreover. The correlation coefficients were 0.70, 0.68 and 0.54 for blue, red and green band, respectively.
The land surface temperature (LST) was retrieved from NOAA image by the method of split window algorithm, and it was found that there were correlation coefficients maximal to 0.93 (P<0.01) between LSTs from remote sensing and those from ground observation, but the LST generally higher than observations. Analysis of the time sequence NOAA-NDVI image suggested that winter wheat NDVI had the obvious regulation. On the basis of in field investigation and hyperspectral reflectance data established the correlation equation: NDVI = -3×10~(-3)x + 0.623, R =0.918 (P<0.01). The aphid damage level from NOAA-NDVI image was retrieved based on above equation, at the same time; the other vegetation elements and winter wheat distribution were retrieved by the NOAA-NDVI image.
The LST was retrieved from MODIS 1 B image by the method of split window algorithm and other vegetation elements also retrieved, moreover, the retrieval results were compared to NOAA. The retrieval LSTs sequence results showed the LST ranged from 260k to 320k. The correlation analysis suggested its retrieval result from MODIS was more accurate than that from NOAA, and more close to the ground observation. The error was 0.41°C and 0.90°C, respectively. Analysis of the NOAA-NDVI and MODIS-NDVI histogram showed that their shapes were the same, and the range of their values was wider than that of NOAA, so MODIS-NDVI had more sensitivity to vegetation.
Based on the occurrence mechanism and feature spectrum of wheat aphid and wheat powdery mildew, the methods of NDVI and Masking, Principal component transformation and Hue adjust (MPH) technique were applied in extraction of the damage information from TM multi-spectral data. The healthy and damaged field samples were located in terms of the GPS information, the extraction information results showed that the NDVI value almost the same before injury, but the damaged sample NDVI value obviously decrease after injury. The statistics of DN value from TM image suggested that the healthy sample winter wheat had a peak at TM4 and decrease at TM5, but the damaged sample was contrary to healthy. From the PCA eigenvector matrix, the healthy and damaged samples in PC 3 of masked imageries before injury were almost the same, but after injury obviously increased. The results indicated that the PC 3 of masked imagery could reveal wheat aphid and wheat powdery mildew feature information apparently.
This paper on the basis of understanding to wheat aphid and powdery mildew occurrence mechanism and their feature spectrum thoroughly and in field investigation, using of multi-source remote sensing image retrieved the LST, vegetation and other elements and extraction the damage information. It is prospective for using remote sensing technology archive large scale and dynamic plant diseases and insect pests occurrence information in real time and quickly, at the same time, it is also important to establish the forecasting system and increase the ability of plant diseases and insect pests control.
引文
1．阿布都瓦斯提·吾拉木和秦其明．基于6S模型的可见光、近红外遥感数据的大气校正．北京大学学报(自然版)，2004，40(4)：611～617．
2．白由路，金继运，杨俐苹，张宁和王磊．低空遥感技术及其在精准农业中的应用．土壤肥料，2004，(1)：3～6．
3．陈华丽，陈刚和郭金柱．LandsatTM在矿区生态环境动态监测中的应用．遥感信息，2004，1：31～34．
4．陈巧和陈永富．单时相NOAA／AVHRR资料监测大范围土壤含水量的研究．林业科学研究，2004，(4)：427～433．
5．陈述彭，童庆禧和郭东华．遥感信息机理研究．北京：科学出版社，1998．
6．陈述彭．遥感大辞典．北京：科学出版社，1990．
7．程乘旗，马廷和王立明．用于植被冠层分析的高光谱波段的组合方法研究．地理学与国土研究，2002，18(2)：23～25．
8．程宇，陈良富，柳钦火，张颢和顾行发．基于MOLDS数据对不同植被覆盖下土壤水分监测的可行性研究．遥感学报，2006，10(5)：783～788
9．戴昌达．植物病虫害的遥感探测．自然灾害学报，1992，1(2)：16～19．
10．董瑶海．FY-1C／D极轨气象卫星及其在轨运行．上海航天，2004，(4)：1～11．
11．杜刚，付必涛和凌峰．基于MODIS的洞庭湖芦苇湿地范围研究．计算机与数字工程，2005，33(9)：32～34．
12．冯先伟，陈曦，包安明，孙莉，王登伟和马亚琴．水分胁迫条件下棉花生理变化及其高光谱响应分析．干旱区地理，2004，27(2)：250～255．
13．甘甫平，王润生和马蔼乃．基于特征谱带的高光谱遥感矿物谱系识别．地学前缘，2003，10(2)：445～454．
14．高懋芳，覃志豪和刘三超．MODIS数据在林火监测中的应用研究．国土资源遥感，2005，2：60～63．
15．高瑞云和范天锡．利用NOAA卫星资料反演最高地表温度．南京气象学院学报，1996，19(1)：106～110．
16．宫鹏，史培军，浦瑞良和郭华东．对地观测技术与地球系统科学．北京：科学出版社，1996．
17．郭广猛和杨青生．利用MODIS数据反演地表温度的研究．遥感技术与应用，2004，19(1)：34～36．
18．郭广猛和赵冰茹．使用MODIS数据监测土壤湿度．土壤，2004，36(2)：219～221．
19．郭宏庆和李秀芬．NOAA气象卫星资料在土地覆盖分类中的应用．黑龙江气象，2001，(2)：22～24．
20．郭景峰．浅谈鄂尔多斯市鄂托克旗遥感TM影像在荒漠化及沙化土地监测中的运用．内蒙古林业调查设计，2006，29(6)：34～35，68．
21．韩素芹和刘淑梅．EOS／MODIS卫星资料在监测冬小麦面积中的应用．天津农学院学报，2004， 2：26～28．
22．韩秀珍，马建文，布和敖斯尔，马超飞和秦树辉．利用卫星ETM与样方统计数据研究西鄂尔多斯珍稀濒危植物种群分布规律．遥感学报，2002，6(2)：136～141
23．何国金，胡德永，金小华，杨建国和李国扬．北京麦蚜虫害的光谱测量与分析．遥感技术与应用，2002，17(3)：119～124．
24．洪楠和候军．SAS for Windows(V8)统计分析系统教程新编．北京：清华大学出版社，北京交通大学出版社，2004．
25．侯英雨，孙林，何延波和王石立．利用EOS～MODIS数据提取作物冠层温度研究．农业工程学报，2006，22(12)：8～12
26．黄春燕，王登伟，陈冠文，袁杰，祁亚琴，陈燕和程诚．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(2)：115～119
27．黄敬峰，王人潮和杨忠恩．水稻遥感估产中AVHRR通道1和通道2的大气影响校正．农业工程学报，2000，15(5)：130～134
28．黄敬峰和许红卫．NOAA／AVHRR数据的几何精纠正方法研究．浙江大学学报：农业与生命科学版，2000，(1)：7～21．
29．黄木易，王纪华，黄义德，黄文江，赵春江和刘良云．高光谱遥感监测冬小麦条锈病的研究进展(综述)．安徽农业大学学报，2004，31(1)：119～122．
30．黄木易，王纪华和黄文江．冬小麦条锈病的光谱特征及遥感监测．农业工程学报，2003，19(6)：154～158．
31．黄晓军，何维，张云柏，张娅香，夏友桦和李秉柏．利用TM卫星资料进行江苏部分地区小麦面积调查．江苏农业科学，2003，4：85～87．
32．季荣，张霞，谢宝瑜，李哲，刘团结，刘闯和李典谟．用MODIS遥感数据监测东亚飞蝗灾害～以河北省南大港为例．昆虫学报，2003，46(6)：713～719．
33．季荣．东亚飞蝗灾害的遥感监测及早期预警研究[博士学位论文]北京：中国科学院．2004．
34．贾建华，刘良云，竞霞和张超．基于多时相MODIS监测冬小麦的种植面积．遥感信息，2005，6：49～51．
35．贾金明，王运行，王树文，孙贵姣，徐巧真和刘九玲．用NOAA／AVHRR资料动态监测小区域冬小麦长势．气象，2005，31(10)：79～82．
36．姜立鹏，覃志豪和谢雯．MODIS数据地表温度反演分裂窗算法的IDL实现测绘与空间地理信息，2006，29(3)：114～117．
37．姜小光和王成．成像光谱数据的光谱信息特点及最佳波段选择：以北京顺义区为例．干旱区地理，2000，23(3)：214～220．
38．蒋建军，倪绍祥和韦玉春．遥感GIS辅助下的环青海湖地区草地蝗虫生境分类研究．遥感学报，2002，6(5)：387～392．
39．金震宇，田庆久，惠凤鸣和陆建飞．水稻叶绿素浓度与光谱反射率关系研究．遥感技术与应用，2003，18(3)：134～137
40．瞿林．TM卫星影像在楚雄市森林资源调查中的应用．林业建设，2003，4：25～29．
41．李石华，王金亮，陈姚，朱妙园和廖迎芸．多光谱遥感数据最佳波段选择方法试验研究．云南 地理环境研究，2005，17(6)：29～33．
42．李虎和徐希慧．利用NOA．A／AVHRR资料监测干旱环境变化的研究．干旱区资源与环境，1996，(2)：81～88．
43．李晓兵．NOAA／AVHRR数据在土地覆盖变化研究中的应用．地学前缘，1997，4(1)：16～16．
44．李星敏，杨文峰，张树誉和高蓓．基于MODIS数据的陕西地表温度监测．陕西气象，2007，1：12～15．
45．李星敏，郑有飞和刘安麟．我国用NOAA／AVHRR资料进行干旱遥感监测的方法综述．中国农业气象，2003，24(3)：38～41．
46．李珍存，张峰，罗进选和何有华．ETM＋SPOT 5融合卫星影像在矿区水土流失调查中的应用．中国水土保持，2006，12：50～51．
47．李行和张连蓬．高光谱遥感图像最佳波段选择的快速算法研究．测绘通报，2004，9：10～12．
48．廖顺宝，马琳，岳燕珍和李泽辉．NOAA／AVHRR资料反演地温与地面实测值的对比分析．国土资源遥感，2004，(1)：19～22．
49．刘爱霞，王长耀，刘正军和牛铮．基于NOAA时间序列数据分析的中国西部荒漠化监测．武汉大学学报：信息科学版，2004，29(101：924～927．
50．刘海岩，牛振国和陈晓玲EOS～MODIS数据在我国农作物监测中的应用．遥感技术与应用，2005，20(5)：531～536．
51．刘建平和赵英时．高光谱遥感数据解译的最佳波段选择方法研究．中国科学院研究生院学报，1999，16(2)：153～161．
52．刘建平和赵英时．高光谱遥感数据最佳波段选择方法试验研究．遥感技术与应用，2001，16(1)：7～13．
53．刘良明和鄢俊洁．MODIS数据在火灾监测中的应用．武汉大学学报：信息科学版，2004，29(1)：55～57．
54．刘良云，黄木易，黄文江，王纪华和赵春江．利用多时相的高光谱航空图像监测冬小麦条锈病．遥感学报，2004，8(3)：276～282．
55．刘亮，姜小光，李显彬和唐伶俐．利用高光谱遥感数据进行农作物分类方法研究．中国科学院研究生院学报，2006，23(4)：484～488．
56．刘献伦，周欣佩，路洪春和杨志军．ETM＋卫星影像数字放大在林业调查中的应用．华东森林经理，2005，19(3)：74～75．
57．刘秀英，林辉，熊建利，熊育久，孙华和莫登奎．森林树种高光谱波段的选择．遥感信息，2005，4：41～44．
58．楼琇林，黄韦艮，周长宝和杨劲松．遥感图像数据重采样的一种快速算法．遥感学报，2002，6(2)：96～101．
59．罗亨树．青海柴达木盆地地物波谱特征和遥感最佳波段选择．国土资源遥感，1990，1：40～46．
60．马建文，韩秀珍，哈斯巴干，张跃进，汤金仪和谢志庚 东亚飞蝗灾害的遥感监测实验．国土资源遥感，2003，(1)：51～55．
61．马建文，韩秀珍，哈斯巴干，王志刚，燕守勋和戴芹．基于东亚飞蝗生育周期的遥感蝗灾监测新模式．遥感学报，2004，8(4)：371～377
62．马建文，赵忠明和布和敖斯尔．遥感数据模型与处理方法．北京：中国科学技术出版社，2001．
63．马建文和韩秀珍．东亚飞蝗灾害遥感监测机理与方法北京：科学出版社．2004．
64．毛克彪，覃志豪，施建成和宫鹏．针对MODIS影像的劈窗算法研究．武汉大学学报：信息科学版，2005，30(8)：703～707．
65．穆晓萱．TM图像在森林资源分类研究中的应用．甘肃科技，2003，19(10)：54～55．
66．倪绍祥．环青海湖地区草地蝗虫遥感监测与预测．上海科学技术出版社，2002．
67．潘宏涛．TM卫星片在森林资源二类调查中的应用．黑龙江林业，2001，6：22～22．
68．浦吉存，董谢琼和尤临．NOAA／AVHRR资料在低纬高原小春作物估产中的初步应用．中国农业气象，2004，(1)：54～56．
69．浦瑞良和宫鹏．森林生物化学与CASI图像数据估计横跨美国俄勒冈州针叶林叶面积指数．遥感学报，1997，1(2)：115～123．
70．祁亚琴，王登伟，陈冠文，黄春燕，段震宇，陈燕，袁杰和杨坤．基于高光谱数据提取作物冠层特征信息的研究进展．棉花学报，2005，17(6)：371～375
71．乔红波，程登发，孙京瑞，田喆，陈林和林芙蓉．麦蚜对小麦冠层光谱特性的影响研究．植物保护，2005，31(2)：21～26
72．秦晓敏，覃志豪和毛克彪．基于MODIS数据的陕西省地表温度的空间分布研究．干旱区地理，2005，28(4)：548～553．
73．任建强，陈仲新和唐华俊．基于MODIS-NDVI的区域冬小麦遥感估产——以山东省济宁市为例．应用生态学报，2006，17(12)：2371～2375．
74．佘万明和叶彩华．MODIS资料遥感监测土壤水分与干旱研究进展．河南气象，2006，1：44～46．
75．沈其君．SAS统计分析．南京：东南大学出版社，2001，
76．沈其益．棉花病害基础研究与防治．北京：科学出版社，1992
77．盛宝钦和段霞瑜．对记载小麦成株白粉病“0～9”级法的改进．北京农业科学，1991，9(1)：38～39．
78．石瑞香，刘闯，李典漠和谢宝瑜．MODI-NDVI在白洋淀蝗虫灾害监测中的应用．自然灾害学报，2003，12(3)：155～160．
79．石瑞香，刘闯，李典谟和谢宝瑜．蝗虫生境监测方法研究进展．生态学报，2003，23(11)：2475～2483
80．石宇虹，张菁，朴瀛，崔劲松和隋东．NOAA／AVHRR遥感信息在火灾监测中的应用．辽宁气象，1995，(1)：32～35．
81．石宇虹和李国春．应用NOAA／AVHRR资料监测水稻长势的研究．应用气象学报，1999，(2)：243～248．
82．舒立福，王明玉，田晓瑞，闫厚和杜建华．NOAA／AVHRR和EOS／MODIS林火监测比较．森林防火，2005，4：27～29．
83．孙家柄，舒宁和关泽群．遥感原理、方法与应用．北京：测绘出版社，2000．
84．覃志豪，Zhang M和Karnieli A．用陆地卫星TM6数据演算地表温度的单窗算法．地理学报，2001，56(4)：456～466．
85．覃志豪，Zhang M．H．，和A．Karnieli．用NOAA／AVHRR热通道数据演算地表温度的劈窗算法． 国土资源遥感，2001，(2)：33～42．
86．覃志豪，高懋芳，秦晓敏，李文娟和徐斌．农业旱灾监测中的地表温度遥感反演方法～以MODIS数据为例．自然灾害学报，2005，14(4)：64～71．
87．田庆久，董卫东，郑兰芬和童庆禧．基于地面定标的地物光谱反演方法研究．遥感技术与应用，1997，12(2)：1～7．
88．田庆久，董卫东和张良培．成像光谱遥感数据的光谱重建反演．测绘学报，1997，25(1)：52～57．
89．田庆久和闵祥军．植被指数研究进展．地球科学进展，1998，13(4)：327～333．
90．童庆禧．中国典型地物波谱及其特征分析．北京：科学出版社，1990．
91．王长耀和林文鹏．基于MODIS-EVI的冬小麦产量遥感预测研究．农业工程学报，2005，21(10)：90～94．
92．王茂新和裴志远．用NOAA图像监测冬小麦面积的方法研究．农业工程学报，1998，14(3)：84～88．
93．王庆光和潘燕芳．多光谱遥感数据最佳波段选择的研究．韶关学院学报，2006，27(3)：42～44．
94．王秀珍，黄敬峰，李云梅和王人潮．水稻生物化学参数与高光谱遥感特性参数的相关分析．农业工程学报，2003，19(2)：144～148．
95．吴炳方．NOAA／AVHRR 1B数据的几何精纠正．环境遥感，1994，9(4)：288～295．
96．吴继友和倪建．松毛虫为害的光谱特征与虫害早期探测模式．遥感学报，1995，10(4)：250～258．
97．吴健平和杨星卫．用NOAA／AVHRR数据估算上海地区水稻种植面积．应用气象学报，1996，7(2)：190～194．
98．吴平．TM卫星影像图在剑河县森林资源二类调查中的应用．林业调查规划，2002，27(4)：60～64．
99．吴曙雯，王人潮，陈晓斌，沈掌泉和史舟．稻叶瘟对水稻光谱特性的影响研究．上海交通大学学报(农业科学版)，2001，(3)：73～76．
100．吴文斌和杨桂霞用NOAA／AVHRR图像监测冬小麦长势的方法研究．中国农业资源与区划，2001，(2)：58～62．
101．武红敢和石进．松毛虫灾害的TM影像监测技术．遥感学报，2004，8(2)：172～177．
102．肖乾广和陈维英．用NOAA／AVHRR资料监测土壤湿度．遥感信息，1990，(1)：22～25．
103．肖胜．SPOT和Landsat ETM＋卫星影像结合下的平潭岛防护林资源遥感研究．福州大学学报：自然科学版，2006，34(5)：669～673．
104．燕守勋，马建文和蔺启忠．中国西部喀喇昆仓明铁盖多金属矿化区的卫星遥感勘查．遥感学报．2001，5(4)：306～311
105．杨青生和刘闯．MODIS数据陆面温度反演研究．遥感技术与应用，2004，19(2)：90～94．
106．叶明，杨晓平和蒋刚毅．基于TM遥感图像的宁波土地利用动态监测．宁波大学学报：理工版，2003，16(1)：30～34．
107．尹楠，周云轩，王黎明，万力．NOAA／AVHRR的分裂窗算法在地表温度反演中的应用．测绘与空间地理信息，2005，(4)：8～11，32．
108．张爱民和马晓群．利用NOAA卫星资料进行冬小麦苗情分类统计的研究．生态农业研究，1996，4(3)：30～34．
109．张长春，王晓燕和邵景力．利用NOAA数据估算黄河三角洲区域蒸散量．资源科学，2005，27(1)：86～91．
110．张良培，郑兰芬和童庆禧．利用高光谱对生物变量进行估计．遥感学报，1997，1(2)：111～114．
111．张文宗，姚树然，赵春雷和王云秀．利用MODIS资料监测和预警干旱新方法．气象科技，2006，34(4)：501～504．
112．张霞，刘良云，赵春江和张兵．利用高光谱遥感图像估算小麦氮含量．遥感学报，2003，7(3)：176～181，T002．
113．张霞，张兵，卫征，陈正超和郑兰芬．MODIS光谱指数监测小麦长势变化研究．中国图像图形学报：A辑，2005，10(4)：420～424
114．张旭，常顺利，师庆东和潘晓玲．彩色合成法在新疆地表植被覆盖变化中的应用——以多时相NOAA／AVHRR影像为例．新疆环境保护，2003，25(4)：5～9．
115．张学勇和陈忠林．应用NOAA／AVHRR资料进行草地遥感估产模型的研究．辽宁大学学报：自然科学版，2006，33(2)：127～129．
116．张有为和张振中．TM卫片在森林资源调查中的应用．林业勘查设计，2001，2：84～84
117．张玉书，班显秀，陈鹏狮，冯锐，纪瑞鹏和肖艳．应用NOAA／AVHRR资料监测松毛虫危害研究初探．应用生态学报，2005，(5)：870～874．
118．张志宏．TM卫星像片在生态公益林区森林资源现状调查中的应用．华东森林经理，2001，15(2)：60～61．
119．赵立成．FY～1C／D极轨气象卫星的运行和应用．上海航天，2004，(4)：12～15．
120．赵英时．遥感应用分析原理与方法．北京：科学出版社，2004．
121．赵有松和李京伟．ETM＋影像用于土地利用自动分类的实验研究．遥感信息，2001，2：38～42．
122．郑新江，陈渭民，鞠洪波，方翔，罗敬宁和董旭辉．利用NOAA卫星资料估算陆地沙尘量的方法研究．高科技与产业化，2006，9：110～112．
123．周咏梅．NOAA／AVHRR资料在青海省牧区草场旱情监测中的应用．应用气象学报，1998，4：496～500．
124．朱启疆和刘金英．NOAA气象卫星的AVHRR数字图像在植被调查中的应用．北京师范大学学报：自然科学版，1989，2：73～77．
125. Aase J.K. and Siddoway F.H. Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sensing of Environment, 1981, 11: 267～277.
126. Adams M.L.and Philpot W.D.. Yellowness index: an application of spectral spring derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing, 1999, 20: 3663～3675.
127. Adamsen F.J., Paul J.P., Barnes E.M., LaMorte R.L., Wall G W., Leavitt S. W. and Kimball B.A.. Measuring wheat senescence with a digital camera. Crop Science. 1999,(39): 719～724.
128. Almeida-Filho R.and Shimabukuro Y. E.. Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners, Roraima State, Brazilian Amazon. Remote Sensing of Environment, 2002, 79(1): 42～50.
129. Andersen S.H.. Land surface temperature estimation based on NOAA/AVHRR data during the HAPEX-Sahel experiment. Journal of Hydrology, 1997, 188-189: 788-814.
130. Anding D.and Kauth R. . Estimation of sea surface temperature from space. Remote Sensing of Environment, 1970, 1: 217—220.
131. Apan A., Datt B.and Kelly R. . Detection of Pests and Diseases in Vegetable Crops Using Hyperspectral Sensing: A Comparison of Reflectance Data for Different Sets of Symptoms. Proceedings of SSC 2005 Spatial Intelligence, Innovation and Praxis: The national biennial Conference of the Spatial Sciences Institute, September 2005: 10—18.
132. Ausmus B.S.and Hilty J.W. . Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves. Remote Sensing of Environment, 1972, 2: 77-81.
133. Backer F.Z.L. . Towards a local split window method over land surface. Internationa Journal of Remote Sensing, 1990, 11(3): 369-393.
134. Bawden F. C.. Infra-red photography and plant virus diseases. Nature, 1933, 132: 168.
135. Becker F.and Li Z.L. . Towards a local split window method over land surface. International Journal of Remote Sensing, 1990, 11: 369—393.
136. Blazquez C. H. . Remote sensing of foliar diseases of vegetable crops with infrared color photography. Fluorida.State Hortic.Society, 1972, 85: 123—126.
137. Blazquez C.H. . The role of remote sensing in phytopathology and crop production. Processing of American phytopathology Society, 1976, 3: 245.
138. Brasa-Ramos A.,de Santa Olalla F.M. and Caselles V. . Maximum and Actual Evapotranspiration for Barley (Hordeum vulgare L.) through NOAA Satellite Images in Castilla-La Mancha, Spain. Journal of Agricultural Engineering Research, 1996, 63(4): 283—293.
139. Brasa-Ramos A.,de Santa Olalla F.M.,Caselles V. and Jochum A.M. . Comparison of Evapotranspiration Estimates by NOAA～AVHRR Images and Aircraft Flux Measurements in a Semiarid Region of Spain. Journal of Agricultural Engineering Research, 1998, 70(3): 285—294.
140. Bravo C.,Moshou D.,West J.,McCartney A. and Ramon H. . Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 2003, 84(2): 137—145.
141. Bryceson K.P. . Discrimination of small areas of vegetation growth after localized rain in semi-arid environments using Landsat. Proceedings of the third Australian Remote Sensing Conference, Queens land, 1984.: 541-550.
142. Bryceson K.P. . The use of Landsat MSS data to determine the distribution of locust eggbeds in the Riverina region of New South Wales, Australia. International Journal of Remote Sensing, 1989, 10: 1762-1749.
143. Bryceson K.P and Wright D.E. . An analysis of the 1984 locust plague in Australia using multitemporal Landsat multispectral data and a simulation model of locust development. Agricultural Ecosystem and Environment, 1986, 16: 87—102.
144. Carter GA.,Michael R.S. and Haley T. . Airborne detection of southern pine beetle damage using key spectral bands. Canada Journal of Forest Resource, 1998, 28: 1040—1045.
145. Caselles V.,Coll C. and Valor E. . Land surface temperature determination in the whole HAPEX sahel area from AVHRR data. International Journal of Remote Sensing, 1997,18: 1009-1027.
146. Colwell R.N. . Determing the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 1956,26: 223-286.
147. Curran RJ. . Relative reflectance data from preprocessed multispectral photography. International Journal of Remote Sensing, 1980, 1: 77-83.
148. Dalezios N. R.,Domenikiotis C.,Loukas A.,Tzortzios S. T.and Kalaitzidis C. . Cotton yield estimation based on NOAA/AVHRR produced NDVI. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001, 26(3): 247—251.
149. Daughtry C.S.T.,Ranson K.J. and Biehl L.L.. A new technique to measure the spectral properties of conifer needles. Remote Sensing of Environment, 1989, 33: 55-64.
150. DeWit A. J. W.,Boogaard H. L. and vanDiepen C. A. . Using NOAA/AVHRR estimates of land surface temperature for regional agrometeorogical modeling. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(3): 187-204.
151. Diaz-Lago J. E.,Stuthman D.D. and Leonard K.J. . Evaluation of components of partial resistance to oat crown rust using digital image analysis. Plant Disease, 2003, 87(6): 667-674.
152. Dymond J.R.and Trotter CM. . Directional reflectance of vegetation measured by a calibrated digital camera.. Applied Optics, 1997,(18): 4314—4319.
153. Everitt J.H., Escobar D.E., Appel D.N., Riggs W.G and Davis. M.R. . Using airborne digital imagery for detecting oak wilt disease. Plant disease,1999,83(6):502-505
154. Fletcher R. S., Skaria M. and Escobar D. E. . Field spectra and airborne digital imagery for detecting Phytophthora footrot infections in citrus trees. Hort Science, 2001, 36(1): 94-97.
155. Foody G.M.,Boyd D.S., and Cutler M.E.J. Cutler. . Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 2003, 85(4): 463-474.
156. Franklin S.E and Raske A.G. . Satellite remote sensing of spruce budworm forest defoliation in western Newfoundland. Canada Journal of Remote Sensing, 1994 20(1): 30-48.
157. Frederic A. and Christine E. . Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sensing of Environment, 1995, 54(3): 198-208.
158. Fung T.,Ma F.Y. and Siu W.L. . Band selection using hyperspectral data of subtropical tree species. Geocarto International, 2003, 18(4): 3—11.
159. Gausman H.W.,Menges R.M.,Richardson A.J.,Walter H.,Rodriguez R.R.and Tamez S. . Optical parameters of leaves of 7 weed species. Weed Science, 1981, 29(1): 24-26.
160. Gausman H.W. and Hart W.G . Reflectance of sooty mold fungus on citrus leaves over 2.5 to 40-micrometer wavelength interval. Journal of Economic Entomology, 1974, 67(4): 479-480.
161. Goel P.K., Prasher S.O., Landry J.A., Patel R.M., Bonnell R.B., Viau A.A. and Miller J.R. Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 2003, 38: 99- 124.
162. Goetz A.F.H., Vane G, Solomon J.E. and Rock. B.N. . Imaging Spectrometry for Earth Remote Sensing. Science, 1985,228: 1147-1153.
163. Goward S.N., Huemmrich K.F. and Waring R.H.. Visible-near infrared spectral reflectance of landscape components in western Oregon. Remote Sensing of Environment, 1994, 47: 190—203.
164. Guan, Y. N., S. Guo, Y. Xue, R. Li and Q. K. Yang . Landscape dynamitic change in Mu Us Desert derived from Landsat TM data. Journal of Environ Science (China), 2003, 15(2): 222—226.
165. Gupta R. K., Prasad S., Rao G. H. and Nadham T. S. V . District level wheat yield estimation using NOAA/AVHRR NDVI temporal profile. Advances in Space Research, 1993, 13(5): 253-256.
166. Hair J.F., Anderson R.E., Tatham R.L. and Black W.C. Multivariate data analysis with readings prentice hall. New Jersey: Upper Saddle River, 1995.
167. Hart W G and Meyers. V. I . Infrared aerial color photography for detection of populations of brown soft scale in citrus groves. Journal of Economic Entomology, 1968, 61(3): 617-624.
168. Hashiba H.,K. Kameda S. U. and Tanaka. S. . Landuse change analysis of Tama river basin with different spatial resolution sensor data by Landsat/MSS and TM. Advances in Space Research, 2000,26(7): 1069-1073.
169. Heikkila J., Nevalainen S. and Tokola T. . Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. Forest Ecology and Management, 2002, 158:9-23.
170. Hielkema J .U. . AGPPLCCP77P11FAO. Rome, Italy, 1977.
171. Hielkema J .U. . FAOPUSAID Final Report. FAO, Rome, Italy. 1980.
172. Holden H. and LeDrew L. E. . Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal components analysis, and derivative spectroscopy. Remote Sensing of Environment, 1998, 65: 217-224.
173. http://www.nfiieos.cn/.
174. http://www.noaa.gov/.
175. Huete A.R. . A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295-309.
176. Hurtado E., Vidal A. and Caselles V. . Comparison of two atmospheric correction methods for Landsat TM thermal band. International Journal of Remote Sensing, 1996, 17: 237—247.
177. Izumi H., Mitomi Y, Nakayama Y. and Takeuchi S. . Land covers analysis using multi seasonal NOAA AVHRR mosaicked images for hydrological applications. Advances in Space Research, 1998, 22(5): 677-680.
178. Jackson H.R.and Wallen V.R. . The epiphytology of late blight of potato monitored by sequential color infrared aerial photography. Journal of Applying Photograph Engineer, 1976, 2: 207—212.
179. Ji R., Xie B.Y., Li D.M., Lia Z. and Zhang X. . Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems and Environment, 2004, 104: 615～620
180. Jia L.L., Chen X.P., Zhang F.S., Buerkert A. and Romheld V. . Use of Digital Camera to Assess Nitrogen Status of Winter Wheat in the Northern China Plain. Journal of Plant Nutrition, 2004, 27(3): 441-450.
181. Jordan C.F.. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50: 663-666
182. Kauth R.J.and Thoams GS. . The Tasseled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat. Proceeding, Symposium On March Processing Of Remotely Sensed Data. West Lafayette, In : Laboratory for Application of Remote Sensing, 1976:41-51.
183. Kerdiles H., Grondona M., Rodriguez R. and Seguin B. . Forest mapping using NOAA/AVHRR data in the Pampean region, Argentina. Agricultural and Forest Meteorology, 1996, 79(3): 157— 182.
184. Kieckhefer R.W., Gellner J.L. and Riedell W.E. . Evaluation of aphid-day standard as a predictor of yield loss caused by cereal aphids. Agronomy Journal, 1995, 87: 785—788.
185. Kieckhefer R.W.and Kantack B.H. . Losses in yield in spring wheat in South Dakota caused by cereal aphids. Journal of Economic Entomology, 1980, 73: 582—585.
186. Kieckhefer R.W.and Kantack B.H. . Yield losses in winter grains caused by cereal aphids in South Dakota. Journal of Economic Entomology, 1988, 81: 317—321.
187. Knipling E.B. . Physical and physiological basis for the reflectance of visible and near～infrared radiation from vegetation. Remote Sensing of Environment, 1971, 1: 155—159.
188. Kobayashi T, Kanda E., Kitada K., Ishiguro K. and Torigoe Y.. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 2001, 91(3): 316-323.
189. Kondoh A.and Oyamada Y. . Monitoring surface moisture and vegetation status by NOAA and GMS over North China Plain. Advances in Space Research, 2000, 26(7): 1055—1058.
190. Krause GH.and Weis E.. Chlorophyll fluorescence and photosynthesis: The basics. Annual Review of Plant Physiology, 1991, 42: 313-349.
191. Krause GH and Weis E. . Chlorophyll fluorescence as a tool in plant physiology. Interpretation of fluorescence signals Photosynthesis, 1984,5: 139—157.
192. Kressler F.P. and Steinnocher K.T. . Detecting land cover changes from NOAA/AVHRR data by using spectral mixture analysis. International Journal of Applied Earth Observation and Geoinformation, 1999, 1(1): 21—26.
193. Labrecque S., Fournier R. A., Luther J. E. and Piercey D. . A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, 2006, 226: 129—144.
194. Lamb D.W., Weedon M.M. and Rew. L.J. . Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Research, 1999, 39(6): 481-492.
195. Lass L.W.and Callihan R.H. . The effect of phenological stage on detection of Yellow Hawkweed (Hieracium pratense) and Oxeye Daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technology, 1997,11(2): 248—256.
196. Lathrop L.D.and Pennypacker S. . Spectral classification of tomato disease severity levels. Photogram Engineer Remote Sensing, 1980,46:1433—1438.
197. Liang S.L., Fang H.L.and Chen. M. Z. . Atmospheric Correction of Landsat ETM+ Land Surface Imagery-Part I:Methods IEEE Transactions on Geosciences and Remote Sensing, 2001, 39(11): 2490-2498.
198. Liu J., Liu M., Tian H., Zhuang D., Zhang Z., Zhang W., Tang X. and Deng X. . Spatial and temporal patterns of China's cropland during 1990—2000: An analysis based on Landsat TM data. Remote Sensing of Environment, 2005, 98(4): 442—456.
199. Lorenzen B.and Jensen A. . Changes in leaf spectral properties induced in bareley by cereal powdery mildew. Remote Sensing of Environment, 1989, 27: 201—209.
200. Lu D.. Integration of vegetation inventory data and Landsat TM image for vegetation classification in the western Brazilian Amazon. Forest Ecology and Management, 2005, 213: 369—383.
201. Lu D., Mausel P., Brondizio E. and Moran E. . Relationships between forests stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 2004, 198: 149-167
202. Lukina E., Stone M. and Raun W. . Estimating vegetation coverage in wheat using digital images. Plant Nutrition, 1999, 22(2): 341—350.
203. Luther J. E., Fournier R.A. Fournier D.E. Piercey L. Guindon and R. J. Hall. . Biomass mapping using forest type and structure derived from Landsat TM imagery. International Journal of Applied Earth Observation and Geoinformation, 2006, 8(3): 173—187.
204. Makela H.and Pekkarinen A. . Estimation of forest stand volumes by Landsat TM imagery and stand～level field～inventory data. Forest Ecology and Management, 2004, 196: 245—255.
205. Makela H.and Pekkarinen A. . Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery. Remote Sensing of Environment, 2001, 77(1): 66-75.
206. Maselli F., Romanelli S., Bottai L. and Zipoli G . Use of NOAA～AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 2003, 86(2): 187-197.
207. Malthus T.J.and Maderia A.C. . High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 1993,45: 107—116.
208. McCulloch C.a. H. D.M. . Identification and monitoring of Australian plague locust habitats from Landsat. Remote Sensing of Environment, 1983,(13): 95—102.
209. Medlin C.R., Shaw D.R.,Gerard P.D.and LaMastus F.E. . Using remote sensing to detect weed infestation in Glycine max. Weed Science, 2000,48(3): 393—398.
210. Meyer M.P. and Calpouzos L.. Detection of crop diseases resources. Photogrammetric Engineering & Remote Sensing, 1968,47: 2—9.
211. Michael F., Randall W. and Ronnie H. . Quantitative approaches for using colorinfrared photography for assessing In-season nitrogen status in winter wheat. Agronomy Journal, 2003, 95: 1189-1199.
212. Miller J.R. . Season patterns in leaf reflectance red edge characteristics. International Journal of Remote Sensing, 1991,12(7): 1509-1523.
213. Mkhabela M.S. and Mashinini N.N. . Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA-AVHRR. Agricultural and Forest Meteorology, 2005,129: 1—9.
214. Moran M. S.,Bryant R.,Thome K.,Ni W.,Nouvellon Y.,Gonzalez-Dugo M. P.,Qi J. and Clarke T. R. . A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+. Remote Sensing of Environment, 2001, 78(1—2): 71—82.
215. Moshoua D., Bravo C, West J., Wahlen S., McCartney A.and Ramona H. . Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 2004,44: 173-188.
216. Moya E.A., Barralesa L. R., Apablaza GE. . Assessment of the disease severity of squash powdery mildew through visual analysis, digital image analysis and validation of these methodologies. Crop Protection, 2005, 24: 785-789
217. Muhammed H H and Larsolle A. . Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosystems Engineering, 2003, 86(2): 125-134
218. Muhammed H.H. . Hyperspectral Crop Reflectance Data for characterizing and estimating Fungal Disease Severity in Wheat. Biosystems Engineering, 2005, 91(1): 9-20.
219. Mukai Y., Sugimura T., Watanabe H., and Wakamori K. . Extraction of areas infested by pine bark beetle using Lansat MSS data. Photogrammetric Engineering and Remote Sensing, 1987 54 77 — 81.
220. Neblette C. B. . Airplane photography for plant disease surveys. Photo-Era magazine, 1928, 59: 175.
221. Neblette C.B.. Aerial photography for the study of plant diseases. Photo～Era magazine, 1927, 58: 346.
222. Nicholas C.C. and Christine S. . A comparison of field～based and modeled reflectance spectra from damaged Pinus radiated foliage. Australian Journal of Botany, 2005,(53): 417—429.
223. Nilsson H.E.. Remote sensing of oil seed rape infected by Sclerotinia stem rot and Verticillium wilt. Vaxtskyddsrapporter-jordbruk, 1985a, 33
224. Nilsson H.E. . Remote sensing of 2-row barley infected by net blotch disease. Vaxtskyddsrapporter-jordbruk, 1985b, 34
225. Nilsson H.E. . Remote sensing of 6-row barley infected by barley stripe disease.. Vaxtskyddsrapporter-jordbruk, 1985c, 36
226. Nilsson H.E. . Hand ～held radiometry and IR-thermograph of plant disease in field plot experiments. International Journal of Remote Sensing, 1991, 12(3): 545-557.
227. Nilsson H.E. . Remote sensing and image analysis in plant pathology. Canada Journal of Plant Pathology, 1995,17:154-66.
228. Oguro, Y., Suga Y., Takeuchi S., Ogawa H. and Tsuchiya K. . Monitoring of a rice field using landsat-5 TM and landsat-7 ETM+ data. Advances in Space Research, 2003, 32(11): 2223—2228.
229. Palmer A. R.and van Rooyen A.F. . Detecting vegetation change in the southern Kalahari using Landsat TM data. Journal of Arid Environments, 1998, 39(2): 143- 153.
230. Pax-Lenney M. and Woodcock C.E.. . Monitoring agricultural lands in Egypt with multitemporal Landsat TM imagery: How many images are needed? Remote Sensing of Environment, 1997, 59(3): 522-529.
231. Pax-Lenney M., Woodcock C. E., Macomber S.A., Gopal S. and Song C. . Forest mapping with a generalized classifier and Landsat TM data. Remote Sensing of Environment, 2001, 77(3): 241 — 250.
232. Penuelas J., Fiella. I., Biel C., Serrano L. and Save R. . The reflectance at the 950-970 nm regions as an indicator of plant water status. International Journal of Remote Sensing, 1993, 14: 1887— 1905.
233. Penuelas J., Pinol J. and Filella F. . Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997, 18: 2869—2875.
234. Persson P., Hall-Konyves K., Sjostrom G and Pinzke S. . NOAA/AVHRR data for crop productivity estimation in Sweden. Advances in Space Research, 1993, 13(11): 111 — 116.
235. Petkov L., Pieri M., Maselli F. and Maracchi G . Study and modeling of temperature spatial variability by NOAA/AVHRR thermal imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 1996,51(3): 127—136.
236. Polischuk V.P., Shadchina T. M., Kompanetz T.I., Budzanivskaya I.G and Sozinov A.A. Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection, 1997,31(1): 115—119.
237. Prabhakara C, Dalu G and Kunde V.G... Estimation of sea temperature from remote sensing in the 11 to 12 μm window. Journal of Geophysical Research, 1974, 79: 5039—5044.
238. Price J .C. . Band selection procedure for multispectrals canners. Applied Optics, 1994, 33(15): 3281-3288.
239. Price J.C. . Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal of Geophysical Research, 1984, 89(D5): 7231-7237.
240. Qi J., Chehbouni A., Huete A.R. and Kerr Y.H.. A modified soil adjusted vegetation index. Remote Sensing of Environment, 1994,48: 119—126.
241. Qin Z., Dall'Olmo G. And P. Berliner. . Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research, 2001, 106(D19): 22655—22670.
242. Raikes C, and Burpee L. L. . Use of Multispectral Radiometry for Assessment of Rhizoctonia Blight in Creeping Bentgrass. Phytopathology, 1998, 88: 446—449.
243. Ramsey E.W., Hodgson M. E., Sapkota S. K. and Nelson G A. . Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution. Remote Sensing of Environment, 2001,77(3): 279—292.
244. Rees W. G, Williams M. and Vitebsky P. . Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge. Remote Sensing of Environment, 2003, 85(4): 441—452.
245. Reese H. M., Lilies T. M., Nagel D. E., Stewart J. S., Goldmann R. A., Simmons T. E., Chipman J. W.and Tessar P. A.. . Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project. Remote Sensing of Environment, 2002, 82(2): 224—237.
246. Richardson A.J., Menges R. M.and Nixon P.R.. Distinguishing weed from crop plants using video remote sensing. Photogrammetric Engineering & Remote Sensing, 1985,51(11): 1785-1790.
247. Riedell W.E., and Blackmer T M. . Leaf Reflectance Spectra of Cereal Aphid-Damaged Wheat. Crop Science, 1999, 39(6): 1835-1840.
248. Sasaki Y, Okamoto T, Imou K. and Torii T. . Automatic diagnosis of plant disease: spectral reflectance of healthy and diseased leaves. In: Proceedings of Agriculture Engineer 1998 Conference, 1998, Oslo: 6.
249. Scotford I.M.and MillerP.C.H. . Applications of Spectral Reflectance Techniques in Northern European Cereal Production: A Review. Biosystems Engineering, 2005, 90(3): 235—250.
250. Seelan S.K., Laguette S., Casady GM. and Seielstad GA. . Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment, 2003, 88: 157-169.
251. Shanahan J.F., Schepers J.S., Francis D.D., Varvel Gary.E., Wilhelm W.W.,Tringe J.M.,Schlemme M.R. and Major D.J. . Use of Remote Sensing Imagery to Estimate Corn Grain Yield. Journal of Agronomy, 2001,93: 583-589.
252. Sobrino J.A., Li z.l., Stoll M.P. and Becker F. . Improvements in the split-window technique for land surface temperature determination. IEEE Transactions on Geosciences and Remote Sensing,, 1994, 32(2): 243-253.
253. Sobrino, J. A., Jimenez-Munoz J. C. and Pauline L.. Land surface temperature retrieval from Land sat TM 5. Remote Sensing of Environment, 2004, 90(4): 434—440.
254. Song C, Woodcock C. E., Seto K. C, Lenney M. P. and Macomber S. A.. Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment, 2001,75(2): 230—244.
255. Stafford J. V.and Benlloch J. V.. Machine-assisted detection of weeds and weed patches. 1997, In: Precision Agriculture 97, Vol. II: Technology, IT and Management (Stafford J V, Ed) (511—518): First European Conference on Precision Agriculture, Warwick, 7-10 September 1997. SCI Bios Scientific Publishers, Oxford.
256. Steddom K., Bredehoeft M. W., Khan M. and Rush C. M.. Comparison of visual and multispectral radiometric disease evaluations of cercospora leaf spot of sugar beet. Plant Disease, 2005, 89(2): 153-158.
257. Stone C., Chisholm L. A. and McDonald S. . Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight. Canadian Journal of Botany, 2003, 81(6): 560—569.
258. Stow D., Hope A., Richardson D., Chen D., Garrisonc.and Service D. . Potential of color-infrared digital camera imagery for inventory and mapping of alien plant invasions in South African shrublands. International Journal of Remote Sensing, 2000, 21(15): 2965-2970.
259. Taubenhaus J. J., Ezekiel W. N. and Neblette C. B. . Airplane photography in the study of cotton root rot. Phytopathology, 1929,19: 1025—1029.
260. Tokola T. . The Influence of Field Sample Data Location on Growing Stock Volume Estimation in Landsat TM-Based Forest Inventory in Eastern Finland. Remote Sensing of Environment, 2000, 74(3): 422-431.
261. Tommervik H., Hogda K.A. and Solheim I. . Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 2003, 85(3): 370—388.
262. Verstraeten W.W., Veroustraete F. and Feyen J. . Estimating evapotranspiration of European forests from NOAA～imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products. Remote Sensing of Environment, 2005, 96(2): 256-276.
263. Vidal A., Pinglo F., Durand H., Devaux-Ros C. and Maillet A. . Evaluation of a temporal fire risk index in Mediterranean forests from NOAA thermal IR. Remote Sensing of Environment, 1994, 49(3): 296-303.
264. Vogelmann J.E.and Rock B.N. . Use of Thematic Mapper data for the detection of forest damage caused by the pear thrips. Remote Sensing of Environment, 1989, 30: 217—225.
265. Voss F.and Dreiser U. In: KrallS., WilpsH.(eds.) .Eschborn. New Trends in Locust Control. 1994: 23-40.
266. Wan Z M.and Dosier J.A. . A generalized split-window algorithm for retrieving land surface temperature from space. IEEE Transactions on Geosciences and Remote Sensing, 1996, 34(4): 892-905.
267. Wang C., Qi J., Moran S. and Marsett R.. . Soil moisture estimation in a semiarid rangeland using ERS～2 and TM imagery. Remote Sensing of Environment, 2004,90(2): 178—189.
268. Williams D.L. . A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected conkfer species. Remote Sensing of Environment, 1991, 35: 79—93.
269. Wilson E. H.and Sader S.A. . Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 2002, 80(3): 385—396.
270. Xin J.F., Yu Z.R., van Leeuwen L. and Driessen P.M.... Mapping crop key phenological stages in the North China Plain using NOAA time series images. International Journal of Applied Earth Observation and Geoinformation, 2002,4(2): 109— 117.
271. Yang Z., Rao M.N., Elliott N.C., Kindler S.D.and Popham T.W.. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 2005,47: 121～135.
272. Ye X. J., Sakai K., Garciano L.O., Asada S.L. and Sasao A. . Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological Modelling, 2006, 198: 426～432
273. Zha Y, J. Gao S. Ni Y. Liu J. Jiang and Wei Y. . A spectral reflectance-based approach to quantification of grassland covers from Landsat TM imagery. Remote Sensing of Environment, 2003,87:371～375.
274. Zhou M.C., Ishidaira H., Hapuarachchi H.P., Magome J., Kiem A.S. and Takeuchi K. . Estimating potential evapotranspiration using Shuttleworth-Wallace model and NOAA/AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin. Journal of Hydrology, 2006, 327:151～173.
2．白由路，金继运，杨俐苹，张宁和王磊．低空遥感技术及其在精准农业中的应用．土壤肥料，2004，(1)：3～6．
3．陈华丽，陈刚和郭金柱．LandsatTM在矿区生态环境动态监测中的应用．遥感信息，2004，1：31～34．
4．陈巧和陈永富．单时相NOAA／AVHRR资料监测大范围土壤含水量的研究．林业科学研究，2004，(4)：427～433．
5．陈述彭，童庆禧和郭东华．遥感信息机理研究．北京：科学出版社，1998．
6．陈述彭．遥感大辞典．北京：科学出版社，1990．
7．程乘旗，马廷和王立明．用于植被冠层分析的高光谱波段的组合方法研究．地理学与国土研究，2002，18(2)：23～25．
8．程宇，陈良富，柳钦火，张颢和顾行发．基于MOLDS数据对不同植被覆盖下土壤水分监测的可行性研究．遥感学报，2006，10(5)：783～788
9．戴昌达．植物病虫害的遥感探测．自然灾害学报，1992，1(2)：16～19．
10．董瑶海．FY-1C／D极轨气象卫星及其在轨运行．上海航天，2004，(4)：1～11．
11．杜刚，付必涛和凌峰．基于MODIS的洞庭湖芦苇湿地范围研究．计算机与数字工程，2005，33(9)：32～34．
12．冯先伟，陈曦，包安明，孙莉，王登伟和马亚琴．水分胁迫条件下棉花生理变化及其高光谱响应分析．干旱区地理，2004，27(2)：250～255．
13．甘甫平，王润生和马蔼乃．基于特征谱带的高光谱遥感矿物谱系识别．地学前缘，2003，10(2)：445～454．
14．高懋芳，覃志豪和刘三超．MODIS数据在林火监测中的应用研究．国土资源遥感，2005，2：60～63．
15．高瑞云和范天锡．利用NOAA卫星资料反演最高地表温度．南京气象学院学报，1996，19(1)：106～110．
16．宫鹏，史培军，浦瑞良和郭华东．对地观测技术与地球系统科学．北京：科学出版社，1996．
17．郭广猛和杨青生．利用MODIS数据反演地表温度的研究．遥感技术与应用，2004，19(1)：34～36．
18．郭广猛和赵冰茹．使用MODIS数据监测土壤湿度．土壤，2004，36(2)：219～221．
19．郭宏庆和李秀芬．NOAA气象卫星资料在土地覆盖分类中的应用．黑龙江气象，2001，(2)：22～24．
20．郭景峰．浅谈鄂尔多斯市鄂托克旗遥感TM影像在荒漠化及沙化土地监测中的运用．内蒙古林业调查设计，2006，29(6)：34～35，68．
21．韩素芹和刘淑梅．EOS／MODIS卫星资料在监测冬小麦面积中的应用．天津农学院学报，2004， 2：26～28．
22．韩秀珍，马建文，布和敖斯尔，马超飞和秦树辉．利用卫星ETM与样方统计数据研究西鄂尔多斯珍稀濒危植物种群分布规律．遥感学报，2002，6(2)：136～141
23．何国金，胡德永，金小华，杨建国和李国扬．北京麦蚜虫害的光谱测量与分析．遥感技术与应用，2002，17(3)：119～124．
24．洪楠和候军．SAS for Windows(V8)统计分析系统教程新编．北京：清华大学出版社，北京交通大学出版社，2004．
25．侯英雨，孙林，何延波和王石立．利用EOS～MODIS数据提取作物冠层温度研究．农业工程学报，2006，22(12)：8～12
26．黄春燕，王登伟，陈冠文，袁杰，祁亚琴，陈燕和程诚．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(2)：115～119
27．黄敬峰，王人潮和杨忠恩．水稻遥感估产中AVHRR通道1和通道2的大气影响校正．农业工程学报，2000，15(5)：130～134
28．黄敬峰和许红卫．NOAA／AVHRR数据的几何精纠正方法研究．浙江大学学报：农业与生命科学版，2000，(1)：7～21．
29．黄木易，王纪华，黄义德，黄文江，赵春江和刘良云．高光谱遥感监测冬小麦条锈病的研究进展(综述)．安徽农业大学学报，2004，31(1)：119～122．
30．黄木易，王纪华和黄文江．冬小麦条锈病的光谱特征及遥感监测．农业工程学报，2003，19(6)：154～158．
31．黄晓军，何维，张云柏，张娅香，夏友桦和李秉柏．利用TM卫星资料进行江苏部分地区小麦面积调查．江苏农业科学，2003，4：85～87．
32．季荣，张霞，谢宝瑜，李哲，刘团结，刘闯和李典谟．用MODIS遥感数据监测东亚飞蝗灾害～以河北省南大港为例．昆虫学报，2003，46(6)：713～719．
33．季荣．东亚飞蝗灾害的遥感监测及早期预警研究[博士学位论文]北京：中国科学院．2004．
34．贾建华，刘良云，竞霞和张超．基于多时相MODIS监测冬小麦的种植面积．遥感信息，2005，6：49～51．
35．贾金明，王运行，王树文，孙贵姣，徐巧真和刘九玲．用NOAA／AVHRR资料动态监测小区域冬小麦长势．气象，2005，31(10)：79～82．
36．姜立鹏，覃志豪和谢雯．MODIS数据地表温度反演分裂窗算法的IDL实现测绘与空间地理信息，2006，29(3)：114～117．
37．姜小光和王成．成像光谱数据的光谱信息特点及最佳波段选择：以北京顺义区为例．干旱区地理，2000，23(3)：214～220．
38．蒋建军，倪绍祥和韦玉春．遥感GIS辅助下的环青海湖地区草地蝗虫生境分类研究．遥感学报，2002，6(5)：387～392．
39．金震宇，田庆久，惠凤鸣和陆建飞．水稻叶绿素浓度与光谱反射率关系研究．遥感技术与应用，2003，18(3)：134～137
40．瞿林．TM卫星影像在楚雄市森林资源调查中的应用．林业建设，2003，4：25～29．
41．李石华，王金亮，陈姚，朱妙园和廖迎芸．多光谱遥感数据最佳波段选择方法试验研究．云南 地理环境研究，2005，17(6)：29～33．
42．李虎和徐希慧．利用NOA．A／AVHRR资料监测干旱环境变化的研究．干旱区资源与环境，1996，(2)：81～88．
43．李晓兵．NOAA／AVHRR数据在土地覆盖变化研究中的应用．地学前缘，1997，4(1)：16～16．
44．李星敏，杨文峰，张树誉和高蓓．基于MODIS数据的陕西地表温度监测．陕西气象，2007，1：12～15．
45．李星敏，郑有飞和刘安麟．我国用NOAA／AVHRR资料进行干旱遥感监测的方法综述．中国农业气象，2003，24(3)：38～41．
46．李珍存，张峰，罗进选和何有华．ETM＋SPOT 5融合卫星影像在矿区水土流失调查中的应用．中国水土保持，2006，12：50～51．
47．李行和张连蓬．高光谱遥感图像最佳波段选择的快速算法研究．测绘通报，2004，9：10～12．
48．廖顺宝，马琳，岳燕珍和李泽辉．NOAA／AVHRR资料反演地温与地面实测值的对比分析．国土资源遥感，2004，(1)：19～22．
49．刘爱霞，王长耀，刘正军和牛铮．基于NOAA时间序列数据分析的中国西部荒漠化监测．武汉大学学报：信息科学版，2004，29(101：924～927．
50．刘海岩，牛振国和陈晓玲EOS～MODIS数据在我国农作物监测中的应用．遥感技术与应用，2005，20(5)：531～536．
51．刘建平和赵英时．高光谱遥感数据解译的最佳波段选择方法研究．中国科学院研究生院学报，1999，16(2)：153～161．
52．刘建平和赵英时．高光谱遥感数据最佳波段选择方法试验研究．遥感技术与应用，2001，16(1)：7～13．
53．刘良明和鄢俊洁．MODIS数据在火灾监测中的应用．武汉大学学报：信息科学版，2004，29(1)：55～57．
54．刘良云，黄木易，黄文江，王纪华和赵春江．利用多时相的高光谱航空图像监测冬小麦条锈病．遥感学报，2004，8(3)：276～282．
55．刘亮，姜小光，李显彬和唐伶俐．利用高光谱遥感数据进行农作物分类方法研究．中国科学院研究生院学报，2006，23(4)：484～488．
56．刘献伦，周欣佩，路洪春和杨志军．ETM＋卫星影像数字放大在林业调查中的应用．华东森林经理，2005，19(3)：74～75．
57．刘秀英，林辉，熊建利，熊育久，孙华和莫登奎．森林树种高光谱波段的选择．遥感信息，2005，4：41～44．
58．楼琇林，黄韦艮，周长宝和杨劲松．遥感图像数据重采样的一种快速算法．遥感学报，2002，6(2)：96～101．
59．罗亨树．青海柴达木盆地地物波谱特征和遥感最佳波段选择．国土资源遥感，1990，1：40～46．
60．马建文，韩秀珍，哈斯巴干，张跃进，汤金仪和谢志庚 东亚飞蝗灾害的遥感监测实验．国土资源遥感，2003，(1)：51～55．
61．马建文，韩秀珍，哈斯巴干，王志刚，燕守勋和戴芹．基于东亚飞蝗生育周期的遥感蝗灾监测新模式．遥感学报，2004，8(4)：371～377
62．马建文，赵忠明和布和敖斯尔．遥感数据模型与处理方法．北京：中国科学技术出版社，2001．
63．马建文和韩秀珍．东亚飞蝗灾害遥感监测机理与方法北京：科学出版社．2004．
64．毛克彪，覃志豪，施建成和宫鹏．针对MODIS影像的劈窗算法研究．武汉大学学报：信息科学版，2005，30(8)：703～707．
65．穆晓萱．TM图像在森林资源分类研究中的应用．甘肃科技，2003，19(10)：54～55．
66．倪绍祥．环青海湖地区草地蝗虫遥感监测与预测．上海科学技术出版社，2002．
67．潘宏涛．TM卫星片在森林资源二类调查中的应用．黑龙江林业，2001，6：22～22．
68．浦吉存，董谢琼和尤临．NOAA／AVHRR资料在低纬高原小春作物估产中的初步应用．中国农业气象，2004，(1)：54～56．
69．浦瑞良和宫鹏．森林生物化学与CASI图像数据估计横跨美国俄勒冈州针叶林叶面积指数．遥感学报，1997，1(2)：115～123．
70．祁亚琴，王登伟，陈冠文，黄春燕，段震宇，陈燕，袁杰和杨坤．基于高光谱数据提取作物冠层特征信息的研究进展．棉花学报，2005，17(6)：371～375
71．乔红波，程登发，孙京瑞，田喆，陈林和林芙蓉．麦蚜对小麦冠层光谱特性的影响研究．植物保护，2005，31(2)：21～26
72．秦晓敏，覃志豪和毛克彪．基于MODIS数据的陕西省地表温度的空间分布研究．干旱区地理，2005，28(4)：548～553．
73．任建强，陈仲新和唐华俊．基于MODIS-NDVI的区域冬小麦遥感估产——以山东省济宁市为例．应用生态学报，2006，17(12)：2371～2375．
74．佘万明和叶彩华．MODIS资料遥感监测土壤水分与干旱研究进展．河南气象，2006，1：44～46．
75．沈其君．SAS统计分析．南京：东南大学出版社，2001，
76．沈其益．棉花病害基础研究与防治．北京：科学出版社，1992
77．盛宝钦和段霞瑜．对记载小麦成株白粉病“0～9”级法的改进．北京农业科学，1991，9(1)：38～39．
78．石瑞香，刘闯，李典漠和谢宝瑜．MODI-NDVI在白洋淀蝗虫灾害监测中的应用．自然灾害学报，2003，12(3)：155～160．
79．石瑞香，刘闯，李典谟和谢宝瑜．蝗虫生境监测方法研究进展．生态学报，2003，23(11)：2475～2483
80．石宇虹，张菁，朴瀛，崔劲松和隋东．NOAA／AVHRR遥感信息在火灾监测中的应用．辽宁气象，1995，(1)：32～35．
81．石宇虹和李国春．应用NOAA／AVHRR资料监测水稻长势的研究．应用气象学报，1999，(2)：243～248．
82．舒立福，王明玉，田晓瑞，闫厚和杜建华．NOAA／AVHRR和EOS／MODIS林火监测比较．森林防火，2005，4：27～29．
83．孙家柄，舒宁和关泽群．遥感原理、方法与应用．北京：测绘出版社，2000．
84．覃志豪，Zhang M和Karnieli A．用陆地卫星TM6数据演算地表温度的单窗算法．地理学报，2001，56(4)：456～466．
85．覃志豪，Zhang M．H．，和A．Karnieli．用NOAA／AVHRR热通道数据演算地表温度的劈窗算法． 国土资源遥感，2001，(2)：33～42．
86．覃志豪，高懋芳，秦晓敏，李文娟和徐斌．农业旱灾监测中的地表温度遥感反演方法～以MODIS数据为例．自然灾害学报，2005，14(4)：64～71．
87．田庆久，董卫东，郑兰芬和童庆禧．基于地面定标的地物光谱反演方法研究．遥感技术与应用，1997，12(2)：1～7．
88．田庆久，董卫东和张良培．成像光谱遥感数据的光谱重建反演．测绘学报，1997，25(1)：52～57．
89．田庆久和闵祥军．植被指数研究进展．地球科学进展，1998，13(4)：327～333．
90．童庆禧．中国典型地物波谱及其特征分析．北京：科学出版社，1990．
91．王长耀和林文鹏．基于MODIS-EVI的冬小麦产量遥感预测研究．农业工程学报，2005，21(10)：90～94．
92．王茂新和裴志远．用NOAA图像监测冬小麦面积的方法研究．农业工程学报，1998，14(3)：84～88．
93．王庆光和潘燕芳．多光谱遥感数据最佳波段选择的研究．韶关学院学报，2006，27(3)：42～44．
94．王秀珍，黄敬峰，李云梅和王人潮．水稻生物化学参数与高光谱遥感特性参数的相关分析．农业工程学报，2003，19(2)：144～148．
95．吴炳方．NOAA／AVHRR 1B数据的几何精纠正．环境遥感，1994，9(4)：288～295．
96．吴继友和倪建．松毛虫为害的光谱特征与虫害早期探测模式．遥感学报，1995，10(4)：250～258．
97．吴健平和杨星卫．用NOAA／AVHRR数据估算上海地区水稻种植面积．应用气象学报，1996，7(2)：190～194．
98．吴平．TM卫星影像图在剑河县森林资源二类调查中的应用．林业调查规划，2002，27(4)：60～64．
99．吴曙雯，王人潮，陈晓斌，沈掌泉和史舟．稻叶瘟对水稻光谱特性的影响研究．上海交通大学学报(农业科学版)，2001，(3)：73～76．
100．吴文斌和杨桂霞用NOAA／AVHRR图像监测冬小麦长势的方法研究．中国农业资源与区划，2001，(2)：58～62．
101．武红敢和石进．松毛虫灾害的TM影像监测技术．遥感学报，2004，8(2)：172～177．
102．肖乾广和陈维英．用NOAA／AVHRR资料监测土壤湿度．遥感信息，1990，(1)：22～25．
103．肖胜．SPOT和Landsat ETM＋卫星影像结合下的平潭岛防护林资源遥感研究．福州大学学报：自然科学版，2006，34(5)：669～673．
104．燕守勋，马建文和蔺启忠．中国西部喀喇昆仓明铁盖多金属矿化区的卫星遥感勘查．遥感学报．2001，5(4)：306～311
105．杨青生和刘闯．MODIS数据陆面温度反演研究．遥感技术与应用，2004，19(2)：90～94．
106．叶明，杨晓平和蒋刚毅．基于TM遥感图像的宁波土地利用动态监测．宁波大学学报：理工版，2003，16(1)：30～34．
107．尹楠，周云轩，王黎明，万力．NOAA／AVHRR的分裂窗算法在地表温度反演中的应用．测绘与空间地理信息，2005，(4)：8～11，32．
108．张爱民和马晓群．利用NOAA卫星资料进行冬小麦苗情分类统计的研究．生态农业研究，1996，4(3)：30～34．
109．张长春，王晓燕和邵景力．利用NOAA数据估算黄河三角洲区域蒸散量．资源科学，2005，27(1)：86～91．
110．张良培，郑兰芬和童庆禧．利用高光谱对生物变量进行估计．遥感学报，1997，1(2)：111～114．
111．张文宗，姚树然，赵春雷和王云秀．利用MODIS资料监测和预警干旱新方法．气象科技，2006，34(4)：501～504．
112．张霞，刘良云，赵春江和张兵．利用高光谱遥感图像估算小麦氮含量．遥感学报，2003，7(3)：176～181，T002．
113．张霞，张兵，卫征，陈正超和郑兰芬．MODIS光谱指数监测小麦长势变化研究．中国图像图形学报：A辑，2005，10(4)：420～424
114．张旭，常顺利，师庆东和潘晓玲．彩色合成法在新疆地表植被覆盖变化中的应用——以多时相NOAA／AVHRR影像为例．新疆环境保护，2003，25(4)：5～9．
115．张学勇和陈忠林．应用NOAA／AVHRR资料进行草地遥感估产模型的研究．辽宁大学学报：自然科学版，2006，33(2)：127～129．
116．张有为和张振中．TM卫片在森林资源调查中的应用．林业勘查设计，2001，2：84～84
117．张玉书，班显秀，陈鹏狮，冯锐，纪瑞鹏和肖艳．应用NOAA／AVHRR资料监测松毛虫危害研究初探．应用生态学报，2005，(5)：870～874．
118．张志宏．TM卫星像片在生态公益林区森林资源现状调查中的应用．华东森林经理，2001，15(2)：60～61．
119．赵立成．FY～1C／D极轨气象卫星的运行和应用．上海航天，2004，(4)：12～15．
120．赵英时．遥感应用分析原理与方法．北京：科学出版社，2004．
121．赵有松和李京伟．ETM＋影像用于土地利用自动分类的实验研究．遥感信息，2001，2：38～42．
122．郑新江，陈渭民，鞠洪波，方翔，罗敬宁和董旭辉．利用NOAA卫星资料估算陆地沙尘量的方法研究．高科技与产业化，2006，9：110～112．
123．周咏梅．NOAA／AVHRR资料在青海省牧区草场旱情监测中的应用．应用气象学报，1998，4：496～500．
124．朱启疆和刘金英．NOAA气象卫星的AVHRR数字图像在植被调查中的应用．北京师范大学学报：自然科学版，1989，2：73～77．
125. Aase J.K. and Siddoway F.H. Assessing winter wheat dry matter production via spectral reflectance measurements. Remote Sensing of Environment, 1981, 11: 267～277.
126. Adams M.L.and Philpot W.D.. Yellowness index: an application of spectral spring derivatives to estimate chlorosis of leaves in stressed vegetation. International Journal of Remote Sensing, 1999, 20: 3663～3675.
127. Adamsen F.J., Paul J.P., Barnes E.M., LaMorte R.L., Wall G W., Leavitt S. W. and Kimball B.A.. Measuring wheat senescence with a digital camera. Crop Science. 1999,(39): 719～724.
128. Almeida-Filho R.and Shimabukuro Y. E.. Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners, Roraima State, Brazilian Amazon. Remote Sensing of Environment, 2002, 79(1): 42～50.
129. Andersen S.H.. Land surface temperature estimation based on NOAA/AVHRR data during the HAPEX-Sahel experiment. Journal of Hydrology, 1997, 188-189: 788-814.
130. Anding D.and Kauth R. . Estimation of sea surface temperature from space. Remote Sensing of Environment, 1970, 1: 217—220.
131. Apan A., Datt B.and Kelly R. . Detection of Pests and Diseases in Vegetable Crops Using Hyperspectral Sensing: A Comparison of Reflectance Data for Different Sets of Symptoms. Proceedings of SSC 2005 Spatial Intelligence, Innovation and Praxis: The national biennial Conference of the Spatial Sciences Institute, September 2005: 10—18.
132. Ausmus B.S.and Hilty J.W. . Reflectance studies of healthy, maize dwarf mosaic virus-infected, and Helminthosporium maydis-infected corn leaves. Remote Sensing of Environment, 1972, 2: 77-81.
133. Backer F.Z.L. . Towards a local split window method over land surface. Internationa Journal of Remote Sensing, 1990, 11(3): 369-393.
134. Bawden F. C.. Infra-red photography and plant virus diseases. Nature, 1933, 132: 168.
135. Becker F.and Li Z.L. . Towards a local split window method over land surface. International Journal of Remote Sensing, 1990, 11: 369—393.
136. Blazquez C. H. . Remote sensing of foliar diseases of vegetable crops with infrared color photography. Fluorida.State Hortic.Society, 1972, 85: 123—126.
137. Blazquez C.H. . The role of remote sensing in phytopathology and crop production. Processing of American phytopathology Society, 1976, 3: 245.
138. Brasa-Ramos A.,de Santa Olalla F.M. and Caselles V. . Maximum and Actual Evapotranspiration for Barley (Hordeum vulgare L.) through NOAA Satellite Images in Castilla-La Mancha, Spain. Journal of Agricultural Engineering Research, 1996, 63(4): 283—293.
139. Brasa-Ramos A.,de Santa Olalla F.M.,Caselles V. and Jochum A.M. . Comparison of Evapotranspiration Estimates by NOAA～AVHRR Images and Aircraft Flux Measurements in a Semiarid Region of Spain. Journal of Agricultural Engineering Research, 1998, 70(3): 285—294.
140. Bravo C.,Moshou D.,West J.,McCartney A. and Ramon H. . Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 2003, 84(2): 137—145.
141. Bryceson K.P. . Discrimination of small areas of vegetation growth after localized rain in semi-arid environments using Landsat. Proceedings of the third Australian Remote Sensing Conference, Queens land, 1984.: 541-550.
142. Bryceson K.P. . The use of Landsat MSS data to determine the distribution of locust eggbeds in the Riverina region of New South Wales, Australia. International Journal of Remote Sensing, 1989, 10: 1762-1749.
143. Bryceson K.P and Wright D.E. . An analysis of the 1984 locust plague in Australia using multitemporal Landsat multispectral data and a simulation model of locust development. Agricultural Ecosystem and Environment, 1986, 16: 87—102.
144. Carter GA.,Michael R.S. and Haley T. . Airborne detection of southern pine beetle damage using key spectral bands. Canada Journal of Forest Resource, 1998, 28: 1040—1045.
145. Caselles V.,Coll C. and Valor E. . Land surface temperature determination in the whole HAPEX sahel area from AVHRR data. International Journal of Remote Sensing, 1997,18: 1009-1027.
146. Colwell R.N. . Determing the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 1956,26: 223-286.
147. Curran RJ. . Relative reflectance data from preprocessed multispectral photography. International Journal of Remote Sensing, 1980, 1: 77-83.
148. Dalezios N. R.,Domenikiotis C.,Loukas A.,Tzortzios S. T.and Kalaitzidis C. . Cotton yield estimation based on NOAA/AVHRR produced NDVI. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001, 26(3): 247—251.
149. Daughtry C.S.T.,Ranson K.J. and Biehl L.L.. A new technique to measure the spectral properties of conifer needles. Remote Sensing of Environment, 1989, 33: 55-64.
150. DeWit A. J. W.,Boogaard H. L. and vanDiepen C. A. . Using NOAA/AVHRR estimates of land surface temperature for regional agrometeorogical modeling. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(3): 187-204.
151. Diaz-Lago J. E.,Stuthman D.D. and Leonard K.J. . Evaluation of components of partial resistance to oat crown rust using digital image analysis. Plant Disease, 2003, 87(6): 667-674.
152. Dymond J.R.and Trotter CM. . Directional reflectance of vegetation measured by a calibrated digital camera.. Applied Optics, 1997,(18): 4314—4319.
153. Everitt J.H., Escobar D.E., Appel D.N., Riggs W.G and Davis. M.R. . Using airborne digital imagery for detecting oak wilt disease. Plant disease,1999,83(6):502-505
154. Fletcher R. S., Skaria M. and Escobar D. E. . Field spectra and airborne digital imagery for detecting Phytophthora footrot infections in citrus trees. Hort Science, 2001, 36(1): 94-97.
155. Foody G.M.,Boyd D.S., and Cutler M.E.J. Cutler. . Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 2003, 85(4): 463-474.
156. Franklin S.E and Raske A.G. . Satellite remote sensing of spruce budworm forest defoliation in western Newfoundland. Canada Journal of Remote Sensing, 1994 20(1): 30-48.
157. Frederic A. and Christine E. . Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sensing of Environment, 1995, 54(3): 198-208.
158. Fung T.,Ma F.Y. and Siu W.L. . Band selection using hyperspectral data of subtropical tree species. Geocarto International, 2003, 18(4): 3—11.
159. Gausman H.W.,Menges R.M.,Richardson A.J.,Walter H.,Rodriguez R.R.and Tamez S. . Optical parameters of leaves of 7 weed species. Weed Science, 1981, 29(1): 24-26.
160. Gausman H.W. and Hart W.G . Reflectance of sooty mold fungus on citrus leaves over 2.5 to 40-micrometer wavelength interval. Journal of Economic Entomology, 1974, 67(4): 479-480.
161. Goel P.K., Prasher S.O., Landry J.A., Patel R.M., Bonnell R.B., Viau A.A. and Miller J.R. Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 2003, 38: 99- 124.
162. Goetz A.F.H., Vane G, Solomon J.E. and Rock. B.N. . Imaging Spectrometry for Earth Remote Sensing. Science, 1985,228: 1147-1153.
163. Goward S.N., Huemmrich K.F. and Waring R.H.. Visible-near infrared spectral reflectance of landscape components in western Oregon. Remote Sensing of Environment, 1994, 47: 190—203.
164. Guan, Y. N., S. Guo, Y. Xue, R. Li and Q. K. Yang . Landscape dynamitic change in Mu Us Desert derived from Landsat TM data. Journal of Environ Science (China), 2003, 15(2): 222—226.
165. Gupta R. K., Prasad S., Rao G. H. and Nadham T. S. V . District level wheat yield estimation using NOAA/AVHRR NDVI temporal profile. Advances in Space Research, 1993, 13(5): 253-256.
166. Hair J.F., Anderson R.E., Tatham R.L. and Black W.C. Multivariate data analysis with readings prentice hall. New Jersey: Upper Saddle River, 1995.
167. Hart W G and Meyers. V. I . Infrared aerial color photography for detection of populations of brown soft scale in citrus groves. Journal of Economic Entomology, 1968, 61(3): 617-624.
168. Hashiba H.,K. Kameda S. U. and Tanaka. S. . Landuse change analysis of Tama river basin with different spatial resolution sensor data by Landsat/MSS and TM. Advances in Space Research, 2000,26(7): 1069-1073.
169. Heikkila J., Nevalainen S. and Tokola T. . Estimating defoliation in boreal coniferous forests by combining Landsat TM, aerial photographs and field data. Forest Ecology and Management, 2002, 158:9-23.
170. Hielkema J .U. . AGPPLCCP77P11FAO. Rome, Italy, 1977.
171. Hielkema J .U. . FAOPUSAID Final Report. FAO, Rome, Italy. 1980.
172. Holden H. and LeDrew L. E. . Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal components analysis, and derivative spectroscopy. Remote Sensing of Environment, 1998, 65: 217-224.
173. http://www.nfiieos.cn/.
174. http://www.noaa.gov/.
175. Huete A.R. . A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295-309.
176. Hurtado E., Vidal A. and Caselles V. . Comparison of two atmospheric correction methods for Landsat TM thermal band. International Journal of Remote Sensing, 1996, 17: 237—247.
177. Izumi H., Mitomi Y, Nakayama Y. and Takeuchi S. . Land covers analysis using multi seasonal NOAA AVHRR mosaicked images for hydrological applications. Advances in Space Research, 1998, 22(5): 677-680.
178. Jackson H.R.and Wallen V.R. . The epiphytology of late blight of potato monitored by sequential color infrared aerial photography. Journal of Applying Photograph Engineer, 1976, 2: 207—212.
179. Ji R., Xie B.Y., Li D.M., Lia Z. and Zhang X. . Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems and Environment, 2004, 104: 615～620
180. Jia L.L., Chen X.P., Zhang F.S., Buerkert A. and Romheld V. . Use of Digital Camera to Assess Nitrogen Status of Winter Wheat in the Northern China Plain. Journal of Plant Nutrition, 2004, 27(3): 441-450.
181. Jordan C.F.. Derivation of leaf area index from quality of light on the forest floor. Ecology, 1969, 50: 663-666
182. Kauth R.J.and Thoams GS. . The Tasseled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat. Proceeding, Symposium On March Processing Of Remotely Sensed Data. West Lafayette, In : Laboratory for Application of Remote Sensing, 1976:41-51.
183. Kerdiles H., Grondona M., Rodriguez R. and Seguin B. . Forest mapping using NOAA/AVHRR data in the Pampean region, Argentina. Agricultural and Forest Meteorology, 1996, 79(3): 157— 182.
184. Kieckhefer R.W., Gellner J.L. and Riedell W.E. . Evaluation of aphid-day standard as a predictor of yield loss caused by cereal aphids. Agronomy Journal, 1995, 87: 785—788.
185. Kieckhefer R.W.and Kantack B.H. . Losses in yield in spring wheat in South Dakota caused by cereal aphids. Journal of Economic Entomology, 1980, 73: 582—585.
186. Kieckhefer R.W.and Kantack B.H. . Yield losses in winter grains caused by cereal aphids in South Dakota. Journal of Economic Entomology, 1988, 81: 317—321.
187. Knipling E.B. . Physical and physiological basis for the reflectance of visible and near～infrared radiation from vegetation. Remote Sensing of Environment, 1971, 1: 155—159.
188. Kobayashi T, Kanda E., Kitada K., Ishiguro K. and Torigoe Y.. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 2001, 91(3): 316-323.
189. Kondoh A.and Oyamada Y. . Monitoring surface moisture and vegetation status by NOAA and GMS over North China Plain. Advances in Space Research, 2000, 26(7): 1055—1058.
190. Krause GH.and Weis E.. Chlorophyll fluorescence and photosynthesis: The basics. Annual Review of Plant Physiology, 1991, 42: 313-349.
191. Krause GH and Weis E. . Chlorophyll fluorescence as a tool in plant physiology. Interpretation of fluorescence signals Photosynthesis, 1984,5: 139—157.
192. Kressler F.P. and Steinnocher K.T. . Detecting land cover changes from NOAA/AVHRR data by using spectral mixture analysis. International Journal of Applied Earth Observation and Geoinformation, 1999, 1(1): 21—26.
193. Labrecque S., Fournier R. A., Luther J. E. and Piercey D. . A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, 2006, 226: 129—144.
194. Lamb D.W., Weedon M.M. and Rew. L.J. . Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Research, 1999, 39(6): 481-492.
195. Lass L.W.and Callihan R.H. . The effect of phenological stage on detection of Yellow Hawkweed (Hieracium pratense) and Oxeye Daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technology, 1997,11(2): 248—256.
196. Lathrop L.D.and Pennypacker S. . Spectral classification of tomato disease severity levels. Photogram Engineer Remote Sensing, 1980,46:1433—1438.
197. Liang S.L., Fang H.L.and Chen. M. Z. . Atmospheric Correction of Landsat ETM+ Land Surface Imagery-Part I:Methods IEEE Transactions on Geosciences and Remote Sensing, 2001, 39(11): 2490-2498.
198. Liu J., Liu M., Tian H., Zhuang D., Zhang Z., Zhang W., Tang X. and Deng X. . Spatial and temporal patterns of China's cropland during 1990—2000: An analysis based on Landsat TM data. Remote Sensing of Environment, 2005, 98(4): 442—456.
199. Lorenzen B.and Jensen A. . Changes in leaf spectral properties induced in bareley by cereal powdery mildew. Remote Sensing of Environment, 1989, 27: 201—209.
200. Lu D.. Integration of vegetation inventory data and Landsat TM image for vegetation classification in the western Brazilian Amazon. Forest Ecology and Management, 2005, 213: 369—383.
201. Lu D., Mausel P., Brondizio E. and Moran E. . Relationships between forests stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 2004, 198: 149-167
202. Lukina E., Stone M. and Raun W. . Estimating vegetation coverage in wheat using digital images. Plant Nutrition, 1999, 22(2): 341—350.
203. Luther J. E., Fournier R.A. Fournier D.E. Piercey L. Guindon and R. J. Hall. . Biomass mapping using forest type and structure derived from Landsat TM imagery. International Journal of Applied Earth Observation and Geoinformation, 2006, 8(3): 173—187.
204. Makela H.and Pekkarinen A. . Estimation of forest stand volumes by Landsat TM imagery and stand～level field～inventory data. Forest Ecology and Management, 2004, 196: 245—255.
205. Makela H.and Pekkarinen A. . Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery. Remote Sensing of Environment, 2001, 77(1): 66-75.
206. Maselli F., Romanelli S., Bottai L. and Zipoli G . Use of NOAA～AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 2003, 86(2): 187-197.
207. Malthus T.J.and Maderia A.C. . High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 1993,45: 107—116.
208. McCulloch C.a. H. D.M. . Identification and monitoring of Australian plague locust habitats from Landsat. Remote Sensing of Environment, 1983,(13): 95—102.
209. Medlin C.R., Shaw D.R.,Gerard P.D.and LaMastus F.E. . Using remote sensing to detect weed infestation in Glycine max. Weed Science, 2000,48(3): 393—398.
210. Meyer M.P. and Calpouzos L.. Detection of crop diseases resources. Photogrammetric Engineering & Remote Sensing, 1968,47: 2—9.
211. Michael F., Randall W. and Ronnie H. . Quantitative approaches for using colorinfrared photography for assessing In-season nitrogen status in winter wheat. Agronomy Journal, 2003, 95: 1189-1199.
212. Miller J.R. . Season patterns in leaf reflectance red edge characteristics. International Journal of Remote Sensing, 1991,12(7): 1509-1523.
213. Mkhabela M.S. and Mashinini N.N. . Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA-AVHRR. Agricultural and Forest Meteorology, 2005,129: 1—9.
214. Moran M. S.,Bryant R.,Thome K.,Ni W.,Nouvellon Y.,Gonzalez-Dugo M. P.,Qi J. and Clarke T. R. . A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM+. Remote Sensing of Environment, 2001, 78(1—2): 71—82.
215. Moshoua D., Bravo C, West J., Wahlen S., McCartney A.and Ramona H. . Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture, 2004,44: 173-188.
216. Moya E.A., Barralesa L. R., Apablaza GE. . Assessment of the disease severity of squash powdery mildew through visual analysis, digital image analysis and validation of these methodologies. Crop Protection, 2005, 24: 785-789
217. Muhammed H H and Larsolle A. . Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosystems Engineering, 2003, 86(2): 125-134
218. Muhammed H.H. . Hyperspectral Crop Reflectance Data for characterizing and estimating Fungal Disease Severity in Wheat. Biosystems Engineering, 2005, 91(1): 9-20.
219. Mukai Y., Sugimura T., Watanabe H., and Wakamori K. . Extraction of areas infested by pine bark beetle using Lansat MSS data. Photogrammetric Engineering and Remote Sensing, 1987 54 77 — 81.
220. Neblette C. B. . Airplane photography for plant disease surveys. Photo-Era magazine, 1928, 59: 175.
221. Neblette C.B.. Aerial photography for the study of plant diseases. Photo～Era magazine, 1927, 58: 346.
222. Nicholas C.C. and Christine S. . A comparison of field～based and modeled reflectance spectra from damaged Pinus radiated foliage. Australian Journal of Botany, 2005,(53): 417—429.
223. Nilsson H.E.. Remote sensing of oil seed rape infected by Sclerotinia stem rot and Verticillium wilt. Vaxtskyddsrapporter-jordbruk, 1985a, 33
224. Nilsson H.E. . Remote sensing of 2-row barley infected by net blotch disease. Vaxtskyddsrapporter-jordbruk, 1985b, 34
225. Nilsson H.E. . Remote sensing of 6-row barley infected by barley stripe disease.. Vaxtskyddsrapporter-jordbruk, 1985c, 36
226. Nilsson H.E. . Hand ～held radiometry and IR-thermograph of plant disease in field plot experiments. International Journal of Remote Sensing, 1991, 12(3): 545-557.
227. Nilsson H.E. . Remote sensing and image analysis in plant pathology. Canada Journal of Plant Pathology, 1995,17:154-66.
228. Oguro, Y., Suga Y., Takeuchi S., Ogawa H. and Tsuchiya K. . Monitoring of a rice field using landsat-5 TM and landsat-7 ETM+ data. Advances in Space Research, 2003, 32(11): 2223—2228.
229. Palmer A. R.and van Rooyen A.F. . Detecting vegetation change in the southern Kalahari using Landsat TM data. Journal of Arid Environments, 1998, 39(2): 143- 153.
230. Pax-Lenney M. and Woodcock C.E.. . Monitoring agricultural lands in Egypt with multitemporal Landsat TM imagery: How many images are needed? Remote Sensing of Environment, 1997, 59(3): 522-529.
231. Pax-Lenney M., Woodcock C. E., Macomber S.A., Gopal S. and Song C. . Forest mapping with a generalized classifier and Landsat TM data. Remote Sensing of Environment, 2001, 77(3): 241 — 250.
232. Penuelas J., Fiella. I., Biel C., Serrano L. and Save R. . The reflectance at the 950-970 nm regions as an indicator of plant water status. International Journal of Remote Sensing, 1993, 14: 1887— 1905.
233. Penuelas J., Pinol J. and Filella F. . Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 1997, 18: 2869—2875.
234. Persson P., Hall-Konyves K., Sjostrom G and Pinzke S. . NOAA/AVHRR data for crop productivity estimation in Sweden. Advances in Space Research, 1993, 13(11): 111 — 116.
235. Petkov L., Pieri M., Maselli F. and Maracchi G . Study and modeling of temperature spatial variability by NOAA/AVHRR thermal imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 1996,51(3): 127—136.
236. Polischuk V.P., Shadchina T. M., Kompanetz T.I., Budzanivskaya I.G and Sozinov A.A. Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection, 1997,31(1): 115—119.
237. Prabhakara C, Dalu G and Kunde V.G... Estimation of sea temperature from remote sensing in the 11 to 12 μm window. Journal of Geophysical Research, 1974, 79: 5039—5044.
238. Price J .C. . Band selection procedure for multispectrals canners. Applied Optics, 1994, 33(15): 3281-3288.
239. Price J.C. . Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. Journal of Geophysical Research, 1984, 89(D5): 7231-7237.
240. Qi J., Chehbouni A., Huete A.R. and Kerr Y.H.. A modified soil adjusted vegetation index. Remote Sensing of Environment, 1994,48: 119—126.
241. Qin Z., Dall'Olmo G. And P. Berliner. . Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research, 2001, 106(D19): 22655—22670.
242. Raikes C, and Burpee L. L. . Use of Multispectral Radiometry for Assessment of Rhizoctonia Blight in Creeping Bentgrass. Phytopathology, 1998, 88: 446—449.
243. Ramsey E.W., Hodgson M. E., Sapkota S. K. and Nelson G A. . Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution. Remote Sensing of Environment, 2001,77(3): 279—292.
244. Rees W. G, Williams M. and Vitebsky P. . Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge. Remote Sensing of Environment, 2003, 85(4): 441—452.
245. Reese H. M., Lilies T. M., Nagel D. E., Stewart J. S., Goldmann R. A., Simmons T. E., Chipman J. W.and Tessar P. A.. . Statewide land cover derived from multiseasonal Landsat TM data: A retrospective of the WISCLAND project. Remote Sensing of Environment, 2002, 82(2): 224—237.
246. Richardson A.J., Menges R. M.and Nixon P.R.. Distinguishing weed from crop plants using video remote sensing. Photogrammetric Engineering & Remote Sensing, 1985,51(11): 1785-1790.
247. Riedell W.E., and Blackmer T M. . Leaf Reflectance Spectra of Cereal Aphid-Damaged Wheat. Crop Science, 1999, 39(6): 1835-1840.
248. Sasaki Y, Okamoto T, Imou K. and Torii T. . Automatic diagnosis of plant disease: spectral reflectance of healthy and diseased leaves. In: Proceedings of Agriculture Engineer 1998 Conference, 1998, Oslo: 6.
249. Scotford I.M.and MillerP.C.H. . Applications of Spectral Reflectance Techniques in Northern European Cereal Production: A Review. Biosystems Engineering, 2005, 90(3): 235—250.
250. Seelan S.K., Laguette S., Casady GM. and Seielstad GA. . Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment, 2003, 88: 157-169.
251. Shanahan J.F., Schepers J.S., Francis D.D., Varvel Gary.E., Wilhelm W.W.,Tringe J.M.,Schlemme M.R. and Major D.J. . Use of Remote Sensing Imagery to Estimate Corn Grain Yield. Journal of Agronomy, 2001,93: 583-589.
252. Sobrino J.A., Li z.l., Stoll M.P. and Becker F. . Improvements in the split-window technique for land surface temperature determination. IEEE Transactions on Geosciences and Remote Sensing,, 1994, 32(2): 243-253.
253. Sobrino, J. A., Jimenez-Munoz J. C. and Pauline L.. Land surface temperature retrieval from Land sat TM 5. Remote Sensing of Environment, 2004, 90(4): 434—440.
254. Song C, Woodcock C. E., Seto K. C, Lenney M. P. and Macomber S. A.. Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment, 2001,75(2): 230—244.
255. Stafford J. V.and Benlloch J. V.. Machine-assisted detection of weeds and weed patches. 1997, In: Precision Agriculture 97, Vol. II: Technology, IT and Management (Stafford J V, Ed) (511—518): First European Conference on Precision Agriculture, Warwick, 7-10 September 1997. SCI Bios Scientific Publishers, Oxford.
256. Steddom K., Bredehoeft M. W., Khan M. and Rush C. M.. Comparison of visual and multispectral radiometric disease evaluations of cercospora leaf spot of sugar beet. Plant Disease, 2005, 89(2): 153-158.
257. Stone C., Chisholm L. A. and McDonald S. . Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight. Canadian Journal of Botany, 2003, 81(6): 560—569.
258. Stow D., Hope A., Richardson D., Chen D., Garrisonc.and Service D. . Potential of color-infrared digital camera imagery for inventory and mapping of alien plant invasions in South African shrublands. International Journal of Remote Sensing, 2000, 21(15): 2965-2970.
259. Taubenhaus J. J., Ezekiel W. N. and Neblette C. B. . Airplane photography in the study of cotton root rot. Phytopathology, 1929,19: 1025—1029.
260. Tokola T. . The Influence of Field Sample Data Location on Growing Stock Volume Estimation in Landsat TM-Based Forest Inventory in Eastern Finland. Remote Sensing of Environment, 2000, 74(3): 422-431.
261. Tommervik H., Hogda K.A. and Solheim I. . Monitoring vegetation changes in Pasvik (Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 2003, 85(3): 370—388.
262. Verstraeten W.W., Veroustraete F. and Feyen J. . Estimating evapotranspiration of European forests from NOAA～imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products. Remote Sensing of Environment, 2005, 96(2): 256-276.
263. Vidal A., Pinglo F., Durand H., Devaux-Ros C. and Maillet A. . Evaluation of a temporal fire risk index in Mediterranean forests from NOAA thermal IR. Remote Sensing of Environment, 1994, 49(3): 296-303.
264. Vogelmann J.E.and Rock B.N. . Use of Thematic Mapper data for the detection of forest damage caused by the pear thrips. Remote Sensing of Environment, 1989, 30: 217—225.
265. Voss F.and Dreiser U. In: KrallS., WilpsH.(eds.) .Eschborn. New Trends in Locust Control. 1994: 23-40.
266. Wan Z M.and Dosier J.A. . A generalized split-window algorithm for retrieving land surface temperature from space. IEEE Transactions on Geosciences and Remote Sensing, 1996, 34(4): 892-905.
267. Wang C., Qi J., Moran S. and Marsett R.. . Soil moisture estimation in a semiarid rangeland using ERS～2 and TM imagery. Remote Sensing of Environment, 2004,90(2): 178—189.
268. Williams D.L. . A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected conkfer species. Remote Sensing of Environment, 1991, 35: 79—93.
269. Wilson E. H.and Sader S.A. . Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 2002, 80(3): 385—396.
270. Xin J.F., Yu Z.R., van Leeuwen L. and Driessen P.M.... Mapping crop key phenological stages in the North China Plain using NOAA time series images. International Journal of Applied Earth Observation and Geoinformation, 2002,4(2): 109— 117.
271. Yang Z., Rao M.N., Elliott N.C., Kindler S.D.and Popham T.W.. Using ground-based multispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae) infestation. Computers and Electronics in Agriculture, 2005,47: 121～135.
272. Ye X. J., Sakai K., Garciano L.O., Asada S.L. and Sasao A. . Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological Modelling, 2006, 198: 426～432
273. Zha Y, J. Gao S. Ni Y. Liu J. Jiang and Wei Y. . A spectral reflectance-based approach to quantification of grassland covers from Landsat TM imagery. Remote Sensing of Environment, 2003,87:371～375.
274. Zhou M.C., Ishidaira H., Hapuarachchi H.P., Magome J., Kiem A.S. and Takeuchi K. . Estimating potential evapotranspiration using Shuttleworth-Wallace model and NOAA/AVHRR NDVI data to feed a distributed hydrological model over the Mekong River basin. Journal of Hydrology, 2006, 327:151～173.