水稻高光谱特征及其生物理化参数模拟与估测模型研究
|
摘要
高光谱遥感能在特定光谱范围内以高光谱分辨率同时获取连续的地物光谱曲线，从而可以构成独特的超多维光谱空间，使得遥感应用着重于在光谱维上进行空间信息展开，获得目标物更多的光谱信息，为定量分析地球表面生物物理、生物化学过程提供参数和依据。高光谱遥感具有光谱分辨率高(一般＜10nm)、波段连续性强(在0.4～2.5μm范围内有几百个、上千个波段)、光谱信息量大等特点，其分辨率低于一般地表物质的半吸收宽度(约20～40nm)，因此，高光谱遥感所获得的数据和图像能以足够的光谱分辨率区分那些具有诊断性光谱特征的地表物质，使得高光谱遥感具有广阔的应用前景。
农作物的光谱特征是植被遥感的一个重要方面，它是农作物遥感识别、遥感长势监测和估产、遥感品质监测的重要依据。农作物光谱与农作物品种、植株密度、冠层结构、叶片形状、叶组织结构、农作物生化组分及比例、光谱测量条件(如气象条件、光谱仪分辨率、测量日期、背景)等因素有关。水稻高光谱特征及其理化基础和农学机理是水稻长势遥感监测、遥感估产和水稻品质遥感监测的主要依据。早期对水稻光谱的研究主要是针对遥感长势监测与估产，近年来对水稻光谱的研究主要集中在高光谱植被指数构建、导数光谱处理方法及辐射模型反演三个方面，目的是探索水稻光谱的物理化学基础和农学机理、进而提高水稻遥感长势监测和估产的精度，并利用水稻高光谱来估算其蛋白质、淀粉含量，进而探究水稻品质与其光谱特征之间的关系。
品种、生态环境、施肥量和施肥方法、栽种密度等因素都会影响水稻的株型结构、冠层结构、LAI、生物量、叶绿素密度、营养成分转移方法及速率等农学、生物化学参数，进而影响衡量水稻品质的两个重要参数——粗蛋白质含量和直链淀粉含量。而遥感理论和实践表明，植被株型结构、
中文摘要
冠层结构、LAI、生物量、叶绿素密度和生化组分及其含量的不同，其反射
光谱大小及特征也会不同。因此，从理论上讲，通过监测水稻在各个不同
发育时期(特别是抽穗后各时期)的光谱及其变化，可以预测稻米中的蛋
白质和直链淀粉的积累，进而监测水稻品质。
本研究得到了国家“863”子课题“水稻品质遥感监测研究与应用”
(2002AA243011)、国家自然科学基金项目“不同氮素水平的水稻高光
谱遥感精确诊断机理与方法研究”(40 1 7 1 065)的资助，是在浙江大学农
业遥感与信息技术应用研究所从1983年以来对水稻光谱研究的基础上，通
过适当的水培和小区试验设计、测量与分析，获取不同品种在不同氮素水
平下、不同发育时期水稻冠层和叶片的高光谱数据及水稻生育期间的平均
气温等气象数据，测定水稻主要生化组分的高光谱、冠层和组分的生物物
理与生物化学参数，对试验结果通过多种方法予以分析，建立水稻产量、
主要生化组分的高光谱遥感估测模型，探索水稻品质遥感监测的可行性和
初步建立水稻品质综合监测模型，进而探索水稻冠层、器官和生化组分的
高光谱特征及其理化基础和农学机理。具体研究内容及结果如下:
.通过两年水稻田间试验，培养出不同品种在不同氮素营养水平下的
水稻试验样本;
.利用光谱仪系统测定不同品种水稻于不同氮素营养水平下在不同
时期的冠层、日一卜片、穗和稻谷光谱，及水稻主要生化组分(蛋白质、
粗淀粉、直链淀粉)的高光谱，比较了不同品种水稻冠层、叶片的
光谱差异、水稻不同生化组分的光谱特征及差异;
.根据高光谱特点和水稻光谱特征，明确了水稻产量与冠层光谱高光
谱植被指数的相关性在抽穗前以RVI较好、在抽穗后以OVI较好，
并建立了水稻高光谱遥感估产模型，检验精度在95%以上;
.采用单变量线性与非线性拟合模型和逐步回归分析，得到了单位面
积叶片全氮含量的最佳估测模型为原始光谱反射率的逐步回归模
型、单位面积土地上叶片全氮含量和单位面积土地上叶茎全氮含量
的最佳估测模型为一阶导数光谱值的逐步回归模型、稻穗粗蛋白
质、粗淀粉含量和稻谷粗蛋白质、粗淀粉含量的最佳估测模型为一
阶导数光谱值的逐步回归模型，模型检验精度在90%以上;
.分析了不同品种之间、同一品种不同生长条件下时粗蛋白质、淀粉
和直链淀粉含量差异，建立了水稻品质两个重要指标—粗蛋白质
中文摘要
尸和直链淀粉A含量的一般预测模型，可以根据水稻的冠层光谱和
灌浆期的日平均温度来预测，
P(A)=S(兄)*(kT+b)
模型检验精度一般在90%以上;
.分析了水稻冠层和室外廿卜片的红边特征，明确了它们的红边“双峰”
和“多峰”现象并不是由导数光谱的计算方法引起的，而可能是由
光源、气象条件、冠层结构等因素引起的;
.对水稻蛋白质和粗淀粉的混合光谱分析发现，混合物的反射光谱与
纯净物反射光谱相比会出现峰、谷位置“红移”或“蓝移”现象，
且稻米蛋白质和粗淀粉含量与其光谱曲线在2020一2235nm之间的
吸收面积S具有显著相关。
本研究的技术新意和创新点有以下三个方面:
.本研
Hyperspectral remote sensing can note the continual spectral curves of ground objects with hyperspectral resolution at the same time in specifical waveband range. They may form particular super-multidimensional spectral space to bring the application of remote sensing emphasize on space information on spectral dimension for obtaining more spectral information. This will provide parameters and basis for quantitive analysis of the biophysical and biochemical processes on the ground surface. It has high spectral resolution (generally less than 10nm), strong waveband continuity (several hundreds and thousands wavebands from 0.4 u m to 2.5 um and a great deal of spectral information. Its spectral resolution is commonly less than the half absoring width (about 20~40nm) of common objects. Therefore the data and pictures obtained by hyperspectral remote sensing are callable of distinguishing objects having diagnostic characteristics with adequate spectral resolution. It has wide application foreground.
The spectral characteristics of crops is an significant aspect of remote sensing of vegetation and an important basis of distinguishing crops, monitoring their growth, estimating their yield and quality by remote sensing. The crops spectra are relate to their variety, density, canopy structure, leaf shape, leaf structure, biochemical components and rate of all biochemical components and conditions of measuring spectrum such as meteorology, resolution of spectroradiometer, measuring date and background. The hyperspectral characteristics of rice and its physical and chemical basis and agronomic mechanism are the primary foundation of its growth monitoring, yield estimating and quality monitoring by remote sensing. The study of rice spectrum mostly aimed at its growth monitoring and yield estimating in early time. This study mostly focused on constitution hyperspectral vegetation
index, method of derivative spectrum and retro-deduction of radiant models. The purpose is to search after the physical and chemical basis and agronomic mechanism of rice spectrum, improve the precision of growth monitoring and yield estimating, and estimating the contents of its crude protein and amylose. The final aim is at exploring the relation between rice quality and its spectral characteristics.
The factors such variety, entrionment, quantity and method of fertilization and density could influence on the agronomic and biochemical parameters of rice such as its structure of plant and canopy, leaf area index LAI, biomass, chlorophyll density and transfer method and speed of nutrition. They cuould more influence on two important parameters weighing rice quality such as the contents of crude protein and amylose. The theory and practice of remote sensing indicate that vegetation with different structure of plant and canopy, leaf area index LAI, biomass, chlorophyll density, biochemical component and their contents will bring on different value and characteristics of reflective spectra. It may theoretically predict the accumulation of crude protein and amylose of rice by monitoring the rice spectrum and its change at different stages especially after heading. In this way, the quality of rice could be monitored.
This study is Supported by the National '863' Project of China "Study on Monitoring Quality of Rice by Remote Sensing and Its Application" (2002AA243011) and the National Natural Science Foundation of China " Study on Mechanism and Method of Precisely Diagnose Hyperspectra of Rice under Different Nitrogen Levels" (40171065). On the basis of rice spectrum having been studied by Institute of Agricultural remote sensing & information application from 1983, it is to obtain the hyperspectral data of canopy and leaf of different variety of rice at different stages under different nitrogen levels and some meteorologic data such as average temperature in growth stages, determine the hyperspectral data of main biochemical component and the biophysical and biochemical parameters of canopy and organ. It is to establish estimating models of yi
农作物的光谱特征是植被遥感的一个重要方面，它是农作物遥感识别、遥感长势监测和估产、遥感品质监测的重要依据。农作物光谱与农作物品种、植株密度、冠层结构、叶片形状、叶组织结构、农作物生化组分及比例、光谱测量条件(如气象条件、光谱仪分辨率、测量日期、背景)等因素有关。水稻高光谱特征及其理化基础和农学机理是水稻长势遥感监测、遥感估产和水稻品质遥感监测的主要依据。早期对水稻光谱的研究主要是针对遥感长势监测与估产，近年来对水稻光谱的研究主要集中在高光谱植被指数构建、导数光谱处理方法及辐射模型反演三个方面，目的是探索水稻光谱的物理化学基础和农学机理、进而提高水稻遥感长势监测和估产的精度，并利用水稻高光谱来估算其蛋白质、淀粉含量，进而探究水稻品质与其光谱特征之间的关系。
品种、生态环境、施肥量和施肥方法、栽种密度等因素都会影响水稻的株型结构、冠层结构、LAI、生物量、叶绿素密度、营养成分转移方法及速率等农学、生物化学参数，进而影响衡量水稻品质的两个重要参数——粗蛋白质含量和直链淀粉含量。而遥感理论和实践表明，植被株型结构、
中文摘要
冠层结构、LAI、生物量、叶绿素密度和生化组分及其含量的不同，其反射
光谱大小及特征也会不同。因此，从理论上讲，通过监测水稻在各个不同
发育时期(特别是抽穗后各时期)的光谱及其变化，可以预测稻米中的蛋
白质和直链淀粉的积累，进而监测水稻品质。
本研究得到了国家“863”子课题“水稻品质遥感监测研究与应用”
(2002AA243011)、国家自然科学基金项目“不同氮素水平的水稻高光
谱遥感精确诊断机理与方法研究”(40 1 7 1 065)的资助，是在浙江大学农
业遥感与信息技术应用研究所从1983年以来对水稻光谱研究的基础上，通
过适当的水培和小区试验设计、测量与分析，获取不同品种在不同氮素水
平下、不同发育时期水稻冠层和叶片的高光谱数据及水稻生育期间的平均
气温等气象数据，测定水稻主要生化组分的高光谱、冠层和组分的生物物
理与生物化学参数，对试验结果通过多种方法予以分析，建立水稻产量、
主要生化组分的高光谱遥感估测模型，探索水稻品质遥感监测的可行性和
初步建立水稻品质综合监测模型，进而探索水稻冠层、器官和生化组分的
高光谱特征及其理化基础和农学机理。具体研究内容及结果如下:
.通过两年水稻田间试验，培养出不同品种在不同氮素营养水平下的
水稻试验样本;
.利用光谱仪系统测定不同品种水稻于不同氮素营养水平下在不同
时期的冠层、日一卜片、穗和稻谷光谱，及水稻主要生化组分(蛋白质、
粗淀粉、直链淀粉)的高光谱，比较了不同品种水稻冠层、叶片的
光谱差异、水稻不同生化组分的光谱特征及差异;
.根据高光谱特点和水稻光谱特征，明确了水稻产量与冠层光谱高光
谱植被指数的相关性在抽穗前以RVI较好、在抽穗后以OVI较好，
并建立了水稻高光谱遥感估产模型，检验精度在95%以上;
.采用单变量线性与非线性拟合模型和逐步回归分析，得到了单位面
积叶片全氮含量的最佳估测模型为原始光谱反射率的逐步回归模
型、单位面积土地上叶片全氮含量和单位面积土地上叶茎全氮含量
的最佳估测模型为一阶导数光谱值的逐步回归模型、稻穗粗蛋白
质、粗淀粉含量和稻谷粗蛋白质、粗淀粉含量的最佳估测模型为一
阶导数光谱值的逐步回归模型，模型检验精度在90%以上;
.分析了不同品种之间、同一品种不同生长条件下时粗蛋白质、淀粉
和直链淀粉含量差异，建立了水稻品质两个重要指标—粗蛋白质
中文摘要
尸和直链淀粉A含量的一般预测模型，可以根据水稻的冠层光谱和
灌浆期的日平均温度来预测，
P(A)=S(兄)*(kT+b)
模型检验精度一般在90%以上;
.分析了水稻冠层和室外廿卜片的红边特征，明确了它们的红边“双峰”
和“多峰”现象并不是由导数光谱的计算方法引起的，而可能是由
光源、气象条件、冠层结构等因素引起的;
.对水稻蛋白质和粗淀粉的混合光谱分析发现，混合物的反射光谱与
纯净物反射光谱相比会出现峰、谷位置“红移”或“蓝移”现象，
且稻米蛋白质和粗淀粉含量与其光谱曲线在2020一2235nm之间的
吸收面积S具有显著相关。
本研究的技术新意和创新点有以下三个方面:
.本研
Hyperspectral remote sensing can note the continual spectral curves of ground objects with hyperspectral resolution at the same time in specifical waveband range. They may form particular super-multidimensional spectral space to bring the application of remote sensing emphasize on space information on spectral dimension for obtaining more spectral information. This will provide parameters and basis for quantitive analysis of the biophysical and biochemical processes on the ground surface. It has high spectral resolution (generally less than 10nm), strong waveband continuity (several hundreds and thousands wavebands from 0.4 u m to 2.5 um and a great deal of spectral information. Its spectral resolution is commonly less than the half absoring width (about 20~40nm) of common objects. Therefore the data and pictures obtained by hyperspectral remote sensing are callable of distinguishing objects having diagnostic characteristics with adequate spectral resolution. It has wide application foreground.
The spectral characteristics of crops is an significant aspect of remote sensing of vegetation and an important basis of distinguishing crops, monitoring their growth, estimating their yield and quality by remote sensing. The crops spectra are relate to their variety, density, canopy structure, leaf shape, leaf structure, biochemical components and rate of all biochemical components and conditions of measuring spectrum such as meteorology, resolution of spectroradiometer, measuring date and background. The hyperspectral characteristics of rice and its physical and chemical basis and agronomic mechanism are the primary foundation of its growth monitoring, yield estimating and quality monitoring by remote sensing. The study of rice spectrum mostly aimed at its growth monitoring and yield estimating in early time. This study mostly focused on constitution hyperspectral vegetation
index, method of derivative spectrum and retro-deduction of radiant models. The purpose is to search after the physical and chemical basis and agronomic mechanism of rice spectrum, improve the precision of growth monitoring and yield estimating, and estimating the contents of its crude protein and amylose. The final aim is at exploring the relation between rice quality and its spectral characteristics.
The factors such variety, entrionment, quantity and method of fertilization and density could influence on the agronomic and biochemical parameters of rice such as its structure of plant and canopy, leaf area index LAI, biomass, chlorophyll density and transfer method and speed of nutrition. They cuould more influence on two important parameters weighing rice quality such as the contents of crude protein and amylose. The theory and practice of remote sensing indicate that vegetation with different structure of plant and canopy, leaf area index LAI, biomass, chlorophyll density, biochemical component and their contents will bring on different value and characteristics of reflective spectra. It may theoretically predict the accumulation of crude protein and amylose of rice by monitoring the rice spectrum and its change at different stages especially after heading. In this way, the quality of rice could be monitored.
This study is Supported by the National '863' Project of China "Study on Monitoring Quality of Rice by Remote Sensing and Its Application" (2002AA243011) and the National Natural Science Foundation of China " Study on Mechanism and Method of Precisely Diagnose Hyperspectra of Rice under Different Nitrogen Levels" (40171065). On the basis of rice spectrum having been studied by Institute of Agricultural remote sensing & information application from 1983, it is to obtain the hyperspectral data of canopy and leaf of different variety of rice at different stages under different nitrogen levels and some meteorologic data such as average temperature in growth stages, determine the hyperspectral data of main biochemical component and the biophysical and biochemical parameters of canopy and organ. It is to establish estimating models of yi
引文
1．浦瑞良，宫鹏．森林生物化学与CASI高光谱分辨率遥感数据的相关分析[J]．遥感学报，1997，1(2)：115～123．
2．浦瑞良，宫鹏．高光谱遥感及其应用[M]．北京，高等教育出版社，2000：3～35．
3．湖南省优质稻生产技术体系及其应用理论研究协作组．施肥对稻米品质和产量影响的研究[J]．湖南农学院学报，1989，15(3)：1～5．
4．江丕栋译．生物聚合物波谱学导论(M)．科学出版社，北京，1983：21～95．
5．沈掌泉，王人潮．连续型光谱数据的处理及信息提取试验[J]．浙江农业大学学报，1993(增)，19：83～88．
6．潘晓华，李木英，曹黎明等．水稻发育胚乳中淀粉的积累及淀粉合成的酶活性变化[J]．江西农业大学学报，1999，21(4)：456～462．
7．郑兰芬，王晋年．成像光谱遥感技术及其图像光谱信息提取分析研究[J]．环境遥感，1992，7(1)：49～58
8．郑兰芬，童庆禧，王晋年．高光谱分辨率遥感研究进展[M]．遥感科学进展，科学出版社，1995，42～50．
9．郑九如，林文彬，林荣光等．改良稻米品质提高稻米经济价值[J]．福建稻米科技，1992，10(1)：4～6
10．曾琪明，马洪兵，张涛．水稻微波后向散射系数的模拟分析[J] 遥感学报，2000，4(1)：14～21．
11．辛淑亮，蔡秋芳．现代农业试验统计．北京，中国计量出版社，1999：270
12．颜龙安，李季能，钟海明等．优质稻米生产技术[M]．北京：中国农业出版社，1999，30～37．
13．方红亮，田庆久．高光谱遥感在植被监测中的研究综述[J]．遥感技术与应用，1998，13(1)：62～69
14．唐延林，王秀珍，黄敬峰等．水稻微分光谱和植被指数的作用探讨[J]．农业工程学报，2003A，19(1)：145～150．
15．唐延林，王秀珍，黄敬峰等．棉花高光谱及其红边特征(Ⅰ)[J]．棉花学报，2003B，15(3)：146～150．
16．唐延林，黄敬峰．农业高光谱遥感研究的现状与发展趋势[J]．遥感技术与应用，2001，16(4)：248～251．
17．刘伟东，项月琴，郑兰芬等．高光谱数据与水稻叶面积指数及叶绿素密度
的相关性分析[J]．遥感学报，2000，4(4)：279～283．
18．刘立军，王志琴，桑大志，杨建昌．氮肥运筹对水稻产量及稻米品质的影响[J]．扬州大学学报(农业与生命科学版)，2002，23(3)：46～50．
19．谭倩，赵永超，童庆禧等．植物光谱维特征提取模型[J]．遥感信息，2001．1：14-18．
20．褚圣麟．原子物理学(M)．北京，人民教育出版社，1983：250～284．
21．王晋年，张兵，刘建贵等．以地物识别和分类为目标的高光谱数据挖掘[J]．中国图象图形学报，1999，4(11)：957～964．
22．王春胜，禹秉熙，戴逸松等．高光谱图像数据变换编码压缩方法[J]．遥感学报，2000，4(2)：95～99．
23．王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅰ．不同氮素水平的水稻光谱特征及其敏感波段的选择[J]．浙江农业大学学报，1993A(增刊)：7～14．
24．王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅱ．农学参数与光谱变量的相关分析[J]．浙江农业大学学报，1993B(增刊)：15～22．
25．王人潮，黄敬峰．水稻遥感估产[M]．北京，中国农业出版社．2002：38～79．
26．王纪华，赵春江，郭晓维等．用遥感方法来监测小麦叶片水分含量[J]．华北农学报，2000，15(4)：68～72．
27．王纪华，黄文江，赵春江等．利用光谱反射率估算叶片生化组分和籽粒品质指标研究[J]．遥感学报，2002，6(增)：92～98．
28．王秀珍．水稻生物物理与生物化学参数的光谱遥感估算模型研究(D)．浙江大学博士学位论文，2001：88～90．
29．王秀珍，王人潮，黄敬峰．微分光谱遥感及其在水稻农学参数测定上的应用研究[J]．农业工程学报，2002A，18(1)：9～13．
30．王秀珍，黄敬峰，李云梅等．高光谱数据与水稻农学参数之间的相关分析[J]．浙江大学学报(农业与生命科学版)，2002B，28(3)：283～288．
31．王长耀，牛铮，唐华俊．对地观测技术与精细农业[M]．科学出版社，2001．
32．王绍华，曹卫星，王强盛等．水稻叶色分布特点与氮素营养诊断[J]．中国农业科学，2002，35(12)：1461～1466．
33．王鼎昌译．分子光谱与分子结构(第二卷)——多原子分子的红外光谱与喇曼光谱(M)．科学出版社，北京，1986：499～505．
34．丁元树主编．浙江粮食与吨粮技术[M]．杭州，浙江科学技术出版社，1992．
35．吉海彦 严衍禄．红外光谱图像技术及其在生物学研究中的应用[J]．物理，2000，29(3)：174-177，161．
36．赵春江，黄文江，王纪华等．不同品种、肥水条件下冬小麦光谱红边参数研究[J]．中国农业科学，2002，35(8)：980～987．
37．赵增煜：常用农业科学试验法[M]．北京，农业出版社，1986：2～7．
38．赵华山，姜胶东，吴大诚等．高分子物理学(M)．北京，纺织工业出版社，1994：409～416．
39．戴平安，刘向华．氮磷钾及有机肥不同配施量对水稻品质和产量效应的研究[J]．作物研究，1999，13(3)：26～30．
40．蒋德安主编．植物生理学实验指导[M]．成都，成都科技大学出版社，1999．
41．薛利红，罗卫红，曹卫星等．作物水分和氮素光谱诊断研究进展[J]．遥感学报，2003，7(1)：73～80．
42．南京农业大学主编：土壤农化分析[M]．北京，农业出版社，1996年10月第2版：19～94、232～234、257～265．
43．胡玉才，戴 寰译．对称性与光谱学——振动和电子光谱学导论(M)．高等教育出版社，北京，1988：66～147．
44．李云梅．水稻BRDF模型集成与应用研究．浙江大学博士学位论文，2001．
45．李云梅，王人潮，王秀珍等．水稻冠层二向反射率的模拟及其反演[J]．中国水稻科学，2002A，16(3)：291～294．
46．李云梅，王秀珍，沈掌泉等．水稻叶片反射率模拟[J]．浙江大学学报(农业与生命科学版)，2002B，28(3)：195～198．
47．李林，沙国栋，陆准淮．灌浆结实期温光因子对稻米品质的影响[J]．中国农业气象，1996，5(2)：33～38．
48．李小文．地物的二向性反射和方向谱特征[J]．环境遥感，1989，4(1)：67～72．
49．李小文，汪骏发，王锦地等．多角度与热红外对地遥感[M]．北京，科学出版社，2001．
50．李军，顾德法，李林峰．环境和栽培因子对稻米品质影响的研究进展[J]．上海农业学报，1997，13(1)：94～97．
51．李筱明，刘进明．水稻品种不同季节栽培稻米品质的影响[J]．湖南农业科学，1993，5：16～17．
52．李德成，徐彬彬，石晓日等．模拟酸雨对水稻叶片反射光谱特性影响的初
步研究[J]．环境遥感，1996，11(4)：241～247．
53．杨吉龙，李家存，杨德明．高光谱分辨率遥感在植被监测中的应用综述[J]．世界地质，2001，20(3)：306～312．
54．杨长明，杨林章，韦朝领等．不同品种水稻群体冠层光谱特征比较研究[J]．应用生态学报，2002，13(6)：689～692．
55．夏有龙，蒋观真，周春和等．水稻栽培实用新技术．南京，江苏科学技术出版社，1993．
56．小西教夫，志贺弘行，安责大志等．遥感技术在美味稻米生产中的应用[J]．日本测量学杂志，2000，7：14～21．
57．吴关庭，夏英武．环境与栽培对稻米品质的影响[J]．中国稻米，1994，4：37～39．
58．吴豪翔，王人潮．土壤光谱特征及其定量分析在土壤分类上的应用[J]．土壤学报，1991，28(2)：178～185．
59．吴长山，项月琴，郑兰芬等．利用高光谱数据对作物群体叶绿素密度估算的研究[J]．遥感学报，2000，4(3)：228～232．
60．申广荣，王人潮，李云梅等．水稻群丛结果和辐射传输分析[J]．作物学报，2001 A，27(6)：769～775
61．申广荣，王人潮，李云梅等．水稻多组分双向反射模型的建立[J]．农业工程学报，2001B，17(5)：146～149．
62．申广荣，王人潮．基于神经网络的水稻双向反射模型研究[J]．遥感学报，2002，6(4)：252～258．
63．罗玉坤，施一平，闵 捷等．中国食用优质米品质的分析研究[J]．浙江农业学报，1991，3(2)：55～60．
64．罗玉坤，朱智伟，张伯平等．食用稻米品质的理化指标与食味的相关性研究[J]．中国水稻科学，1997，11(2)：70～76．
65．陆凳槐．农业遥感的应用效益及在我国的发展战略[J]．农业工程学报，1998，14(3)：64～67．
66．陈述彭，童庆禧，郭华东主编．高光谱分辨率遥感信息机理与地物识别[M]．北京，科学出版社，1998．
67．牛铮，陈永华，隋洪智等．叶片化学成分成像光谱遥感探测机理分析[J]．遥感学报，2000，4(2)：125～129．
68．朱旭东，熊振民，罗玉坤等．异季栽培对稻米品质的影响[J]．中国水稻科
学，1993，7(3)：172-174．
69．程方民，钟连进，不同气候生态条件下稻米品质性状的变异及主要影响因子分析[J]．中国水稻科学．2001，15(3)：187-191．
70．白宝璋，汤学军主编．植物生理学测试技术[M]．北京，中国科学技术出版社，1993．
71．徐正进，陈温福，张庄步等．水稻品质性状的品种间差异及其与产量关系的研究[J]．沈阳农业大学学报，1993，24(3)：217～223．
72．徐大勇，金军，杜永，潘启民，朱庆森．江苏省主要高产粳稻品种品质性状分析[J]．江苏农业学报，2002，18(4)：203～207．
73．周永洽．分子结构分析(M)．化学工业出版社，北京，1991年1月第1版：1～12．
74．周瑞庆．肥料种类及营养元素对稻米产量与品质影响的初步研究[J]．作物研究，1988，2(1)：14-17．
75．周培南，冯惟珠，许乃霞，张亚洁，苏祖芳．施氮量和移栽密度对水稻产量及稻米品质的影响[J]．江苏农业研究，2001，22(1)：27～31．
76．周德翼，张嵩午，高如嵩等．稻米综合品质与结实期气象因子的关系研究[J]．西北农业大学学报，1994，22(2)：6～10．
77．鲁如坤．土壤—植物营养学原理[M]．北京：化学工业出版社，1998．281～305．
78．张良培，郑兰芬，童庆禧．利用高光谱对生物变量进行估计[J]．遥感学报，1997A，1(2)：111～114．
79．张良培，李德仁．鄱阳湖地区高光谱遥感数据的定标研究[J]．武汉测绘科技大学学报，1997B，22(1)：35～38．
80．张丰，熊桢，寇宁．高光谱遥感数据用于水稻精细分类研究[J]．武汉理工大学学报，2002，24(10)：36～39．
81．张玉华．稻米直链淀粉含量及其影响因素研究[J]．黑龙江农业科学，2002，3：34～37．
82．孙家柄主编．遥感原理与应用[M]．武汉，武汉大学出版社，2003：32～77．
83．熊振民，朱旭东，罗玉坤等．稻米品质研究的新进展[J]．水稻文摘，1993．12(3)1～6．
84. Aldakheel Y Y, Danson F M. Spectral reflectance of dehydrating leaves: measurements and modeling[J]. Int. J Remote Sens. 1997, 18(17):3683～3690.
85. Anser G P. Biophysical and biochemical sources of variability in canopy reflectance[J]. Remote Sens. Environ., 1998, 64: 234～253.
86. Bach H, Mauser W. Improvements of plant parameter estimations with hyperspectral data compared to multispectral data[J]. SPIE. 1997, 2959: 59～67.
87. Crosta AP, Souza C R de F. Evaluating AVIRIS hyperspectrai remote sensing data for geological mapping in Laterized Terranes, Central Brazil. Proceeding of the Twelfth International Conference and Workshops on Applied Geologic Remote Sensing, Denver, Colorado. 17-19 November, 1997,Volume, 1997, Ⅱ:430～437.
88. Barton F E, Himmelsbach D S, McClung A M, et al., Rice quality by spectroscopic analysis: Precision of three spectral regions[J]. Cereal Chem. 2000,77(5):669～672.
89. Bournan B AM. Crop modeling and remote sensing for yield prediction[J]. Neth.J. Agric.Sci. 1995,43:143～161.
90. Burgan R E. Use of remotely sensed data for fire danger estimation[J]. Earsel Advances in Remote Sensing. 1996, 4(4): 1～8.
91. Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches[J]. Remote Sens. Environ., 1998, 66: 273～285.
92. Blackburn G A. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves[J], Int.J. Remote Sens. 1998, 19(4): 657～675.
93. Blackburn G A, Milton E J. Seasonal variations in the spectral reflectance of deciduous tree canopies[J], Int.J.Remote Sens. 1995, 16(4): 709～720.
94. Blakeney A B, Lewin L G and Reinke R F. Quality Rice for North Asia[A]. A report for the Rural Industries Research and Development Corporation. RIRDC Project No BRE-IA, 2001.
95. Boegh E, Soegaard H, Hanan N,ect. A Remote Sensing Study of the NDVITS Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High-Resolution Satellite Data[J]. Remote Sens. Environ., 1999, 69: 224～240
96. Boochs F, Kupfer G, Dockter. Shape of the red edge as vitality indicator for plants, Internal Journal of Remote Sensing, 1990. 11(12):1741～1753.
97. Bostater C, et al. Temporal measurement and analysis of high resolution spectral signatures of plants and relationships to biophysical characteristics. SPIE, 1991,2585:298～309.
98. Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data[J]. Remote Sens. Environ., 2002, 81: 45～57.
99. Caetano M, Pereira J M C. Analysis of the integrated (overstory/background) hyperspectral response of pine stands[J]. SPIE, 1997, 3222: 26～37.
100. Card D H, Peterson D L, Matson PA,et al. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy[J]. Remote Sens. Environ., 1988, 26: 123～147.
101. Casanova D, Epema G F, Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI[J]. Field Crops Research. 1998, 55: 83～92.
102. Casanova D, Goudriaan J, Bosch A D. Testing the performance of ORYZAl, an explanatory model for rice growth simulation, for Mediterranean conditions[J]. European Journal of Agronomy, 2000, 12:175～189.
103. Ceccato P, Flasse S, Gregoire M J. Designing a spectral indes to estimate vegetation water content from remote sensing data: Part 1 Validation and applications [J]. Remote Sens. Environ. 2002, 82: 198～207.
104. Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Sci., 2002, 167(2): 110～116.
105. Choubey V K, Choubey R. Spectral reflectance, growth and chlorophyll relationships for rice crop in a semi-arid region of India. Water resources management. 1999,13: 73～84.
106. Curran P J. Remote sensing of foliar chemistry[J]. Remote Sens. Environ. 1989, 30: 271～278.
107. Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine[J]. Tree Physiology, 1990, 7: 33～48.
108. Curran P J, Dungan J L, Gholz H L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry[J]. Remote sens.Environ. 2001, 76:349～359.
109. Curran P J, Windham W R, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine leaves[J]. Tree Physiology. 1995, 15: 203～206.
110. Daniel A S, 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[J]. Remote Sens. Environ. 2002, 82: 1～12.
111. Danson F M. Red Edge Response to Leaf Area Index[J]. Int.J. Remote Sensing, 1995, 36(1): 45～53.
112. Davies A, Grant A M C. Near infrared spectrocopy for the analysis of specific molecules in food. In Analytical Applications of Spectroscopy(M). Royal Society of Chemistry, London, 1988: 46～51.
113. Dawson T P, Curran P J, Plummer S E. LIBERTY modeling the effects of leaf biochemical concentration on reflectance spectra[J]. Remote Sens. Environ., 1998, 65: 50～60.
114. Demetriades-Shah T H, Steven M D, Clark J A. High Resolution Derivative Spectra in Remote Sensing[J]. Remote Sens.Environ., 1990,33:55～64
115. Dipti S S, Bari M N and Kabir K A. Grain Quality Characteristics of Some Beruin Rice Varieties of Bangladesh[J]. Pakistan Journal of Nutrition, 2003, 2 (4): 242～245.
116. Dungan J, Johnson L, Billow C, et al. High spectral resolution reflectance of Douglas fir grouth under different fertilization treatments: experiment design and treatment effect[J]. Remote Sens. Environ. 1996, 55: 217～228.
117. Eiji K, Yoshio A, Kunihiro T, et al. Seasonal patterns of canopy structure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy[J]. Forest Ecology and Management, 2002, 167: 233～249.
118. EOSAT. Comparison of Satellites and Sensors. Earth Resources Satellite Systems [M]. Lanham, Marland, USA, 1995.
119. Feng; Y, Miller J R. Vegetation green reflectance at high spectral resolution
as a measure of leaf chlorophyll content[A]. In: Proceedings of the 14th Canadian Symposium on Remote Sensing, Calgary Alberta. 1991, 351～355.
120. Filella D, Pen-uelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status[J]. Int.J.Remote Sens., 1994, 15(7): 1459～1470.
121. Fourty, T, Baret F. On spectral estimates of fresh leaf biochemistry[J]. Int.J. Remote Sens. 1998, 19(7): 1289～1297.
122. Fourty, T, Baret F, Jacquemoud, S. Leaf optical properties with explicit description of its biochemical composition:direct and inverse problems[J]. Remote sens.Environ. 1996, 56:104～117.
123. Gastellu-Etchegorry J P, et al. Assessment of canopy chemistry with AVIRIS case study in the Landes Forest South-West France[J]. Int.J.Remote Sens., 1995, 16(3): 487～501.
124. Gausman H W, Allen W A, Cardenas R. Relation of light reflectance to histological and physical evaluations of cotton leaf maturity[J]. Application of Optics, 1970, 9: 545～552.
125. Gebbing T, Schnyder H. Pre-anthesis reserve utilization for protein and carbohydrate synthesis in grains of wheat[J]. Plant Physiolohy. 1999, 121: 871～878.
126. Gibaert M A, Gandia S, Melia J. Analyses of Spectral-Biophysical Relationships for a Corn Canopy[J]. Remote Sens. Environ. , 1996, 55 : 11～20.
127. Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll[J]. J. Plant Physical. 1996, 148: 494～500.
128. Gitelson AA, Merzlyak M N. Remote estimation of chlorophyll content in higher plant leaves[J]. Int.J.Remote Sens. 1997, 18(12): 2691～2697.
129. Gong P, Pu R L, Miller J R. Correlating leaf area index of ponderosa pine with hyperspectral CASI data[J]. Canadian J. Remote Sens., 1992, 18: 275～282.
130. Gong P, Pu R L, Miller J R. Coniferous forest leaf area index estimation along the Oregon transect using compact airborne spectrographic imager data[J]. P E & R S. 1995, 61(9): 1107～1117.
131. Gong P, Pu R L, Yu B. Conifer species recognition: an esploratory analysis on in situ hyperspectral data[J]. Remote Sens. Environ., 1997, 62: 189～200.
132. Grossman Y L, Ustin S L, Jacquemoud S, et al. Critique stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data[J]. Remote Sens. Environ. 1996, 56: 182～193.
133. Gupta R K, Vijayan D, Prasad T S. New hyperspectral vegetation characterization parameters[J]. Ads. Space Res. 2001A, 28(1): 201～206.
134. Gupta R K, Vijayan D, Prasad T S. Characterization of red-near infrared transition for wheat and chickpea using 3 nm bandwidth data[J]. Adv. Space Res. 2001B, 28(1): 189～194.
135. Himmel J W, Sudduth K A, Hollinger S E. Soil moisture and organic matter perdiction of surface and subsurface soils using an NIR soil sensor[J]. Computers and Electronics in Agriculture. 2001, 32: 149～165.
136. Horig B, Kuhn F, Oschutz F, et al. HyMap hyperspectral remote sensing to detect hydrocarbons[J]. Int. J. Remote Sens. 2001, 22(8): 1413～1422.
137. Horler D N H, Dockray M, Barber J. The red edge of plant leaf reflectance. Internal Journal of Remote Sensing, 1983. 4(3): 273～288.
138. Hunt G R. Electromagnetic radiation: the communication link in remote sensing. In: Remote Sensing in Grology. Siegal B, Gillespia A. Wiley New York, 1980,702.
139. Inoue Y, Penuelas J, Nouevllon Y, et al. Hyperspectral reflectance measurements for estimating eco-physiological status of plants[J]. SPIE, 2001, 4151:153～163.
140. Inoue Y, Penuelas J. An AOTE-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. Int. J. Remote Sens. 2001, 22(18): 3883～3888.
141. Irudayaraj J & Yang H. Depth profiling of a heterogeneous food-packaging model using step-scan Fourier transform infrared photoacoustic spectroscopy[J]. Journal of Food Engineering, 2002,55: 25～33.
142. Jacquemoud S, Baret F. PROSPECT: a model of leaf optical properties spectra[J]. Remote Sens. Environ., 1990, 34: 75～91.
143. Jacquemoud S, Ustin S L, Verdebout J, et al., Estimating leaf biochemistry
using the PROSPECT leaf optical properties model[J]. Remote Sens. Environ., 1996, 56: 194～202.
144. Jacquemoud S, Verdebout J, Schmuck G, et al. Investigation of leaf biochemistry by statistics[J]. Remote Sens. Environ., 1995, 54: 180～188.
145. Johnson L F, Hlavaka C A, Peterson D L. Multivariate analysis of AVIRIS aata for canmopy, biochemical estimation along the Oregon transect[J]. Remote Sens. Environ., 1994,47:216～230.
146. Kelley E F, Jones G R, Germer T A. Display reflectance model based on the BRDF[J]. Displays, 1998, 19:27～34.
147. Kirshnan P, Alexander J D, Butler B J, et al. Reflectance Technique for Predicting Soil Organic Matter[J]. Soil Sci. Soc. Am. J, 1980, 44: 1282～1285.
148. Kokaly R F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration[J]. Remote Sens. Environ. 2001, 75: 153～161.
149. Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of features and stepwise multiple linear regression[J]. Remote Sens. Environ. , 1999, 67 : 267～287.
150. Kokaly R, Root R, Brown K, Anderson G L, and Hager S. Calibration of Compact Airborne Sepctrographic Imager (CASI) data to surface reflectance at Theodore Roosevelt National Park. Abstract: 221st Annual Meeting of the American Chemical Society. San Diego, CA. 2001,April 1～5.
151. Kushida K, Yoshino K. A Monte Carlo radiative transfer simulation of rice canopy based on digital stereo photogrammetry[J]. International Archives of Photogrammetry and Remote Sensing, 1996,31(3): 994～998.
152. Kuusk A. A fast, invertible canopy reflectance model[J]. Remote Sens. Environ. 1995A, 51: 342～350.
153. Kuusk A. A Markov chain model of canopy reflectance[J]. Agri. For. Meteoral. 1995B, 76: 221～236.
154. LaCapra V C, Melack J M, Gastil M, et al. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry[J]. Remote Sens. Environ. 1996, 55: 50～58.
155. Ladha J K, Tirol-Padre A, Punzalan G C, et al. Nondestructive estimation of shoot nitrogrn in fifferent rice genotypes[J]. Agron J., 1998, 90: 33～40.
156. Leblon B, Guerif M, La Rocque A. Wind and cloud cover effects on spectral measurements of flooded rice crops in red and near-infrared SPOT-HRV bands[J], Int. j. Remote Sens.,22(7): 1391～1397
157. Lee T, Raja K, Reddy G. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration[J]. Crop Science. 2000, 40:1814～1819.
158. Leone A P & Escadfal R. Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem)[J]. Int. J.Remote Sensing, 2001, 22(12):2311～2328.
159. Li X W, Strahler A. Geometric-optical bi-directional reflectance modeling of a coniferous forest canopy[J]. IEEE Trans. Geosci. Remote Sensing. 1986, GE～24: 906～919.
160. Madeira A C, Mendonca A, Ferreira ME,et al. Relationship between spectroradiometric and chlorophyll measurements in green beans[J]. Communication in Soil Science and Plant Analysis 2000,31(5-6):631～643.
161. Malthus T J, Maderia A C. High Resolution Spectroradiometry: Spectral Reflectance of Field Bean Leaves Infected by Botrytis Fabae[J]. Remote Sens. Environ., 1993, 45 : 107～116.
162. McGwire K, Minor T, Fenstermaker L. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments[J]. Remote Sens. Environ. 2000, 72: 360～374.
163. Meer F V D, Bakker W. Cross correlogram spectral matching: application to surface mineralogical mapping by using AVIRS data from Cuprite, Nevada[J]. Remote Sens. Environ., 1997, 61: 371～382.
164. Miller J R. Season patterns in leaf reflectance red edge characteristics[J]. Int. J.Remote Sens., 1991, 12(7): 1509～1523.
165. Miller J R, Hare E W, Wu J. Quantitative Characterization of the Vegetation Red Edge Reflectance Model[J], Int. J.Remote Sensing, 1990,11(10):1755～1773.
166. Minolta Co. Ltd.. Chlorophyll SPAD-520 instruction manual[M]. Radiometric Instruments Operations. 1998, 17～21.
167. Patel N K, Singh T P, Sahai B, et al. Spectral response of rice crop and its relation to yield and yield attribytes[J]. Int. J. Remote Sens. 1985, 6(3): 657～664.
168. Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance[J]. Photosynthetica, 1995,31 : 221～230.
169. Penuelas J,Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status[J]. Trends in Plant Science. 1998, 3: 151～156
170. Peterson D L, et al. Remote sensing of forest canopy and leaf biochemical contents[J]. Remote Sens. Environ., 1988, 24: 85～108.
171. Pinar A. Grass Chlorophyll and the Reflectance Red Edge[J]. Int.J.Remote Sensing, 1996, 17(2): 351～357.
172. Qi J, Kerr Y H, Moran M S, et al. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region[J]. Remote Sens. Environ. 2000, 73: 18～30.
173. Reyes M V. Comparison of Matrix Factorization Algorithms for Band Selection in Hyperspectral Imagery, Proceedings of SPIE. 2000,4049: 288～298
174. Rock B N, Hoshizaki T, Miller J R. Comparison of in Situ and Airborne Spectral Measurements of the Blue Shift Associated with Forest Decline[J]. Remote Sens. Environ., 1988, 24: 109～127.
175. Salisbury J W. Preliminary measurements of leaf spectral reflectance in the 8-14μ m region[J]. Int. J. Remote Sens. 1986, 7(10): 1879～1886.
176. Sandmeier S, Miiller C, Hosgood B, et al. Physical mechanisms in hyperspectral BRDF data of grass and watercress[J]. Remote Sens. Environ. 1998, 64: 222～233.
177. Shao Y, Fan X T, Liu H, et al., Rice monitoring and production estimation using multitemporal RADARSAT[J]. Remote Sens. Environ., 2001,76:310～325.
178. Sharma D K & Sharma D R. Sustainable use of poor quality water with proper scheduling of irrigation and nitrogen levels for a rice crop. Wat. Sci. Tech., 1999,40(2):111～114.
179. Shibayama M, Akiyama T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass[J]. Remote Sens. Environ., 1989, 27: 119-127.
180. Shibayama M, Akiyama T. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements[J]. Remote Sens. Environ., 1991, 36: 45～53.
181. Shibayama M, Takahashi W, Morinaga S, et al. Canopy Water Deficit Detection in Paddy Rice Using a High Resolution Field Spectroradiometer [J]. Remote Sens. Environ., 1993, 45: 117～126.
182. Stimson C, et al. Prediction of the nutrirnt content of botanical fractions from annual legumes by near-infrared reflectance spectroscopy[J]. Grass and Forage Science. 1991, 46: 99～105.
183. Su M R, Yang C M. Estimation of rice growth from reflectance spectra of vegetative cover[J]. Journal of Photogrammetry and Remote Sensing. 1999, 4(3): 34～40.
184. Taira T. Relation between mean air temperature during ripening period of rice and amylographic characteristics or cooking quality[J]. Japan J Crop Sci, 1999, 68(1): 45～49.
185. Takahashi W, Nguyen C V, Kawaguchi S, et al. Statistical models for prediction of dry weight and nitrogen accumulation based on visible and near-infrared hyper-spectral reflectance of rice canopies[J]. Plant Prod. Sci. 2000, 3(4): 377～386.
186. Takebe M, Yoneyama T, Inada K, et al. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status[J]. Plant Soil, 1990, 122: 295～297.
187. Tian Q, Tong Q, Pu R, et al. Spectroscopic determination of wheat water statu using 1650-1850nm spectral absorption teatures[J]. Int. J Remote Sens. 2001, 22(12): 2329-2338.
188. Thenkabail P S, Smith R B & Pauw E D. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics[J]. Remote Sens. Environ., 2000, 71(2):158～182.
189. Thomas J R, Gausman H W. Leaf reflectance vs, leaf chlorophyll and carotemoid concentrations for eight crops[J]. Agron J., 1977, 60: 799～802.
190. Treitz P, Howarth P. High Spatial Resolution Remote Sensing Data for Forest Ecosysytems Classitication: An Examination of Spatial Scale[J]. Remote Sens. Environ., 2000, 72(3): 268～289.
191. Tsai F, Philpot W. Derivative analysis of hyperspectral data[J]. Remote Sens. Environ. 1998, 66: 41～51.
192. Vaesen K, Gilliams S, Nachaerts K, et al.. Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice[J]. Field Crops Research. 2001, 69: 13～25.
193. Vane G, Terrestrial imaging spectrometry: current status, future trends[J]. Remote Sens. Enviro., 1993, 44(2): 109～127.
194. Wessman C A, et al. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems[J]. Nature. 1988, 335: 154～156.
195. Wessman C A. An evaluation of imaging spectrometry for estimating forest canopy chemistry[J]. Int. J. Remote Sens., 1989, 10(6): 1293-1316.
196. White J D, Trotter C M, Brown L J, ect.. Nitrogen concentration in New Zealand vegetation foliage derived from laboratory and field spectrometry[J]. Int. J. Remote Sensing, 2000, 21(12): 2525-2531.
197. Rice Quality Workshop. Concepts of rice quality. Rice Quality Workshop, 2003.
198. Wu J G, Shi C H, Zhang X M. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy[J]. Field Crops Research. 2002, 75: 1～7.
199. Xiamg Y Q, Liu W D, Wu C S, et al. Study of crop type with hyperspectral technique[J]. SPIE, 1998, 3502: 124～128.
200. Xue L H, Cao W X, Luo W H, Jiang D. Diagnosis of nitrogen status in rice leaves with canopy spectral reflectance[J]. Agricultural Sciences in China. 2003, 2(3): 250～257.
201. Yadav B K, Jindal V K. Monitoring milling quality of by image analysis[J]. Computers and electronics in agriculture. 2001, 33: 19～33.
202. Yang C M, Su M R. Analysis of reflectance spectrum of rice canopy[J]. Chinese Journal of Agrometeorology. 1997, 4: 87-95.
203. Yang C M, Su M R. Seasonal variations of reflectance spectrum and
vegetation index in rice vegetation cover. In: Proceeding of the 3rd Asian Crop Science Conference. April 27-May 2, 1998. Chinese Society of Agronomy. Taichung, Taiwan, 1998: 574～593.
204. Yang C M, Su M R. Modeling rice growth from characteristics of Reflectance spectra[A]. GISdevelopment, ACRS, 1999, Poster 1.
205. Yang C M, Yang C M, Galloway JN, et al. Estimation of leaf nitrogen content from spectral characteristics of rice canopy[J]. The Scientific World. 2001, 387: 81～89.
206. Yoder B J, Pettigrew-Crosby R E. Predicting nitrogen and chlorophyll content and concentration from reflectance spectra (400-2500nm) at leaf and canopy scales. Remote Sens. Environ., 1995, 53: 199～211.
207. Zarco-Tejada P J, Miller J R, Noland T L, et al.. Scaling -up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data[J]. IEEE Transac. Geoscience. Remote Sensing, 2001, 39(7): 1491～1507.
208. Yoshio I. Remote sensing of physiological parameters for monitoring and predicting rice growth and yield[J]. Remote Sens. Environ., 1997, 63: 241～249.
209. Zhou Q F, Shen Z Q, Wang R C. Fourier transform infrared spectral difference of leaf tips in rice related to nitrogen fertilizer rates[J]. Acta Botanica Sinica. 2002, 44(5): 547～550.
210. Zhou Q F, Wang J H. Comparison of adaxial and abaxial surface reflectance under different nitrogen level[J]. Transactions of the CASE, 2002, 18(5): 34～38.
2．浦瑞良，宫鹏．高光谱遥感及其应用[M]．北京，高等教育出版社，2000：3～35．
3．湖南省优质稻生产技术体系及其应用理论研究协作组．施肥对稻米品质和产量影响的研究[J]．湖南农学院学报，1989，15(3)：1～5．
4．江丕栋译．生物聚合物波谱学导论(M)．科学出版社，北京，1983：21～95．
5．沈掌泉，王人潮．连续型光谱数据的处理及信息提取试验[J]．浙江农业大学学报，1993(增)，19：83～88．
6．潘晓华，李木英，曹黎明等．水稻发育胚乳中淀粉的积累及淀粉合成的酶活性变化[J]．江西农业大学学报，1999，21(4)：456～462．
7．郑兰芬，王晋年．成像光谱遥感技术及其图像光谱信息提取分析研究[J]．环境遥感，1992，7(1)：49～58
8．郑兰芬，童庆禧，王晋年．高光谱分辨率遥感研究进展[M]．遥感科学进展，科学出版社，1995，42～50．
9．郑九如，林文彬，林荣光等．改良稻米品质提高稻米经济价值[J]．福建稻米科技，1992，10(1)：4～6
10．曾琪明，马洪兵，张涛．水稻微波后向散射系数的模拟分析[J] 遥感学报，2000，4(1)：14～21．
11．辛淑亮，蔡秋芳．现代农业试验统计．北京，中国计量出版社，1999：270
12．颜龙安，李季能，钟海明等．优质稻米生产技术[M]．北京：中国农业出版社，1999，30～37．
13．方红亮，田庆久．高光谱遥感在植被监测中的研究综述[J]．遥感技术与应用，1998，13(1)：62～69
14．唐延林，王秀珍，黄敬峰等．水稻微分光谱和植被指数的作用探讨[J]．农业工程学报，2003A，19(1)：145～150．
15．唐延林，王秀珍，黄敬峰等．棉花高光谱及其红边特征(Ⅰ)[J]．棉花学报，2003B，15(3)：146～150．
16．唐延林，黄敬峰．农业高光谱遥感研究的现状与发展趋势[J]．遥感技术与应用，2001，16(4)：248～251．
17．刘伟东，项月琴，郑兰芬等．高光谱数据与水稻叶面积指数及叶绿素密度
的相关性分析[J]．遥感学报，2000，4(4)：279～283．
18．刘立军，王志琴，桑大志，杨建昌．氮肥运筹对水稻产量及稻米品质的影响[J]．扬州大学学报(农业与生命科学版)，2002，23(3)：46～50．
19．谭倩，赵永超，童庆禧等．植物光谱维特征提取模型[J]．遥感信息，2001．1：14-18．
20．褚圣麟．原子物理学(M)．北京，人民教育出版社，1983：250～284．
21．王晋年，张兵，刘建贵等．以地物识别和分类为目标的高光谱数据挖掘[J]．中国图象图形学报，1999，4(11)：957～964．
22．王春胜，禹秉熙，戴逸松等．高光谱图像数据变换编码压缩方法[J]．遥感学报，2000，4(2)：95～99．
23．王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅰ．不同氮素水平的水稻光谱特征及其敏感波段的选择[J]．浙江农业大学学报，1993A(增刊)：7～14．
24．王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅱ．农学参数与光谱变量的相关分析[J]．浙江农业大学学报，1993B(增刊)：15～22．
25．王人潮，黄敬峰．水稻遥感估产[M]．北京，中国农业出版社．2002：38～79．
26．王纪华，赵春江，郭晓维等．用遥感方法来监测小麦叶片水分含量[J]．华北农学报，2000，15(4)：68～72．
27．王纪华，黄文江，赵春江等．利用光谱反射率估算叶片生化组分和籽粒品质指标研究[J]．遥感学报，2002，6(增)：92～98．
28．王秀珍．水稻生物物理与生物化学参数的光谱遥感估算模型研究(D)．浙江大学博士学位论文，2001：88～90．
29．王秀珍，王人潮，黄敬峰．微分光谱遥感及其在水稻农学参数测定上的应用研究[J]．农业工程学报，2002A，18(1)：9～13．
30．王秀珍，黄敬峰，李云梅等．高光谱数据与水稻农学参数之间的相关分析[J]．浙江大学学报(农业与生命科学版)，2002B，28(3)：283～288．
31．王长耀，牛铮，唐华俊．对地观测技术与精细农业[M]．科学出版社，2001．
32．王绍华，曹卫星，王强盛等．水稻叶色分布特点与氮素营养诊断[J]．中国农业科学，2002，35(12)：1461～1466．
33．王鼎昌译．分子光谱与分子结构(第二卷)——多原子分子的红外光谱与喇曼光谱(M)．科学出版社，北京，1986：499～505．
34．丁元树主编．浙江粮食与吨粮技术[M]．杭州，浙江科学技术出版社，1992．
35．吉海彦 严衍禄．红外光谱图像技术及其在生物学研究中的应用[J]．物理，2000，29(3)：174-177，161．
36．赵春江，黄文江，王纪华等．不同品种、肥水条件下冬小麦光谱红边参数研究[J]．中国农业科学，2002，35(8)：980～987．
37．赵增煜：常用农业科学试验法[M]．北京，农业出版社，1986：2～7．
38．赵华山，姜胶东，吴大诚等．高分子物理学(M)．北京，纺织工业出版社，1994：409～416．
39．戴平安，刘向华．氮磷钾及有机肥不同配施量对水稻品质和产量效应的研究[J]．作物研究，1999，13(3)：26～30．
40．蒋德安主编．植物生理学实验指导[M]．成都，成都科技大学出版社，1999．
41．薛利红，罗卫红，曹卫星等．作物水分和氮素光谱诊断研究进展[J]．遥感学报，2003，7(1)：73～80．
42．南京农业大学主编：土壤农化分析[M]．北京，农业出版社，1996年10月第2版：19～94、232～234、257～265．
43．胡玉才，戴 寰译．对称性与光谱学——振动和电子光谱学导论(M)．高等教育出版社，北京，1988：66～147．
44．李云梅．水稻BRDF模型集成与应用研究．浙江大学博士学位论文，2001．
45．李云梅，王人潮，王秀珍等．水稻冠层二向反射率的模拟及其反演[J]．中国水稻科学，2002A，16(3)：291～294．
46．李云梅，王秀珍，沈掌泉等．水稻叶片反射率模拟[J]．浙江大学学报(农业与生命科学版)，2002B，28(3)：195～198．
47．李林，沙国栋，陆准淮．灌浆结实期温光因子对稻米品质的影响[J]．中国农业气象，1996，5(2)：33～38．
48．李小文．地物的二向性反射和方向谱特征[J]．环境遥感，1989，4(1)：67～72．
49．李小文，汪骏发，王锦地等．多角度与热红外对地遥感[M]．北京，科学出版社，2001．
50．李军，顾德法，李林峰．环境和栽培因子对稻米品质影响的研究进展[J]．上海农业学报，1997，13(1)：94～97．
51．李筱明，刘进明．水稻品种不同季节栽培稻米品质的影响[J]．湖南农业科学，1993，5：16～17．
52．李德成，徐彬彬，石晓日等．模拟酸雨对水稻叶片反射光谱特性影响的初
步研究[J]．环境遥感，1996，11(4)：241～247．
53．杨吉龙，李家存，杨德明．高光谱分辨率遥感在植被监测中的应用综述[J]．世界地质，2001，20(3)：306～312．
54．杨长明，杨林章，韦朝领等．不同品种水稻群体冠层光谱特征比较研究[J]．应用生态学报，2002，13(6)：689～692．
55．夏有龙，蒋观真，周春和等．水稻栽培实用新技术．南京，江苏科学技术出版社，1993．
56．小西教夫，志贺弘行，安责大志等．遥感技术在美味稻米生产中的应用[J]．日本测量学杂志，2000，7：14～21．
57．吴关庭，夏英武．环境与栽培对稻米品质的影响[J]．中国稻米，1994，4：37～39．
58．吴豪翔，王人潮．土壤光谱特征及其定量分析在土壤分类上的应用[J]．土壤学报，1991，28(2)：178～185．
59．吴长山，项月琴，郑兰芬等．利用高光谱数据对作物群体叶绿素密度估算的研究[J]．遥感学报，2000，4(3)：228～232．
60．申广荣，王人潮，李云梅等．水稻群丛结果和辐射传输分析[J]．作物学报，2001 A，27(6)：769～775
61．申广荣，王人潮，李云梅等．水稻多组分双向反射模型的建立[J]．农业工程学报，2001B，17(5)：146～149．
62．申广荣，王人潮．基于神经网络的水稻双向反射模型研究[J]．遥感学报，2002，6(4)：252～258．
63．罗玉坤，施一平，闵 捷等．中国食用优质米品质的分析研究[J]．浙江农业学报，1991，3(2)：55～60．
64．罗玉坤，朱智伟，张伯平等．食用稻米品质的理化指标与食味的相关性研究[J]．中国水稻科学，1997，11(2)：70～76．
65．陆凳槐．农业遥感的应用效益及在我国的发展战略[J]．农业工程学报，1998，14(3)：64～67．
66．陈述彭，童庆禧，郭华东主编．高光谱分辨率遥感信息机理与地物识别[M]．北京，科学出版社，1998．
67．牛铮，陈永华，隋洪智等．叶片化学成分成像光谱遥感探测机理分析[J]．遥感学报，2000，4(2)：125～129．
68．朱旭东，熊振民，罗玉坤等．异季栽培对稻米品质的影响[J]．中国水稻科
学，1993，7(3)：172-174．
69．程方民，钟连进，不同气候生态条件下稻米品质性状的变异及主要影响因子分析[J]．中国水稻科学．2001，15(3)：187-191．
70．白宝璋，汤学军主编．植物生理学测试技术[M]．北京，中国科学技术出版社，1993．
71．徐正进，陈温福，张庄步等．水稻品质性状的品种间差异及其与产量关系的研究[J]．沈阳农业大学学报，1993，24(3)：217～223．
72．徐大勇，金军，杜永，潘启民，朱庆森．江苏省主要高产粳稻品种品质性状分析[J]．江苏农业学报，2002，18(4)：203～207．
73．周永洽．分子结构分析(M)．化学工业出版社，北京，1991年1月第1版：1～12．
74．周瑞庆．肥料种类及营养元素对稻米产量与品质影响的初步研究[J]．作物研究，1988，2(1)：14-17．
75．周培南，冯惟珠，许乃霞，张亚洁，苏祖芳．施氮量和移栽密度对水稻产量及稻米品质的影响[J]．江苏农业研究，2001，22(1)：27～31．
76．周德翼，张嵩午，高如嵩等．稻米综合品质与结实期气象因子的关系研究[J]．西北农业大学学报，1994，22(2)：6～10．
77．鲁如坤．土壤—植物营养学原理[M]．北京：化学工业出版社，1998．281～305．
78．张良培，郑兰芬，童庆禧．利用高光谱对生物变量进行估计[J]．遥感学报，1997A，1(2)：111～114．
79．张良培，李德仁．鄱阳湖地区高光谱遥感数据的定标研究[J]．武汉测绘科技大学学报，1997B，22(1)：35～38．
80．张丰，熊桢，寇宁．高光谱遥感数据用于水稻精细分类研究[J]．武汉理工大学学报，2002，24(10)：36～39．
81．张玉华．稻米直链淀粉含量及其影响因素研究[J]．黑龙江农业科学，2002，3：34～37．
82．孙家柄主编．遥感原理与应用[M]．武汉，武汉大学出版社，2003：32～77．
83．熊振民，朱旭东，罗玉坤等．稻米品质研究的新进展[J]．水稻文摘，1993．12(3)1～6．
84. Aldakheel Y Y, Danson F M. Spectral reflectance of dehydrating leaves: measurements and modeling[J]. Int. J Remote Sens. 1997, 18(17):3683～3690.
85. Anser G P. Biophysical and biochemical sources of variability in canopy reflectance[J]. Remote Sens. Environ., 1998, 64: 234～253.
86. Bach H, Mauser W. Improvements of plant parameter estimations with hyperspectral data compared to multispectral data[J]. SPIE. 1997, 2959: 59～67.
87. Crosta AP, Souza C R de F. Evaluating AVIRIS hyperspectrai remote sensing data for geological mapping in Laterized Terranes, Central Brazil. Proceeding of the Twelfth International Conference and Workshops on Applied Geologic Remote Sensing, Denver, Colorado. 17-19 November, 1997,Volume, 1997, Ⅱ:430～437.
88. Barton F E, Himmelsbach D S, McClung A M, et al., Rice quality by spectroscopic analysis: Precision of three spectral regions[J]. Cereal Chem. 2000,77(5):669～672.
89. Bournan B AM. Crop modeling and remote sensing for yield prediction[J]. Neth.J. Agric.Sci. 1995,43:143～161.
90. Burgan R E. Use of remotely sensed data for fire danger estimation[J]. Earsel Advances in Remote Sensing. 1996, 4(4): 1～8.
91. Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches[J]. Remote Sens. Environ., 1998, 66: 273～285.
92. Blackburn G A. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves[J], Int.J. Remote Sens. 1998, 19(4): 657～675.
93. Blackburn G A, Milton E J. Seasonal variations in the spectral reflectance of deciduous tree canopies[J], Int.J.Remote Sens. 1995, 16(4): 709～720.
94. Blakeney A B, Lewin L G and Reinke R F. Quality Rice for North Asia[A]. A report for the Rural Industries Research and Development Corporation. RIRDC Project No BRE-IA, 2001.
95. Boegh E, Soegaard H, Hanan N,ect. A Remote Sensing Study of the NDVITS Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High-Resolution Satellite Data[J]. Remote Sens. Environ., 1999, 69: 224～240
96. Boochs F, Kupfer G, Dockter. Shape of the red edge as vitality indicator for plants, Internal Journal of Remote Sensing, 1990. 11(12):1741～1753.
97. Bostater C, et al. Temporal measurement and analysis of high resolution spectral signatures of plants and relationships to biophysical characteristics. SPIE, 1991,2585:298～309.
98. Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data[J]. Remote Sens. Environ., 2002, 81: 45～57.
99. Caetano M, Pereira J M C. Analysis of the integrated (overstory/background) hyperspectral response of pine stands[J]. SPIE, 1997, 3222: 26～37.
100. Card D H, Peterson D L, Matson PA,et al. Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy[J]. Remote Sens. Environ., 1988, 26: 123～147.
101. Casanova D, Epema G F, Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LAI[J]. Field Crops Research. 1998, 55: 83～92.
102. Casanova D, Goudriaan J, Bosch A D. Testing the performance of ORYZAl, an explanatory model for rice growth simulation, for Mediterranean conditions[J]. European Journal of Agronomy, 2000, 12:175～189.
103. Ceccato P, Flasse S, Gregoire M J. Designing a spectral indes to estimate vegetation water content from remote sensing data: Part 1 Validation and applications [J]. Remote Sens. Environ. 2002, 82: 198～207.
104. Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N[J]. Soil Sci., 2002, 167(2): 110～116.
105. Choubey V K, Choubey R. Spectral reflectance, growth and chlorophyll relationships for rice crop in a semi-arid region of India. Water resources management. 1999,13: 73～84.
106. Curran P J. Remote sensing of foliar chemistry[J]. Remote Sens. Environ. 1989, 30: 271～278.
107. Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine[J]. Tree Physiology, 1990, 7: 33～48.
108. Curran P J, Dungan J L, Gholz H L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry[J]. Remote sens.Environ. 2001, 76:349～359.
109. Curran P J, Windham W R, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine leaves[J]. Tree Physiology. 1995, 15: 203～206.
110. Daniel A S, 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[J]. Remote Sens. Environ. 2002, 82: 1～12.
111. Danson F M. Red Edge Response to Leaf Area Index[J]. Int.J. Remote Sensing, 1995, 36(1): 45～53.
112. Davies A, Grant A M C. Near infrared spectrocopy for the analysis of specific molecules in food. In Analytical Applications of Spectroscopy(M). Royal Society of Chemistry, London, 1988: 46～51.
113. Dawson T P, Curran P J, Plummer S E. LIBERTY modeling the effects of leaf biochemical concentration on reflectance spectra[J]. Remote Sens. Environ., 1998, 65: 50～60.
114. Demetriades-Shah T H, Steven M D, Clark J A. High Resolution Derivative Spectra in Remote Sensing[J]. Remote Sens.Environ., 1990,33:55～64
115. Dipti S S, Bari M N and Kabir K A. Grain Quality Characteristics of Some Beruin Rice Varieties of Bangladesh[J]. Pakistan Journal of Nutrition, 2003, 2 (4): 242～245.
116. Dungan J, Johnson L, Billow C, et al. High spectral resolution reflectance of Douglas fir grouth under different fertilization treatments: experiment design and treatment effect[J]. Remote Sens. Environ. 1996, 55: 217～228.
117. Eiji K, Yoshio A, Kunihiro T, et al. Seasonal patterns of canopy structure, biochemistry and spectral reflectance in a broad-leaved deciduous Fagus crenata canopy[J]. Forest Ecology and Management, 2002, 167: 233～249.
118. EOSAT. Comparison of Satellites and Sensors. Earth Resources Satellite Systems [M]. Lanham, Marland, USA, 1995.
119. Feng; Y, Miller J R. Vegetation green reflectance at high spectral resolution
as a measure of leaf chlorophyll content[A]. In: Proceedings of the 14th Canadian Symposium on Remote Sensing, Calgary Alberta. 1991, 351～355.
120. Filella D, Pen-uelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status[J]. Int.J.Remote Sens., 1994, 15(7): 1459～1470.
121. Fourty, T, Baret F. On spectral estimates of fresh leaf biochemistry[J]. Int.J. Remote Sens. 1998, 19(7): 1289～1297.
122. Fourty, T, Baret F, Jacquemoud, S. Leaf optical properties with explicit description of its biochemical composition:direct and inverse problems[J]. Remote sens.Environ. 1996, 56:104～117.
123. Gastellu-Etchegorry J P, et al. Assessment of canopy chemistry with AVIRIS case study in the Landes Forest South-West France[J]. Int.J.Remote Sens., 1995, 16(3): 487～501.
124. Gausman H W, Allen W A, Cardenas R. Relation of light reflectance to histological and physical evaluations of cotton leaf maturity[J]. Application of Optics, 1970, 9: 545～552.
125. Gebbing T, Schnyder H. Pre-anthesis reserve utilization for protein and carbohydrate synthesis in grains of wheat[J]. Plant Physiolohy. 1999, 121: 871～878.
126. Gibaert M A, Gandia S, Melia J. Analyses of Spectral-Biophysical Relationships for a Corn Canopy[J]. Remote Sens. Environ. , 1996, 55 : 11～20.
127. Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll[J]. J. Plant Physical. 1996, 148: 494～500.
128. Gitelson AA, Merzlyak M N. Remote estimation of chlorophyll content in higher plant leaves[J]. Int.J.Remote Sens. 1997, 18(12): 2691～2697.
129. Gong P, Pu R L, Miller J R. Correlating leaf area index of ponderosa pine with hyperspectral CASI data[J]. Canadian J. Remote Sens., 1992, 18: 275～282.
130. Gong P, Pu R L, Miller J R. Coniferous forest leaf area index estimation along the Oregon transect using compact airborne spectrographic imager data[J]. P E & R S. 1995, 61(9): 1107～1117.
131. Gong P, Pu R L, Yu B. Conifer species recognition: an esploratory analysis on in situ hyperspectral data[J]. Remote Sens. Environ., 1997, 62: 189～200.
132. Grossman Y L, Ustin S L, Jacquemoud S, et al. Critique stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data[J]. Remote Sens. Environ. 1996, 56: 182～193.
133. Gupta R K, Vijayan D, Prasad T S. New hyperspectral vegetation characterization parameters[J]. Ads. Space Res. 2001A, 28(1): 201～206.
134. Gupta R K, Vijayan D, Prasad T S. Characterization of red-near infrared transition for wheat and chickpea using 3 nm bandwidth data[J]. Adv. Space Res. 2001B, 28(1): 189～194.
135. Himmel J W, Sudduth K A, Hollinger S E. Soil moisture and organic matter perdiction of surface and subsurface soils using an NIR soil sensor[J]. Computers and Electronics in Agriculture. 2001, 32: 149～165.
136. Horig B, Kuhn F, Oschutz F, et al. HyMap hyperspectral remote sensing to detect hydrocarbons[J]. Int. J. Remote Sens. 2001, 22(8): 1413～1422.
137. Horler D N H, Dockray M, Barber J. The red edge of plant leaf reflectance. Internal Journal of Remote Sensing, 1983. 4(3): 273～288.
138. Hunt G R. Electromagnetic radiation: the communication link in remote sensing. In: Remote Sensing in Grology. Siegal B, Gillespia A. Wiley New York, 1980,702.
139. Inoue Y, Penuelas J, Nouevllon Y, et al. Hyperspectral reflectance measurements for estimating eco-physiological status of plants[J]. SPIE, 2001, 4151:153～163.
140. Inoue Y, Penuelas J. An AOTE-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. Int. J. Remote Sens. 2001, 22(18): 3883～3888.
141. Irudayaraj J & Yang H. Depth profiling of a heterogeneous food-packaging model using step-scan Fourier transform infrared photoacoustic spectroscopy[J]. Journal of Food Engineering, 2002,55: 25～33.
142. Jacquemoud S, Baret F. PROSPECT: a model of leaf optical properties spectra[J]. Remote Sens. Environ., 1990, 34: 75～91.
143. Jacquemoud S, Ustin S L, Verdebout J, et al., Estimating leaf biochemistry
using the PROSPECT leaf optical properties model[J]. Remote Sens. Environ., 1996, 56: 194～202.
144. Jacquemoud S, Verdebout J, Schmuck G, et al. Investigation of leaf biochemistry by statistics[J]. Remote Sens. Environ., 1995, 54: 180～188.
145. Johnson L F, Hlavaka C A, Peterson D L. Multivariate analysis of AVIRIS aata for canmopy, biochemical estimation along the Oregon transect[J]. Remote Sens. Environ., 1994,47:216～230.
146. Kelley E F, Jones G R, Germer T A. Display reflectance model based on the BRDF[J]. Displays, 1998, 19:27～34.
147. Kirshnan P, Alexander J D, Butler B J, et al. Reflectance Technique for Predicting Soil Organic Matter[J]. Soil Sci. Soc. Am. J, 1980, 44: 1282～1285.
148. Kokaly R F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration[J]. Remote Sens. Environ. 2001, 75: 153～161.
149. Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of features and stepwise multiple linear regression[J]. Remote Sens. Environ. , 1999, 67 : 267～287.
150. Kokaly R, Root R, Brown K, Anderson G L, and Hager S. Calibration of Compact Airborne Sepctrographic Imager (CASI) data to surface reflectance at Theodore Roosevelt National Park. Abstract: 221st Annual Meeting of the American Chemical Society. San Diego, CA. 2001,April 1～5.
151. Kushida K, Yoshino K. A Monte Carlo radiative transfer simulation of rice canopy based on digital stereo photogrammetry[J]. International Archives of Photogrammetry and Remote Sensing, 1996,31(3): 994～998.
152. Kuusk A. A fast, invertible canopy reflectance model[J]. Remote Sens. Environ. 1995A, 51: 342～350.
153. Kuusk A. A Markov chain model of canopy reflectance[J]. Agri. For. Meteoral. 1995B, 76: 221～236.
154. LaCapra V C, Melack J M, Gastil M, et al. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry[J]. Remote Sens. Environ. 1996, 55: 50～58.
155. Ladha J K, Tirol-Padre A, Punzalan G C, et al. Nondestructive estimation of shoot nitrogrn in fifferent rice genotypes[J]. Agron J., 1998, 90: 33～40.
156. Leblon B, Guerif M, La Rocque A. Wind and cloud cover effects on spectral measurements of flooded rice crops in red and near-infrared SPOT-HRV bands[J], Int. j. Remote Sens.,22(7): 1391～1397
157. Lee T, Raja K, Reddy G. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration[J]. Crop Science. 2000, 40:1814～1819.
158. Leone A P & Escadfal R. Statistical analysis of soil colour and spectroradiometric data for hyperspectral remote sensing of soil properties (example in a southern Italy Mediterranean ecosystem)[J]. Int. J.Remote Sensing, 2001, 22(12):2311～2328.
159. Li X W, Strahler A. Geometric-optical bi-directional reflectance modeling of a coniferous forest canopy[J]. IEEE Trans. Geosci. Remote Sensing. 1986, GE～24: 906～919.
160. Madeira A C, Mendonca A, Ferreira ME,et al. Relationship between spectroradiometric and chlorophyll measurements in green beans[J]. Communication in Soil Science and Plant Analysis 2000,31(5-6):631～643.
161. Malthus T J, Maderia A C. High Resolution Spectroradiometry: Spectral Reflectance of Field Bean Leaves Infected by Botrytis Fabae[J]. Remote Sens. Environ., 1993, 45 : 107～116.
162. McGwire K, Minor T, Fenstermaker L. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments[J]. Remote Sens. Environ. 2000, 72: 360～374.
163. Meer F V D, Bakker W. Cross correlogram spectral matching: application to surface mineralogical mapping by using AVIRS data from Cuprite, Nevada[J]. Remote Sens. Environ., 1997, 61: 371～382.
164. Miller J R. Season patterns in leaf reflectance red edge characteristics[J]. Int. J.Remote Sens., 1991, 12(7): 1509～1523.
165. Miller J R, Hare E W, Wu J. Quantitative Characterization of the Vegetation Red Edge Reflectance Model[J], Int. J.Remote Sensing, 1990,11(10):1755～1773.
166. Minolta Co. Ltd.. Chlorophyll SPAD-520 instruction manual[M]. Radiometric Instruments Operations. 1998, 17～21.
167. Patel N K, Singh T P, Sahai B, et al. Spectral response of rice crop and its relation to yield and yield attribytes[J]. Int. J. Remote Sens. 1985, 6(3): 657～664.
168. Penuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance[J]. Photosynthetica, 1995,31 : 221～230.
169. Penuelas J,Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status[J]. Trends in Plant Science. 1998, 3: 151～156
170. Peterson D L, et al. Remote sensing of forest canopy and leaf biochemical contents[J]. Remote Sens. Environ., 1988, 24: 85～108.
171. Pinar A. Grass Chlorophyll and the Reflectance Red Edge[J]. Int.J.Remote Sensing, 1996, 17(2): 351～357.
172. Qi J, Kerr Y H, Moran M S, et al. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region[J]. Remote Sens. Environ. 2000, 73: 18～30.
173. Reyes M V. Comparison of Matrix Factorization Algorithms for Band Selection in Hyperspectral Imagery, Proceedings of SPIE. 2000,4049: 288～298
174. Rock B N, Hoshizaki T, Miller J R. Comparison of in Situ and Airborne Spectral Measurements of the Blue Shift Associated with Forest Decline[J]. Remote Sens. Environ., 1988, 24: 109～127.
175. Salisbury J W. Preliminary measurements of leaf spectral reflectance in the 8-14μ m region[J]. Int. J. Remote Sens. 1986, 7(10): 1879～1886.
176. Sandmeier S, Miiller C, Hosgood B, et al. Physical mechanisms in hyperspectral BRDF data of grass and watercress[J]. Remote Sens. Environ. 1998, 64: 222～233.
177. Shao Y, Fan X T, Liu H, et al., Rice monitoring and production estimation using multitemporal RADARSAT[J]. Remote Sens. Environ., 2001,76:310～325.
178. Sharma D K & Sharma D R. Sustainable use of poor quality water with proper scheduling of irrigation and nitrogen levels for a rice crop. Wat. Sci. Tech., 1999,40(2):111～114.
179. Shibayama M, Akiyama T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass[J]. Remote Sens. Environ., 1989, 27: 119-127.
180. Shibayama M, Akiyama T. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements[J]. Remote Sens. Environ., 1991, 36: 45～53.
181. Shibayama M, Takahashi W, Morinaga S, et al. Canopy Water Deficit Detection in Paddy Rice Using a High Resolution Field Spectroradiometer [J]. Remote Sens. Environ., 1993, 45: 117～126.
182. Stimson C, et al. Prediction of the nutrirnt content of botanical fractions from annual legumes by near-infrared reflectance spectroscopy[J]. Grass and Forage Science. 1991, 46: 99～105.
183. Su M R, Yang C M. Estimation of rice growth from reflectance spectra of vegetative cover[J]. Journal of Photogrammetry and Remote Sensing. 1999, 4(3): 34～40.
184. Taira T. Relation between mean air temperature during ripening period of rice and amylographic characteristics or cooking quality[J]. Japan J Crop Sci, 1999, 68(1): 45～49.
185. Takahashi W, Nguyen C V, Kawaguchi S, et al. Statistical models for prediction of dry weight and nitrogen accumulation based on visible and near-infrared hyper-spectral reflectance of rice canopies[J]. Plant Prod. Sci. 2000, 3(4): 377～386.
186. Takebe M, Yoneyama T, Inada K, et al. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status[J]. Plant Soil, 1990, 122: 295～297.
187. Tian Q, Tong Q, Pu R, et al. Spectroscopic determination of wheat water statu using 1650-1850nm spectral absorption teatures[J]. Int. J Remote Sens. 2001, 22(12): 2329-2338.
188. Thenkabail P S, Smith R B & Pauw E D. Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics[J]. Remote Sens. Environ., 2000, 71(2):158～182.
189. Thomas J R, Gausman H W. Leaf reflectance vs, leaf chlorophyll and carotemoid concentrations for eight crops[J]. Agron J., 1977, 60: 799～802.
190. Treitz P, Howarth P. High Spatial Resolution Remote Sensing Data for Forest Ecosysytems Classitication: An Examination of Spatial Scale[J]. Remote Sens. Environ., 2000, 72(3): 268～289.
191. Tsai F, Philpot W. Derivative analysis of hyperspectral data[J]. Remote Sens. Environ. 1998, 66: 41～51.
192. Vaesen K, Gilliams S, Nachaerts K, et al.. Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice[J]. Field Crops Research. 2001, 69: 13～25.
193. Vane G, Terrestrial imaging spectrometry: current status, future trends[J]. Remote Sens. Enviro., 1993, 44(2): 109～127.
194. Wessman C A, et al. Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems[J]. Nature. 1988, 335: 154～156.
195. Wessman C A. An evaluation of imaging spectrometry for estimating forest canopy chemistry[J]. Int. J. Remote Sens., 1989, 10(6): 1293-1316.
196. White J D, Trotter C M, Brown L J, ect.. Nitrogen concentration in New Zealand vegetation foliage derived from laboratory and field spectrometry[J]. Int. J. Remote Sensing, 2000, 21(12): 2525-2531.
197. Rice Quality Workshop. Concepts of rice quality. Rice Quality Workshop, 2003.
198. Wu J G, Shi C H, Zhang X M. Estimating the amino acid composition in milled rice by near-infrared reflectance spectroscopy[J]. Field Crops Research. 2002, 75: 1～7.
199. Xiamg Y Q, Liu W D, Wu C S, et al. Study of crop type with hyperspectral technique[J]. SPIE, 1998, 3502: 124～128.
200. Xue L H, Cao W X, Luo W H, Jiang D. Diagnosis of nitrogen status in rice leaves with canopy spectral reflectance[J]. Agricultural Sciences in China. 2003, 2(3): 250～257.
201. Yadav B K, Jindal V K. Monitoring milling quality of by image analysis[J]. Computers and electronics in agriculture. 2001, 33: 19～33.
202. Yang C M, Su M R. Analysis of reflectance spectrum of rice canopy[J]. Chinese Journal of Agrometeorology. 1997, 4: 87-95.
203. Yang C M, Su M R. Seasonal variations of reflectance spectrum and
vegetation index in rice vegetation cover. In: Proceeding of the 3rd Asian Crop Science Conference. April 27-May 2, 1998. Chinese Society of Agronomy. Taichung, Taiwan, 1998: 574～593.
204. Yang C M, Su M R. Modeling rice growth from characteristics of Reflectance spectra[A]. GISdevelopment, ACRS, 1999, Poster 1.
205. Yang C M, Yang C M, Galloway JN, et al. Estimation of leaf nitrogen content from spectral characteristics of rice canopy[J]. The Scientific World. 2001, 387: 81～89.
206. Yoder B J, Pettigrew-Crosby R E. Predicting nitrogen and chlorophyll content and concentration from reflectance spectra (400-2500nm) at leaf and canopy scales. Remote Sens. Environ., 1995, 53: 199～211.
207. Zarco-Tejada P J, Miller J R, Noland T L, et al.. Scaling -up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data[J]. IEEE Transac. Geoscience. Remote Sensing, 2001, 39(7): 1491～1507.
208. Yoshio I. Remote sensing of physiological parameters for monitoring and predicting rice growth and yield[J]. Remote Sens. Environ., 1997, 63: 241～249.
209. Zhou Q F, Shen Z Q, Wang R C. Fourier transform infrared spectral difference of leaf tips in rice related to nitrogen fertilizer rates[J]. Acta Botanica Sinica. 2002, 44(5): 547～550.
210. Zhou Q F, Wang J H. Comparison of adaxial and abaxial surface reflectance under different nitrogen level[J]. Transactions of the CASE, 2002, 18(5): 34～38.