新疆棉花主要栽培生理指标的高光谱定量提取与应用研究
|
摘要
多光谱遥感以波长区域不连续的宽波段方式记录地物光谱，它以一个宽波段的单值代表一个相对宽光谱区域的辐射动态。与多光谱遥感相比，高光谱遥感可以将视域中观测到的各种地物以完整的光谱曲线记录下来，获取连续的光谱信息，光谱分辨率一般小于10nm，低于地表物质的诊断性光谱宽度(20-40nm)。其本身的波段数已达几百上千个，加上原始光谱的一阶导数、二阶导数、对数变换及其各种高光谱植被指数等，这并不是简单的数据量的增加，而是信息量的增加，信息量可增加十倍以至数百倍。鉴于高光谱遥感的独特性能，特别是在地表物质的识别和分类，有用信息的提取等方面的优势，使得这一技术在植被的精细分类、农作物的长势监测和农田水肥状况的分析等方面展现了巨大的应用前景。作物栽培生理指标的提取和产量估算的研究成为高光谱遥感技术最精华和核心的部分。
本文系统地研究了棉花主要栽培生理指标和产量与冠层光谱反射率之间的关系，采用两类分析方法，一是通过统计相关分析的方法建立观测光谱数据或由此衍生的植被指数与棉花栽培生理指标和产量之间的关系；二是基于棉花观测光谱特征变量的分析技术。以期①找出一些较适用于棉花栽培生理指标和产量估算的敏感光谱波段和植被指数。②建立高光谱特征变量估测棉花栽培生理指标和产量的模型。
研究内容包括：1)通过三年的不同水、肥和密度水平的棉花田间试验，以人为方式造成棉花长势的等级差异，作为本研究的基础；2)利用野外光谱仪在自然环境下测定不同长势、不同生育期的棉花冠层光谱，分析了棉花光谱在250-3500nm范围内的变化规律和棉花红边参数在各个生育期的变化规律；3)完成了叶绿素、叶片全氮、叶片可溶性糖、叶片及植株含水量、LAI、单株生物量和产量与350-2500nm冠层光谱反射率在各个生育期的统计相关分析，了解了各种生化组分在各个生育期的相关性较好的敏感波段；4)将光谱特征参量与各个生育期的栽培生理指标和产量进行了统计相关分析，挑选通用性的光谱参量，建立了各个生育期的栽培生理指标和产量的遥感模型。
本研究结果有以下几个方面：
1．分析了植被最典型的光谱特征，即红边光谱参数，随生育期的变化规律。红边位置在棉花现蕾以后以营养生长为主阶段，会向长波方向移动，即所谓的“红移”现象，而
当进入开花以后以生殖生长为主的阶段，红边位置会向短波方向移动，出现“蓝移”现
象;红谷置随生育期的变化与红边位置有相似的规律;红边宽度在棉花营养生长期是
减小的，而从开花到吐絮阶段，红边宽度逐步增大。
2.完成了叶绿素、叶片全氮、叶片可溶性糖、叶片及植株含水量、LAI和单株生物
量与350一25O0nm冠层光谱反射率在各个生育期的统计相关分析，分析了各种生化组分
在各个生育期的相关性较好的敏感波段。从棉花盛蕾期至吐絮期各敏感波段中近红外
波段770一85Onm处光谱反射率与LAI在全生育期都显著或极显著正相关;植株含水量
与近红外770一950nm，1000一1300nln波段的光谱反射率在全生育期都有显著正相关特性，
所以近红外波段770一850nln、770一95Onm，1000一130Onln处是棉花LAI和棉株含水量的
指示性波段。
3.将各个生育期的栽培生理指标与冠层光谱特征参量进行了统计相关分析，挑选
通用性光谱参量，建立了各个生育期的生化组分的遥感模型。绿峰(560nm)特征等是很
好的色素组分反演参量，将叶绿素按3种表示方式进行处理，即干重含量、鲜重含量和叶
绿素密度。3种数据与光谱反射率或光谱特征的相关性也不一致。通过比较分析，建立
了基于67Onm吸收谷深度的叶绿素干重含量、叶绿素密度的分段遥感模型，和基于560nm
绿峰反射峰特征参量的叶绿素鲜重含量模型。
从盛蕾期至吐絮期，利用棉花绿峰波段的反射峰高度，能够很好地反演叶片含水
量，冠层光谱的67Onm处吸收谷深度与植株含水量始终都有很好的统计正相关关系，并
建立了基于冠层光谱绿峰波段的反射峰高度和67Onm处吸收谷深度的叶片和植株含水
量遥感反演模型。
对棉花重要的碳氮代谢指标，即叶片全氮含量、叶片可溶性糖含量等进行了研
究，98Onm处弱吸收谷面积是估算叶片含氮量的敏感特征参量，抗大气植被指数VARI
_700是估算叶片可溶性糖的敏感特征参量，并建立了基于98Onm处弱吸收谷面积和抗大
气植被指数vAR工_700的各个生育期的叶片全氮含量、叶片可溶性糖含量的遥感模型。
LA工和生物量是监测作物长势好坏的重要的农学参数。LAI在各个生育期都有稳定
的通用性光谱特征参量，即抗大气植被指数VAR工_700;单株地上部分干生物量与67Onm
处吸收谷的深度在盛蕾期至盛铃期呈极显著的正相关关系。并建立了基于670nln处吸收
谷的深度和抗大气植被指数VARI_700的单株生物量和LAI的遥感反演模型。
4.利用田间冠层光谱反射率和反射率光谱特征参量进行了棉花单位面积产量估算
研究，发现抗大气植被指数VAR工_700能够很好地预测棉花单位面积产量。
5.用高光谱遥感模型对开花结铃期棉株含水量以及LAI的估算效果较佳，其估算
精度80%以上;其次是对叶绿素密度、单株生物量和叶片可溶性糖含量的估算，其估算
精
Multispectral remote sensing notes land spectra by non-continual wide wave band fashion in wave width region, which use one data of wide wave band represents radiation trend of a relative wide spectral region. By comparison with multispectral remote sensing, hyperspectra can completely note everything by a integrated spectral curve in its watching region and can receive continual spectral information, its spectral resolution is commonly less than 10nm and less than 20-40nm of diagnosis wide wave of land. It has several hundreds and thousands wave bands and first-order and second derivative spectra and every vegetation indices and so on. This is not increase in data number but is increase in information. The information capacity is add to several tenfold or hundreds; Because of the special characteristic of hyperspectra, especially identification and classification of land surface and extracting useful information and so on. This technique has tremendous applied foreground. The study on cultivating physiologi
cal characteristics and yield of crop is the most prime and core part of hyperspectra. The main objectives of the study includes:
1) To find out some spectral wave bands and vegetation indices which would be applicable to estimate cultivating physiological characteristics and yield of cotton;
2) The hyperspectral remote sensing models of different cultivating physiological characteristics and yield are built in different growth stages, which are based on the hyperspectral reflectance characteristic parameters.
The main contents of the study are as following:
1) This study is based on the rank difference of the nitrogenous nutrition level and watering level and density by man-made style through three years cotton farm experiment.
2) Canopy spectral of cotton is measured by Analytical Spectral Devices (FieldSpec) in different growth stage. The study has analyzed the variation of cotton canopy reflectance with wavelength, compared the canopy reflectance curves of different nitrogenous nutrition level and watering level, also compared reflectance curves of leaf and cotton canopy. The study has also analyzed the variation of the spectral "red edge" features and meantime designed 46 hyperspectral reflectance characteristic parameters to build the hyperspectral
remote sensing models of different cultivating physiological characteristics and yield in different growth stages.
3) According to the statistical correlation coefficients between spectral reflectance in 350-2500nm range and cultivating physiological characteristics and yield are analyzed in all growth stages, all the sensitive bands for different cultivating physiological characteristics and yield are found.
4) The hyperspectral remote sensing models of different cultivating physiological characteristics and yield are built in different growth stages, which are based on the 46 hyperspectral reflectance characteristic parameters.
The research achievements are as following:
1) The spectral "red edge" features are calculated and analyzed. Red-shift is found with early increase in cotton's budding stage; Blue-shift is found with late decrease in cotton's lint-opened stage. The red edge absorption width decreases in the early growth stages, and increases in the late growth stages.
2) The statistical correlation coefficients between spectral reflectance in 350nm-2500nm range and cultivating physiological characteristics and yield are analyzed in all growth stages, so all the sensitive bands for different cultivating physiological characteristics and yield are found. The statistical correlation coefficients between LAI plant water content and spectral reflectance in near-infrared bands at 770-850nm 770-950nm, 1000-1300nm are significant in all growth stages.
3) The statistical correlation coefficients between 46 hyperspectral reflectance characteristic parameters and cultivating physiological characteristics and yield are analyzed in all growth stages, the hyperspectral remote sensing models of different cultivating physiolog
本文系统地研究了棉花主要栽培生理指标和产量与冠层光谱反射率之间的关系，采用两类分析方法，一是通过统计相关分析的方法建立观测光谱数据或由此衍生的植被指数与棉花栽培生理指标和产量之间的关系；二是基于棉花观测光谱特征变量的分析技术。以期①找出一些较适用于棉花栽培生理指标和产量估算的敏感光谱波段和植被指数。②建立高光谱特征变量估测棉花栽培生理指标和产量的模型。
研究内容包括：1)通过三年的不同水、肥和密度水平的棉花田间试验，以人为方式造成棉花长势的等级差异，作为本研究的基础；2)利用野外光谱仪在自然环境下测定不同长势、不同生育期的棉花冠层光谱，分析了棉花光谱在250-3500nm范围内的变化规律和棉花红边参数在各个生育期的变化规律；3)完成了叶绿素、叶片全氮、叶片可溶性糖、叶片及植株含水量、LAI、单株生物量和产量与350-2500nm冠层光谱反射率在各个生育期的统计相关分析，了解了各种生化组分在各个生育期的相关性较好的敏感波段；4)将光谱特征参量与各个生育期的栽培生理指标和产量进行了统计相关分析，挑选通用性的光谱参量，建立了各个生育期的栽培生理指标和产量的遥感模型。
本研究结果有以下几个方面：
1．分析了植被最典型的光谱特征，即红边光谱参数，随生育期的变化规律。红边位置在棉花现蕾以后以营养生长为主阶段，会向长波方向移动，即所谓的“红移”现象，而
当进入开花以后以生殖生长为主的阶段，红边位置会向短波方向移动，出现“蓝移”现
象;红谷置随生育期的变化与红边位置有相似的规律;红边宽度在棉花营养生长期是
减小的，而从开花到吐絮阶段，红边宽度逐步增大。
2.完成了叶绿素、叶片全氮、叶片可溶性糖、叶片及植株含水量、LAI和单株生物
量与350一25O0nm冠层光谱反射率在各个生育期的统计相关分析，分析了各种生化组分
在各个生育期的相关性较好的敏感波段。从棉花盛蕾期至吐絮期各敏感波段中近红外
波段770一85Onm处光谱反射率与LAI在全生育期都显著或极显著正相关;植株含水量
与近红外770一950nm，1000一1300nln波段的光谱反射率在全生育期都有显著正相关特性，
所以近红外波段770一850nln、770一95Onm，1000一130Onln处是棉花LAI和棉株含水量的
指示性波段。
3.将各个生育期的栽培生理指标与冠层光谱特征参量进行了统计相关分析，挑选
通用性光谱参量，建立了各个生育期的生化组分的遥感模型。绿峰(560nm)特征等是很
好的色素组分反演参量，将叶绿素按3种表示方式进行处理，即干重含量、鲜重含量和叶
绿素密度。3种数据与光谱反射率或光谱特征的相关性也不一致。通过比较分析，建立
了基于67Onm吸收谷深度的叶绿素干重含量、叶绿素密度的分段遥感模型，和基于560nm
绿峰反射峰特征参量的叶绿素鲜重含量模型。
从盛蕾期至吐絮期，利用棉花绿峰波段的反射峰高度，能够很好地反演叶片含水
量，冠层光谱的67Onm处吸收谷深度与植株含水量始终都有很好的统计正相关关系，并
建立了基于冠层光谱绿峰波段的反射峰高度和67Onm处吸收谷深度的叶片和植株含水
量遥感反演模型。
对棉花重要的碳氮代谢指标，即叶片全氮含量、叶片可溶性糖含量等进行了研
究，98Onm处弱吸收谷面积是估算叶片含氮量的敏感特征参量，抗大气植被指数VARI
_700是估算叶片可溶性糖的敏感特征参量，并建立了基于98Onm处弱吸收谷面积和抗大
气植被指数vAR工_700的各个生育期的叶片全氮含量、叶片可溶性糖含量的遥感模型。
LA工和生物量是监测作物长势好坏的重要的农学参数。LAI在各个生育期都有稳定
的通用性光谱特征参量，即抗大气植被指数VAR工_700;单株地上部分干生物量与67Onm
处吸收谷的深度在盛蕾期至盛铃期呈极显著的正相关关系。并建立了基于670nln处吸收
谷的深度和抗大气植被指数VARI_700的单株生物量和LAI的遥感反演模型。
4.利用田间冠层光谱反射率和反射率光谱特征参量进行了棉花单位面积产量估算
研究，发现抗大气植被指数VAR工_700能够很好地预测棉花单位面积产量。
5.用高光谱遥感模型对开花结铃期棉株含水量以及LAI的估算效果较佳，其估算
精度80%以上;其次是对叶绿素密度、单株生物量和叶片可溶性糖含量的估算，其估算
精
Multispectral remote sensing notes land spectra by non-continual wide wave band fashion in wave width region, which use one data of wide wave band represents radiation trend of a relative wide spectral region. By comparison with multispectral remote sensing, hyperspectra can completely note everything by a integrated spectral curve in its watching region and can receive continual spectral information, its spectral resolution is commonly less than 10nm and less than 20-40nm of diagnosis wide wave of land. It has several hundreds and thousands wave bands and first-order and second derivative spectra and every vegetation indices and so on. This is not increase in data number but is increase in information. The information capacity is add to several tenfold or hundreds; Because of the special characteristic of hyperspectra, especially identification and classification of land surface and extracting useful information and so on. This technique has tremendous applied foreground. The study on cultivating physiologi
cal characteristics and yield of crop is the most prime and core part of hyperspectra. The main objectives of the study includes:
1) To find out some spectral wave bands and vegetation indices which would be applicable to estimate cultivating physiological characteristics and yield of cotton;
2) The hyperspectral remote sensing models of different cultivating physiological characteristics and yield are built in different growth stages, which are based on the hyperspectral reflectance characteristic parameters.
The main contents of the study are as following:
1) This study is based on the rank difference of the nitrogenous nutrition level and watering level and density by man-made style through three years cotton farm experiment.
2) Canopy spectral of cotton is measured by Analytical Spectral Devices (FieldSpec) in different growth stage. The study has analyzed the variation of cotton canopy reflectance with wavelength, compared the canopy reflectance curves of different nitrogenous nutrition level and watering level, also compared reflectance curves of leaf and cotton canopy. The study has also analyzed the variation of the spectral "red edge" features and meantime designed 46 hyperspectral reflectance characteristic parameters to build the hyperspectral
remote sensing models of different cultivating physiological characteristics and yield in different growth stages.
3) According to the statistical correlation coefficients between spectral reflectance in 350-2500nm range and cultivating physiological characteristics and yield are analyzed in all growth stages, all the sensitive bands for different cultivating physiological characteristics and yield are found.
4) The hyperspectral remote sensing models of different cultivating physiological characteristics and yield are built in different growth stages, which are based on the 46 hyperspectral reflectance characteristic parameters.
The research achievements are as following:
1) The spectral "red edge" features are calculated and analyzed. Red-shift is found with early increase in cotton's budding stage; Blue-shift is found with late decrease in cotton's lint-opened stage. The red edge absorption width decreases in the early growth stages, and increases in the late growth stages.
2) The statistical correlation coefficients between spectral reflectance in 350nm-2500nm range and cultivating physiological characteristics and yield are analyzed in all growth stages, so all the sensitive bands for different cultivating physiological characteristics and yield are found. The statistical correlation coefficients between LAI plant water content and spectral reflectance in near-infrared bands at 770-850nm 770-950nm, 1000-1300nm are significant in all growth stages.
3) The statistical correlation coefficients between 46 hyperspectral reflectance characteristic parameters and cultivating physiological characteristics and yield are analyzed in all growth stages, the hyperspectral remote sensing models of different cultivating physiolog
引文
1 白宝璋．汤学军主编，《植物生理学测试技术》，中国科学技术出版社：1993年8月出版．
2 陈述彭，赵英时．遥感地学分析，北京，测绘出版杜．1990．
3 陈述彭，童庆禧，郭华东．高光谱分辨率遥感信息机理与地物识别．遥感倍息机理研究，北京：科学出版社，1998，139-231．
4 蒋亨显，王珂，王人潮．建立大田晚稻农学光谱估产模式研究初报．浙江农业大学学报．1993．增刊：73—77．
5 陈沈斌主编，小麦、玉米和水稻遥感估产技术试验研究文集．北京：中国科学技术出版社．1993，158～162．
6 刘伟东，项月琴．郑兰芬．童庆椿．吴长山．高光谱数据与水稻叶面积及叶绿素密度的相关分析，遥感学报．2000．4(4)：279～283．
7 牛铮，陈永华，隋洪志，张庆员，赵春江．叶片化学组分成像光谱遥感探测机理分析：遥感学报，2000，4(2)：125～130．
8 浦瑞良，宫鹏．高光谱遥感及其应用．北京：高等教育出版社，2000．
9 沈掌泉，王人潮，王珂．不同早稻品种的光谱特性及其与农学参数的相关性．浙江农业大学学报．1993，增刊：31～35．
10 沈掌泉，王人潮．杂交稻与常规稻的光谱特性差异初步分析．浙江农业大学学报．1993．增刊：36—39．
11 沈掌泉，王玛，王人潮．水稻冠层光谱反射特性的动态变化研究．国土资源遥感．1996，(4)：40～44．
12 王坷，蒋亨显，王人潮．建立大田早稻农学光谱估产模式研究初报．浙江农业大学学报，1993．增刊：66～72．
13 王人潮，王珂，沈掌泉，蒋亨显．水稻单产遥感估测建模研究．遥感学报．1998．2(2)：119～124．
14 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．浙江农业大学学报，1993，增刊：7～14．
15 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．浙江农业大学学报，1993，增刊：15～22．
16 王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅲ，从产量结构分析建立单产农学光谱试验摸式．浙江农业大学学报，1993．增刊：23～29．
17 王人潮，王深法，陈铭臻，蒋亨显．水稻遥感估产系统研究_——预试验和前期研究结果以及对开展估产研究的讨论．浙江农业大学学报．1993，增刊：1～6．
18 王人潮，黄敬峰．水稻遥感估产，农业出版社，2001．
19 王延颐，高庆芳．稻田光谱与水稻长势及产量结构要素关系的研究．国土资源遥，1996．(1)：51～55．
20 王延颐，植被指数与水稻长势及产量结构要素关系的研究．国土资源遥感1996．(1)：
50～59．
21 王乃斌，中国小麦遥感动态监测与估产，北京：中国科学技术出版社，1996．
22 王晋年，张兵，刘建贵，童庆禧，郑兰芬．以地物识别和分类为目标的高光谱数据挖掘．中国图象图形学报，1999，4(11)
23 吴长山，项月琴，郑兰芬，童庆禧．利用高光谱数据对作物群体叶绿素密度估算的研究．遥感学报，2000，4(3)：228-231．
24 张良培，郑兰芬，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．
25 郑兰芬，王晋年，成像光谱遥感技术及其成像光谱信息提取分析研究．环境遥感．1992，7(1)：49-58，
26 陈述彭，童庆禧，郭华东，遥感信息机理研究，1998，北京：科学出版社．
27 李郁竹主编，冬小麦气象卫星遥感动态监测与估产，1993，北京气象出版社
28 刘爱民等，现代精准农业及我国精准农业的发展方向，中国农业大学学报．2000，5(2)：20-25．
29 陆登槐等编著，遥感技术在农业工程中的应用，北京：清华大学出版让，1997．
30 童庆禧，郑兰芬，王晋年，湿地植被成像光谱遥感研究，遥感学报，1997，1(1)：50-58
31 童庆禧，成像光谱技术发展初探，成像光谱仪技术与应用．1993，1-9
32 王纪华，赵春江，郭晓维等，用光谱反射率诊断小麦叶片水分状况的研究，中国农业科学，2001，34(Ⅰ)：104-107
33 汪懋华，“精细农业”研究与工程科技创新，农业工程学报，1999，15， (1)：1-8
34 严泰来等，精准农业的由来与发展及其在我国的应用策略，计算机与农业，2000，(1)：3-5
35 杨敏华，面向精准农业的高光谱遥感作物信息获取，中国农业大学博士学位论文，2002年
36 陈怀亮，毛留喜，冯定原．遥感监测土壤水分的理论、方法及研究进展．遥感技术与应用，1999，14(2)：55—65．
37 陈迅．利用遥感技术进行农作物估产．计算机应用研究，2001：114—116
38 丁圣彦．精确农业的技术体系与应用研究进展．农业现代化研究，2002，23(3)：222—225
39 杜青林，加快农业遥感技术应用推动农业和农村经济发展．中国农业资源与区划，2002，23(3)：1—2
40 方红亮，田庆久，高光谱遥感在植被监测中的研究综述．遥感技术与应用．1998，13(1)：62—69
41 洪佳华，孙睿，陶英丽．夏玉米叶片光谱吸收率初探．中国农业气象，1998，19(4)：4．
42 李剑萍．气象卫星作物长势监测及产量预报系统．气象科技，2002，30(2)：108—111
43 刘良云．高光谱遥感在精准农业中的应用研究，中国科学院博士后出站报告，2002，12
44 刘伟东，高光谱技术在水稻生长监测与作物识别中的应用研究．中国农业大学硕士论文．2000
45 梅安新，彭望禄，秦其明，刘慧平，遥感导论．高等教育出版社，2001年7月．
46 谭凯琰，李郁竹，遥感植被指数与冬小麦绿叶面积系数和麦土比的关系．遥感信息．1998(1)：18—21．
47 唐延林，王人潮，遥感技术在精准农业中的应用．现代化农业，2002，2：33—35
48 田庆久，宫鹏，赵春江，郭晓维，用光谱反射率诊断小麦水分状况的可行性分析．科学通报．2000，45(24)：2645—2650
49 王纪华 赵春江，郭晓维，田庆久，用光谱反射率诊断小麦叶片水分状况的研究．中国农业科学，2001，34(1)：104—107
50 王纪华等，利用遥感方法诊断小麦叶片含水量的研究．华北农学报，2000，15(4)：68
51 王茂新，裴志远，吴全等，应用NOAA图像监测冬小麦面积的研究．农业工程学报，1998，14(3)：84—88
52 王人潮等，论中国农业遥感与信息技术发展战略．科技通报，1999，15(Ⅰ)：1—7．
53 王思远等，遥感与GIS支持下的中国森林植被动态变化分析，资源学报，2002，24(5)：64—69．
54 吴炳方，全国农情监测与估产的运行化遥感方法，地理学报，2000，55(1)：25—35
55 杨邦杰，裴志远，作物长势的定义与遥感监测．农业工程学报，1999，15(3)：214—218
56 杨邦杰，裴志远等，我国农情遥感监测关键技术研究进展．农业工程学报，2002，18(3)：191—194
57 张建华，作物估产的遥感一数值模拟方法．干旱区资源与环境，2000，14(2)：82—86．
58 张良培，郑兰芬，童庆禧，利用高光谱数据对生物变量进行估计．遥感学报，1997，1(2)：110—113．
59 杨相恒等，氮、磷交互作用下小麦氮素营养丰缺的光谱诊断与估测．遥感信息，1992．
60 童庆禧等，湿地植被成象光谱遥感研究．遥感学报， 1997，1(1)．
61 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．遥感学报，1997，1(2)．
62 浦瑞良、宫鹏等，美国西部黄松叶面积指数与高光谱分辨CASI数据的相关分析．环境遥感，1993，8(2)．
63 高惠璇．实用统计方法与SAS系统[M]．北京：北京大学出版社，2001．
64 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)：228～231．[21]
65 刘伟东，项月琴，郑兰芬等．高光谱数据与水稻叶面积及叶绿素密度的相关分析[J]．遥感学报，2000，4(4)：279～283．
66 张福锁等，环境胁迫与植物营养，1993，北京：中国农业大学出版社．
67 张霞等，利用高光谱遥感图像估算小麦氮含量，遥感学报，2003，Vol，7，No，3．
68 王秀珍等，水稻地上鲜生物量的高光谱遥感估算模型研究，作物学报，2003，Vol，29，No，6．
69 王秀珍等，水稻生物化学参数与高光谱遥感特征参数的相关分析，农业工程学报2003，Vol，19，No，2．
70 唐延林等，水稻、玉米、棉花的高光谱及其红边特征比较，中国农业科学，2004，
37(1)：29-35
71 王登伟等，棉花主要栽培生理参数的高光谱估测研究研究，中国农业科学，2003，36(7)
1. Anatoly A., Gitelson Y, Yoram J K, Robert S, et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing Environ.,2002.80:76-87.
2. Asner G. P. 1998,Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensinge of Environment, 64, 234-253.
3. Andrieu B; Sohbi Y. A, direct method to measure bi-directional gap fraction in vegetation canopies. Remote Sensing of Environment, 1994. 50(1): 61-66.
4. Asner G .P, Wessman C A and Privette J L, 2000,Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of add ecosystems. Remote Sensing of Environment, 41, 133-142
5. Ammenberg P.,Flink P and Lindell T.,.et al. Bio-optical modeling combined with remote Sensing to assess water quality. Int J. Remote Sensing. 2002.23 (8): 1621-1638
6. Bieh L L., and B F Robinson, 1993, Data acquisition and preprocessing techniques for remote Sensing field research, Proc. Soc. Photo-Optical Inst-r. Engr, 356:143—149.
7. Baret,F., Vanderbilt,V C, Steven,M.D., and Jacquemoud,S.,1994,Use of spectral analogy to evaluate canopy reflectance Sensingivity to leaf optical properties. Remote Sensing of Environment, 48,253-260
8. Begue A, 1993,Leaf area index,interepted photosynthetically active radiation and spectral vegetation indices:A Sensingitivity analysis for regular-clumped canopies Remote Sensing of Environment 46:45-59
9. Boochs, F..1990,Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing, Vol 11,No.10,1741—175
10. Bausch W C., 1993,Soil Background Effect on Reflectance -based Crop Coefficients for Corn. Remote Sensing of Environment, 46:213-222
11. Bartlett, D.S. et al, 1990,Use of vegetation indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy, Remote Sensing. Environ. 30: 115—128.
12. Blackbum, G.A.,and Milton,E.J.,1995,Seasonal variations in the spectral reflectance of deciduous tree can canopies. International Journal of Remote Sensing, 16:709-721.
13. Blackburn G A., 1998,Quantifying chlorophylls and carotenoids at leaf and canopy scales :An evaluation of some hyperspectral approaches.Remote Sensing of Environment, 66:273-285.
14. Blackburn G. A.,1998,Spectral indices for estimating photo-synthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing,19: 657-675.
15. Boochs F,Kupfer, G., Duckter, K., and Kuhbauch, W., 1990,Shape of the red edge as a
vitality indicator for plants. International Journal of Remote Sensing, 11,1741-1753
16. Bouman B A M., 1992, Linking physical remote Sensing models with crop growth simulation models, applied for sugar beet, International Journal of Remote Sensing, 13:2565-2581
17. Ball S T.; Frazier, B. E. Evaluating the association between wheat yield and remotelysensing data. Cereal Research Communications, 1993.21 (2/3): 213-219.
18. Bellairs, S. M.; Turner, N. C.; et al. Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers. Australian Journal of Agricultural Research, 1996.47(7): 1017-1034.
19. Blackmer, T. M.; Schepers, J. S.; et al. Analysis of aerial photography for nitrogen, Agronomy Journal, 1996. 88(5): 729-733.
20. Blackmer, T. M.; et al. Remote sensing to detect nitrogen deficiency in corn. American society of Agronomy, 1995.505-512. ISBNO-89118-127-X.
21. Boken. Shaykewich Improving an operational wheat yield model using phonological phase-based Normalized Difference Vegetation Index, Int. J. Remote Sensing. 23.20(2002) :4155-4168.
22. Broge N.H. and Motensen J.V.,Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sensing Environment.,2002,81:45-57.
23. Broge N.H. et al. Comparing prediction power and stability of broadband and hyper spectral vegetation indices for estimation of green leaves area index and canopy chlorophyll density, Remote Sensing Environment.,2001,76:156-172.
24. Curran,P.J., Dungan,J.L., Macler, B.A., and Peterson,D.L.,1992,Reflectance of fresh whole leaves for the estimation of chemical concentration, Remote Sensing of Environment,39:153-156
25. Curran,P.J, Dungan,J.L, Macler, B.A, and Peterson,S.E.,1991,The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment, 35:69-76
26. Curran,P.J.,Windham, W.R.,and Gholz,H.L.,1995,Exploring the relationship between reflectance red edge and chlorophyll content in slash pine, Tree physiology, 15,203-206
27. Compton J. Tucker. Remote sensing of leaf water content in the near Infrared [J]. Remote Sensing of Environment, 1980,10:23-32.
28. Cibula, W.G., Zetka, E.F., and Rickman, D.L Response of Thematic Mapper Bands to Plant Water Stress[J].International Journal of Remote Sensing, 1992,13:1869-1880.
29. Collins, W., Remote sensing of crop type and maturity, Photogrammetric Engineering and Remote Sensing, Vol 44,43—55,1978.
30. Card,D.H., Peterson,D.L., Matson,P.A., and Aber, J.D., 1988,Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy, Remote Sensing of
Environment,26:123-147
31. Chappelie,E.W., Kim,M.S., and Mcmurtrey, J.E.Ⅲ,1992,Ration analysis of reflectance spectra(RARS):An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b and carotenoids in soybean leaves. Remote Sensing of Environment, 39:239-247
32. Clevers,J.G. P.W.,1988,The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25:53 -69
33. Cloutis,E.A., Hyperspectral geological remote Sensing: evaluation of analytical techniques. Int .J. Remote Sensing. 1996,17(12):2215-2242
34. Curran,P.J., 1989,Remote Sensing of foliar chemistry. Remote Sensing of Environment, 30:271-278
35. Clevers, J.G.P.W. 1997, A simplified approach for yield prediction of sugar beet lased on optical remote Sensing data, Remote Sensing. Environ.2001:221—228.
36. Curran,P.J., Dungan,J.L., and Cholz, H.L., 1990,Exploring the relationship between reflectance red edge and chlorophyll content in slash pine Tree physiology,7,33-48
37. Casanova. D.; Epema. G. F., Monitoring rice reflectance at field level for estimating biomass and LAI. Field crops Research, 1998.55(1/2): 83-92.
38. Chosh, R.; et al. Linear polarization measurements of a wheat canopy. International Journal of Remote Sensing, 1993.14(13): 2501-2508.
39. Dawsan,F.W., Steven,M.D., Malthus,T.J. ,and Clark,J.A.,1992,High spectral resolution data for determining leaf water content. International Journal of Remote Sensing, 13:461-470.
40. Dawsan,T.P., Curran,P.J., and Plummer, S.E.,1998,Liberty-modelling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment, 65:50-60
41. Dungan,J., Johnson,L., and Vanderbilt,V.,1996,High spectral reflectance of douglas fir grown under different fertilization treatments: experiment design and treatment effects, Remote Sensing of Environment,55:217-228
42. Demetriades,et al, High Resolution Derivative Spectra in Remote Sensing, Remote Sensing of Environment, Vol 33:55—64,1990..
43. Danson, F. M. Red Edge Response to leaf Area Index, International Journal of Remote Sensing, Vol 16,183—188,1995.
44. Das,D.K.et al, 1993, Assessing growth and yield of wheat using remotely-sensinged canopy temperature and spectral indices. International Journal Remote Sensing 14:3081—3092.
45. Daughtry, C.S.T, 1992, Spectral estimates of absorbed radiation and photomas production in corn and soybean canopies. Remote Sensinge Environment 39:141—152.
46. Daughtry, C.S.T. 1990. Direct measurements of canopy structure. Remote Sensing Rev,
5:45—60.
47. Datt B., Visible/near fared reflectance and chlorophyll content in Eucalyptus leaves, Int J. Remote Sensing., 1999,20(14):27412459.
48. Elvidge, C.D.,Chen,Z.K.,and Groeneveld,D,D.P.,1993,Detection of trace quantities of green vegetation 1990 AVIRIS data. Remote Sensing of Environment, 44,271-297.
49. Elvidge, C.D.,Chen,Z.K.,and Groeneveld,D,D.P.,1993,Detection of trace quantities of green vegetation 1990 AVIRIS data .Remote Sensing of Environment ,44,271-297.
50. Elvidge,C.D, and Chen Z., 1995, Comparison of broadband and narrow-band red and near-infared vegetation indices. Remote Sensing of Environment,,54,38-48.
51. Enumal et al., Spectral blue shift of red edge monitors damage class of beech trees, Remote Sensing. Envien., 1992,39.81-84.
52. Filella, I., and Penuelas,J., 1994,The red edge position and shape as indicators plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15,1459-1470.
53. Filella, I. L. Serrano. J. et al, 1995, Evaluating wheat N status with canopy reflectance indices and discriminated analysis, Crop Science, 35:1400—1405.
54. Ferencz, C.; Tarcsai, G.; et al. Correction of atmospheric effects of satellite remote Sensing data (Land sat MSS-NOAA AVHRR) for surface canopy investigations. International Journal of Remote Sensing, 1993.14(18): 3417-3431.
55. Fernandez, S.; et al. Radiometric characteristics of tritium aestivum c v. Astral under water and nitrogen stress. International Journal of Remote Sensing, 1994.15(9): 1867-1884.
56. Goetz,A.F.H., Vane,G.,Solomon,J.E., and Rock B.N.,1985, Imaging spectrometry for earth remote Sensing.Science,228,1147-1153.
57. Gong,p.,PU,R.,and Miller, J.R.,1995,Coniferous forest leaf index estimation along the oregon transact using compact airborne spectrographic imager data. Photogrammetric Engineering and Remote Sensing, 61,1107-1117.
58. Grossman, Y.L., Ustin, S.L., Jacquemod, S., Sanderson, E.W., Schmuck, G., and Verdebout,J.,1996, Critique of stepwise linear regression for the extraction of leaf biochemistry information form leaf reflectance data. Remote Sensing of Environment, 56,182-193.
59. Gregory A. Carter. Primary and Secondary Effects of Water Content on the Spectral Reflectance of Leaves [J]. American Journal of Botany, 1991,78(7): 916-924.
60. Gilabert, M.A.et al. Analysis of spectral-biophysical Relationships for a Corn Canopy, Remote Sensing of Environment, Vol 55,11—20,1996.
61. Gao B.C., NDWI: a normalized difference water index for remote Sensing vegetation liquid water from space, Remote Sensing. Environ., 1996.58:257-266.
62. Green, C.F.,1987, Nitrogen nutrition and wheat growth in relation to absorbed solar radiation, Agric. For. Meteorol. 41:207—248.
63. Gallo,K .P. et al, 1985, Spectral estimation of absorbed photo synthetically active radiation in corn canopies, Remote Sensing. Environ, 17:221—232.
64. Groward, S.N. et al, 1992, Vegetation canopy PAR absorptance and the normalized difference vegetation Index: An assessment using the sail model. Remote Sensing. Environ, 39:131—140.
65. Horler, D. N. H. et al.1983C, The red edge of plant leaf reflectance, International Journal of Remote Sensing, 4,273—288.
66. Holben, B.N Schutt, J.B and Mcmurtrey J., Leaf Water Stress Detection Utilizing Thematic mapper bands 3,4 and 5 in Soybean Plants [J], International Journal of Remote Sensing, 1983,4:289-297.
67. Hunt G R. Electromagnetic radiation: The communication link in remote Sensing. In: Remote Sensing in geology. Siegel, Gillespie A. Wiley New York, 1980,702.
68. Hunt, E.R.,Jr., Ronk, B.N., and Nobel, P.S., 1987, Measurement of leaf relative water content by infrared reflectance, Remote Sensing Environ, 22:429—435.
69. Hunt, E.R., Jr., and Rock, B.N, 1990, Detection of changes in leaf water content using near-and middle-infrared reflectance, Remote Sensing. Environ.
70. Hope, A.S.,1988,Estimation of wheat canopy resistance using combined remotely sensing spectral reflectance and thermal observations, Remote Sensing. Environ, 24:369—383.
71. Inoue, Y., Morinaga, S.,Shibayama,M.. Non-destructive Estimation of Water Status of Intact Crop Leaves Based on Spectral Reflectance Measurements [J]. Japanese Journal of Crop Science, 1993,62:462-469.
72. Jackson, R.D., Moran, M.S., Slater R.N., and Bigger, S.F., 1987, Field calibration of reflectance panels. Remote Sensing of Environment, 22,145-158.
73. Jacquwmond, S., Verdebout,J., Schmuch, G., Andreoli,g., and Hosgood,B., 1995, Investigation of leaf biochemistry by statistics. Remote Sensing of Environment, 54, 180-188.
74. Johnson, L.F., Hlavka, C.A., and Peterson, D.L., 1994, Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect, Remote Sensing of Environment, 47, 216-230.
75. Jackson,R.D. and Ezra.C.E. Spectral Response of cotton to suddenly induced water stress [J]. International Journal of Remote Sensing, 1985,6:177-185.
76. Johnson_LF, Billow-CR. Spectrometric estimation of total nitrogen concentration in Douglaa_fir forliage[J]. International Journal of Sensing, 1972,64:11-13.
77. Jackson, r.D., and Pinter, Jr.,P.J. Spectral response of a plant canopy with different canopies [J]. Remote Sensing of Enviroment, 1986,20:43-56.
78. Jayarama V. et al, The invariance of red-edge inflection wavelength derived from ground based spectra-radiometer and space-borne IRS-P3: MOS_B data. Int. J. Remote Sensing.,
23(14),2741-2765.
79. Johnson,J.F.,Nitrogen influence on fresh-leafNIR spectra Remote Sensing. EnViron., 2001.78(3):314-320.
80. Jensen, A.B, Lorenzen, H. et al, 1990, Radiometric estimation of biomass and N content of barley grown at different N levels, International Journal Remote Sensing 11:1809—1820.
81. Kuusk, A. A fast, invertible canopy reflectance model. Remote Sensing of Environment, 1995.51(3): 342-350.
82. Lacapra, V.C., Melack, J.M., Gastil, M. And Valeriano, D., 1996, Remote Sensing of foliar chemistry of inundated rice with imaging spectrometry. Remote Sensing of Environment, 55, 50-58.
83. Lee Tarpley, et al,Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science, 2000, 40: 1814-1819.
84. Lilienthal, H.; Haneklaus, S.; et al. Utilization of hyperspectral data for the evaluation of the spatial variability of the nitrogen status of wheat. Aspects of Applied Biology, 2000. No.60: 189-194.
85. Lamb D.W., Steyn-ross D.W., Steyn-ross M., Schaare P. et al, Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modeling and experiment observations, Int. J. Remote Sensing., 2002,23 (18),3619-3648.
86. Munden, R., Curran, P.J., and Catt, J.A., 1994, The relationship between red edge and chlorophyll connection in the Broadbalk winter wheat experiment at Rothamsted. International Journal of Remote Sensing, 15, 705-709.
87. M.Susan Moran, Paul J.Pinter, Jr., Brent E.Clothier and Stephen G. Allen. Effect of water stress on the canopy architecture and spectral Indices of irrigated Alfalfa[J]. remote Sensing of Environment, 1989,29:251～261.
88. Michio shibayama, Tsuyoshi Akiyama, Estimating grain yield of maturing reflectance measuremets. Remote Sensing of Environment, 6:45—53, 1991.
89. Malthus, T.J. et al .High Resolution Spectroradio-metry: Spectral Reflectance of Field Bean Leaves Infected by Botrytis Fabea, Remote Sensing of Environment, Vol 45: 107—116, 1993.
90. Michio Shibayuma, Tsuyoshi Akiyama, Seasonal Visible, Near-infrared and Mid infrared spectra Of Rice Canopies in Relation to LAI and above-ground Dry phytomass., Remote Sensing of Environment,Vol 27,119—127,1989.
91. Moran, M.S.Y, Inoue, et al, 1997, Opportunities and limitations for image-based remote Sensing in precision crop management. Remote Sensinge Environment 61: 319—346.
92. Manjunath; Potdar;Purohit, Large area operational wheat yield model development and validation based on spectral and meteorological data, Int. J. Remote Sensing
23.no. 15,2002:3023_3038.
93. Maselli; Romanelli; Bottai et al, Processing of GAC NDVI data for yield forecasting in the Sahelian region,Int.J.Remote Sensing.21,no. 18,2000:3509-3523.
94. Miller J.R.et al., Quantitative characterization of the vegetation red edge reflectance model, Int. J. remote Sensing. 1990,11,1775-1795.
95. O'Neill, P. E.; et al.Use of active and passive microwave remote Sensing for soil moisture estimation through com. International Journal of Remote Sensing, 1996. 17(10): 1851-1865.
96. Patrice B., et al,A method of biophysical parameter tetrieval at global scale by inversion of a vegetation reflectance model. Remote Sensing Environment, 1999,67(3):251-266.
97. Plant, R.E; Munk, D. Set al. Relationships between remotely Sensinged reflectance data and cotton growth and yield, Transactions of the ASAE, 2000.43(3): 535-546.
98. Penuelas, J.I., Filella, C..Biel, L.Serano and R.Save. The Reflectance at 950-970nm Region as an Indicator of Plant water Status [J]. International Journal of Remote Sensing, 1993,14:1887-1905.
99. Penuelas, J.,J.Pinol,R.Ogaya and I.Filella. Estimatiom of Plant Water Concentration by the Reflectance Water Index W, I(R900/R970)[J].International Journal of Remote Sensing, 1997,18:2869-2872.
100. Penuelas, J. et al,Cell Wall Elasticity and Water Index (R970nm/R900nm) in Wheat Under Different Nitrogen Availabilities, International Journal of Remote Sensing, Vol 17(2):373—382,1996.
101. Pinar, A. Grass Chlorophyll and the Reflectance Red Edge, International of Remote Sensing, 17, 351—357, 1996.
102. Penuelas, J, J.A. Gamon, et al, 1994, Reflectance indices associated with physiological changes in N and water-limited sunflower leaves. Remote Sensing Environment, 46:100—118.
103. Price, J. C.; Bausch, W. C. Leaf area index estimation from visible and near-infrared reflectance data. Remote Sensing of Environment, 1995.52(1): 55-65.
104. Penuelas, J., and I. Filella, 1998, Visible and near-infrared reflectance techniques for dignosing plant physiological status, Trends Plant Sci. 3: 151—156
105. Qi, J.; Chehbouni, A.; et al. A modified soil adjusted vegetation index. Remote Sensing of Environment, 1994. 48(2): 119-126.
106. Railyan,V.Y, red edge structure of canopy reflectance spectra of triticale, Remote Sensing. Environ., 1993,46,173-182.
107. Ripple, W.J. ,1986, Spectral reflectance relationships to leaf water stress. Photogramm. Remote Sensing, 52: 1669—1775.
108. Shibayama, -M and Akiyama, -T.A Spectroradiometer for field use. Ⅶ. Radiometric Estimation of Nitrogen Levels in field Rice Canopies [J]. Japanese journal of Crop
Science, 1986,55(4): 439-455.
109. Stone, M.L., J.B. Solie, W.R.Raun, R.W.Whthitney, S.Ltaylor, J.D.Ringer. Use of spectral radiance for correcting In-season fertilizer nitrogen deficiencies in winter wheat [J]. Transaction of the ASAE, 1996,39(5): 1623-1631.
110. Sellers, P.J.1987, Canopy reflectance, photosynthesis and transpiration. Ⅱ. The role of biophysics in The linearity of their interdependence, Remote Sensing Environment, 21: 143—183.
111. Senay, G. B.; Lyon, J.G. Using high spatial resolution multispectral data to classify corn and soybean crops. Rhotogrammetric Engineering & Remote Sensing, 2000.66(3): 319-327.
112. Shiibayama et al., Estimating grain yield of maturing rice canopies using high spectra resolution refiectance measurements, Remote Sensing Environ, 1991,36,45-53
113. Singh; Semwal; Rai, and others, Small area estimation of crop yield using remote Sensing satellite data, Int J. Remote Sensing.23,no. 1,2002:49-56
114. Strachan I.B., Pattey E., Boisvert J.B., Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance,Remote Semote Sensing. Environ.,2002, 80: 213-224
115. Tian, Q.,Tong,Q.,Pu,R.,Guo X., and Zhao,C.,Spectroscopic determination of wheat water status using 1650-1850nm spectral absorption features, Int J. Remote Sensing., 2001. 22(12), 2329-2338.
116. Thomas, J.r., and g.F.Oerther. Estimating nitrogen content of sweet pepper leaves by reflectance measurements [J]. Agronomy Journal, 1972,64: 11-13.
117. Thomas J.R., Namken L.N., Oerther G.F., and Brown R.G.,Estimation leaf water content by reflectance measurements [J]. Agronomy Journal 1971,63:845-847.
118. Tanimoto, T.; Nakayabu, M.; Estimation of yield and nitrogen absorption of paddy rice in the ripening period using a spectrometer. Bulletin of the Hiroshima Prefectural Agriculture Research Center, 1997. No. 65: 1-8.
119. Thomasson, J. A.; Chenjianan; Wooten, J. R.; Cotton yield prediction improvement with remote. Memphis, USA; National cotton council, 2000.419-421.
120. Vane, G. et al. Terrestrial Imaging Spectrometry, Current Status, Furore Trend, Remote Sensing of Environment, Vol 44,109—127,1993.
121. Vane G., Goetz A.F.H. Terrestrial imaging spectroscopy, Remote Sensing Enviroment, 1988,24:1-29.
122. Van Leeuwen, W.J.D., and A.R.Huete, 1996, Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices, Remote Sensing Environment, 55: 123—138.
123. Wessman, C.A., Aber, J.d., Peterson,D.L., and Mellilo,J.M., 1988, Remote Sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature,
335,154-156.
124. Williards R S,Southworth C S. Remote Sensing makes important gains, Geotimes, 1984,8:13-15.
125. Walburg, G.; M.E.Bauer et al, 1982, Effects of N nutrition on the growth, yield and reflectance characteristics of corn canopies. Agronomy Journal 74:677—683.
126. Wiegand, C.L.et al, 1990, Multisite analyses of spectral-biophysical data for corn. Remote Sensinge. Environ. 33: 1—16.
127. Wen Wang; Wang Chang Yao. The estimation of crop water potential. Aspects of Applied Biology, 2000. No.60: 201-207.
128. Wiegand, C. L.; Rhoades, J. D.; et al. Photographic and video graphic observations for determining and mapping the response of cotton to soil salinity. Remote Sensing of Environment, 1994. 49(3): 212-223.
129. Yoder, B.J and Pettigrew-Crosby, R. E., 1995, Predicting nitrogen and chlorophyll content and concentration from reflectance spectra(400-2500nm) at leaf and canopy scales. Remote Sensing Enviroment, 53, 199-211.
2 陈述彭，赵英时．遥感地学分析，北京，测绘出版杜．1990．
3 陈述彭，童庆禧，郭华东．高光谱分辨率遥感信息机理与地物识别．遥感倍息机理研究，北京：科学出版社，1998，139-231．
4 蒋亨显，王珂，王人潮．建立大田晚稻农学光谱估产模式研究初报．浙江农业大学学报．1993．增刊：73—77．
5 陈沈斌主编，小麦、玉米和水稻遥感估产技术试验研究文集．北京：中国科学技术出版社．1993，158～162．
6 刘伟东，项月琴．郑兰芬．童庆椿．吴长山．高光谱数据与水稻叶面积及叶绿素密度的相关分析，遥感学报．2000．4(4)：279～283．
7 牛铮，陈永华，隋洪志，张庆员，赵春江．叶片化学组分成像光谱遥感探测机理分析：遥感学报，2000，4(2)：125～130．
8 浦瑞良，宫鹏．高光谱遥感及其应用．北京：高等教育出版社，2000．
9 沈掌泉，王人潮，王珂．不同早稻品种的光谱特性及其与农学参数的相关性．浙江农业大学学报．1993，增刊：31～35．
10 沈掌泉，王人潮．杂交稻与常规稻的光谱特性差异初步分析．浙江农业大学学报．1993．增刊：36—39．
11 沈掌泉，王玛，王人潮．水稻冠层光谱反射特性的动态变化研究．国土资源遥感．1996，(4)：40～44．
12 王坷，蒋亨显，王人潮．建立大田早稻农学光谱估产模式研究初报．浙江农业大学学报，1993．增刊：66～72．
13 王人潮，王珂，沈掌泉，蒋亨显．水稻单产遥感估测建模研究．遥感学报．1998．2(2)：119～124．
14 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．浙江农业大学学报，1993，增刊：7～14．
15 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．浙江农业大学学报，1993，增刊：15～22．
16 王人潮，陈铭臻，蒋亨显．水稻遥感估产的农学机理研究——Ⅲ，从产量结构分析建立单产农学光谱试验摸式．浙江农业大学学报，1993．增刊：23～29．
17 王人潮，王深法，陈铭臻，蒋亨显．水稻遥感估产系统研究_——预试验和前期研究结果以及对开展估产研究的讨论．浙江农业大学学报．1993，增刊：1～6．
18 王人潮，黄敬峰．水稻遥感估产，农业出版社，2001．
19 王延颐，高庆芳．稻田光谱与水稻长势及产量结构要素关系的研究．国土资源遥，1996．(1)：51～55．
20 王延颐，植被指数与水稻长势及产量结构要素关系的研究．国土资源遥感1996．(1)：
50～59．
21 王乃斌，中国小麦遥感动态监测与估产，北京：中国科学技术出版社，1996．
22 王晋年，张兵，刘建贵，童庆禧，郑兰芬．以地物识别和分类为目标的高光谱数据挖掘．中国图象图形学报，1999，4(11)
23 吴长山，项月琴，郑兰芬，童庆禧．利用高光谱数据对作物群体叶绿素密度估算的研究．遥感学报，2000，4(3)：228-231．
24 张良培，郑兰芬，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．
25 郑兰芬，王晋年，成像光谱遥感技术及其成像光谱信息提取分析研究．环境遥感．1992，7(1)：49-58，
26 陈述彭，童庆禧，郭华东，遥感信息机理研究，1998，北京：科学出版社．
27 李郁竹主编，冬小麦气象卫星遥感动态监测与估产，1993，北京气象出版社
28 刘爱民等，现代精准农业及我国精准农业的发展方向，中国农业大学学报．2000，5(2)：20-25．
29 陆登槐等编著，遥感技术在农业工程中的应用，北京：清华大学出版让，1997．
30 童庆禧，郑兰芬，王晋年，湿地植被成像光谱遥感研究，遥感学报，1997，1(1)：50-58
31 童庆禧，成像光谱技术发展初探，成像光谱仪技术与应用．1993，1-9
32 王纪华，赵春江，郭晓维等，用光谱反射率诊断小麦叶片水分状况的研究，中国农业科学，2001，34(Ⅰ)：104-107
33 汪懋华，“精细农业”研究与工程科技创新，农业工程学报，1999，15， (1)：1-8
34 严泰来等，精准农业的由来与发展及其在我国的应用策略，计算机与农业，2000，(1)：3-5
35 杨敏华，面向精准农业的高光谱遥感作物信息获取，中国农业大学博士学位论文，2002年
36 陈怀亮，毛留喜，冯定原．遥感监测土壤水分的理论、方法及研究进展．遥感技术与应用，1999，14(2)：55—65．
37 陈迅．利用遥感技术进行农作物估产．计算机应用研究，2001：114—116
38 丁圣彦．精确农业的技术体系与应用研究进展．农业现代化研究，2002，23(3)：222—225
39 杜青林，加快农业遥感技术应用推动农业和农村经济发展．中国农业资源与区划，2002，23(3)：1—2
40 方红亮，田庆久，高光谱遥感在植被监测中的研究综述．遥感技术与应用．1998，13(1)：62—69
41 洪佳华，孙睿，陶英丽．夏玉米叶片光谱吸收率初探．中国农业气象，1998，19(4)：4．
42 李剑萍．气象卫星作物长势监测及产量预报系统．气象科技，2002，30(2)：108—111
43 刘良云．高光谱遥感在精准农业中的应用研究，中国科学院博士后出站报告，2002，12
44 刘伟东，高光谱技术在水稻生长监测与作物识别中的应用研究．中国农业大学硕士论文．2000
45 梅安新，彭望禄，秦其明，刘慧平，遥感导论．高等教育出版社，2001年7月．
46 谭凯琰，李郁竹，遥感植被指数与冬小麦绿叶面积系数和麦土比的关系．遥感信息．1998(1)：18—21．
47 唐延林，王人潮，遥感技术在精准农业中的应用．现代化农业，2002，2：33—35
48 田庆久，宫鹏，赵春江，郭晓维，用光谱反射率诊断小麦水分状况的可行性分析．科学通报．2000，45(24)：2645—2650
49 王纪华 赵春江，郭晓维，田庆久，用光谱反射率诊断小麦叶片水分状况的研究．中国农业科学，2001，34(1)：104—107
50 王纪华等，利用遥感方法诊断小麦叶片含水量的研究．华北农学报，2000，15(4)：68
51 王茂新，裴志远，吴全等，应用NOAA图像监测冬小麦面积的研究．农业工程学报，1998，14(3)：84—88
52 王人潮等，论中国农业遥感与信息技术发展战略．科技通报，1999，15(Ⅰ)：1—7．
53 王思远等，遥感与GIS支持下的中国森林植被动态变化分析，资源学报，2002，24(5)：64—69．
54 吴炳方，全国农情监测与估产的运行化遥感方法，地理学报，2000，55(1)：25—35
55 杨邦杰，裴志远，作物长势的定义与遥感监测．农业工程学报，1999，15(3)：214—218
56 杨邦杰，裴志远等，我国农情遥感监测关键技术研究进展．农业工程学报，2002，18(3)：191—194
57 张建华，作物估产的遥感一数值模拟方法．干旱区资源与环境，2000，14(2)：82—86．
58 张良培，郑兰芬，童庆禧，利用高光谱数据对生物变量进行估计．遥感学报，1997，1(2)：110—113．
59 杨相恒等，氮、磷交互作用下小麦氮素营养丰缺的光谱诊断与估测．遥感信息，1992．
60 童庆禧等，湿地植被成象光谱遥感研究．遥感学报， 1997，1(1)．
61 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．遥感学报，1997，1(2)．
62 浦瑞良、宫鹏等，美国西部黄松叶面积指数与高光谱分辨CASI数据的相关分析．环境遥感，1993，8(2)．
63 高惠璇．实用统计方法与SAS系统[M]．北京：北京大学出版社，2001．
64 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)：228～231．[21]
65 刘伟东，项月琴，郑兰芬等．高光谱数据与水稻叶面积及叶绿素密度的相关分析[J]．遥感学报，2000，4(4)：279～283．
66 张福锁等，环境胁迫与植物营养，1993，北京：中国农业大学出版社．
67 张霞等，利用高光谱遥感图像估算小麦氮含量，遥感学报，2003，Vol，7，No，3．
68 王秀珍等，水稻地上鲜生物量的高光谱遥感估算模型研究，作物学报，2003，Vol，29，No，6．
69 王秀珍等，水稻生物化学参数与高光谱遥感特征参数的相关分析，农业工程学报2003，Vol，19，No，2．
70 唐延林等，水稻、玉米、棉花的高光谱及其红边特征比较，中国农业科学，2004，
37(1)：29-35
71 王登伟等，棉花主要栽培生理参数的高光谱估测研究研究，中国农业科学，2003，36(7)
1. Anatoly A., Gitelson Y, Yoram J K, Robert S, et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing Environ.,2002.80:76-87.
2. Asner G. P. 1998,Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensinge of Environment, 64, 234-253.
3. Andrieu B; Sohbi Y. A, direct method to measure bi-directional gap fraction in vegetation canopies. Remote Sensing of Environment, 1994. 50(1): 61-66.
4. Asner G .P, Wessman C A and Privette J L, 2000,Impact of tissue, canopy, and landscape factors on the hyperspectral reflectance variability of add ecosystems. Remote Sensing of Environment, 41, 133-142
5. Ammenberg P.,Flink P and Lindell T.,.et al. Bio-optical modeling combined with remote Sensing to assess water quality. Int J. Remote Sensing. 2002.23 (8): 1621-1638
6. Bieh L L., and B F Robinson, 1993, Data acquisition and preprocessing techniques for remote Sensing field research, Proc. Soc. Photo-Optical Inst-r. Engr, 356:143—149.
7. Baret,F., Vanderbilt,V C, Steven,M.D., and Jacquemoud,S.,1994,Use of spectral analogy to evaluate canopy reflectance Sensingivity to leaf optical properties. Remote Sensing of Environment, 48,253-260
8. Begue A, 1993,Leaf area index,interepted photosynthetically active radiation and spectral vegetation indices:A Sensingitivity analysis for regular-clumped canopies Remote Sensing of Environment 46:45-59
9. Boochs, F..1990,Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing, Vol 11,No.10,1741—175
10. Bausch W C., 1993,Soil Background Effect on Reflectance -based Crop Coefficients for Corn. Remote Sensing of Environment, 46:213-222
11. Bartlett, D.S. et al, 1990,Use of vegetation indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy, Remote Sensing. Environ. 30: 115—128.
12. Blackbum, G.A.,and Milton,E.J.,1995,Seasonal variations in the spectral reflectance of deciduous tree can canopies. International Journal of Remote Sensing, 16:709-721.
13. Blackburn G A., 1998,Quantifying chlorophylls and carotenoids at leaf and canopy scales :An evaluation of some hyperspectral approaches.Remote Sensing of Environment, 66:273-285.
14. Blackburn G. A.,1998,Spectral indices for estimating photo-synthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing,19: 657-675.
15. Boochs F,Kupfer, G., Duckter, K., and Kuhbauch, W., 1990,Shape of the red edge as a
vitality indicator for plants. International Journal of Remote Sensing, 11,1741-1753
16. Bouman B A M., 1992, Linking physical remote Sensing models with crop growth simulation models, applied for sugar beet, International Journal of Remote Sensing, 13:2565-2581
17. Ball S T.; Frazier, B. E. Evaluating the association between wheat yield and remotelysensing data. Cereal Research Communications, 1993.21 (2/3): 213-219.
18. Bellairs, S. M.; Turner, N. C.; et al. Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers. Australian Journal of Agricultural Research, 1996.47(7): 1017-1034.
19. Blackmer, T. M.; Schepers, J. S.; et al. Analysis of aerial photography for nitrogen, Agronomy Journal, 1996. 88(5): 729-733.
20. Blackmer, T. M.; et al. Remote sensing to detect nitrogen deficiency in corn. American society of Agronomy, 1995.505-512. ISBNO-89118-127-X.
21. Boken. Shaykewich Improving an operational wheat yield model using phonological phase-based Normalized Difference Vegetation Index, Int. J. Remote Sensing. 23.20(2002) :4155-4168.
22. Broge N.H. and Motensen J.V.,Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data, Remote Sensing Environment.,2002,81:45-57.
23. Broge N.H. et al. Comparing prediction power and stability of broadband and hyper spectral vegetation indices for estimation of green leaves area index and canopy chlorophyll density, Remote Sensing Environment.,2001,76:156-172.
24. Curran,P.J., Dungan,J.L., Macler, B.A., and Peterson,D.L.,1992,Reflectance of fresh whole leaves for the estimation of chemical concentration, Remote Sensing of Environment,39:153-156
25. Curran,P.J, Dungan,J.L, Macler, B.A, and Peterson,S.E.,1991,The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment, 35:69-76
26. Curran,P.J.,Windham, W.R.,and Gholz,H.L.,1995,Exploring the relationship between reflectance red edge and chlorophyll content in slash pine, Tree physiology, 15,203-206
27. Compton J. Tucker. Remote sensing of leaf water content in the near Infrared [J]. Remote Sensing of Environment, 1980,10:23-32.
28. Cibula, W.G., Zetka, E.F., and Rickman, D.L Response of Thematic Mapper Bands to Plant Water Stress[J].International Journal of Remote Sensing, 1992,13:1869-1880.
29. Collins, W., Remote sensing of crop type and maturity, Photogrammetric Engineering and Remote Sensing, Vol 44,43—55,1978.
30. Card,D.H., Peterson,D.L., Matson,P.A., and Aber, J.D., 1988,Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy, Remote Sensing of
Environment,26:123-147
31. Chappelie,E.W., Kim,M.S., and Mcmurtrey, J.E.Ⅲ,1992,Ration analysis of reflectance spectra(RARS):An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b and carotenoids in soybean leaves. Remote Sensing of Environment, 39:239-247
32. Clevers,J.G. P.W.,1988,The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25:53 -69
33. Cloutis,E.A., Hyperspectral geological remote Sensing: evaluation of analytical techniques. Int .J. Remote Sensing. 1996,17(12):2215-2242
34. Curran,P.J., 1989,Remote Sensing of foliar chemistry. Remote Sensing of Environment, 30:271-278
35. Clevers, J.G.P.W. 1997, A simplified approach for yield prediction of sugar beet lased on optical remote Sensing data, Remote Sensing. Environ.2001:221—228.
36. Curran,P.J., Dungan,J.L., and Cholz, H.L., 1990,Exploring the relationship between reflectance red edge and chlorophyll content in slash pine Tree physiology,7,33-48
37. Casanova. D.; Epema. G. F., Monitoring rice reflectance at field level for estimating biomass and LAI. Field crops Research, 1998.55(1/2): 83-92.
38. Chosh, R.; et al. Linear polarization measurements of a wheat canopy. International Journal of Remote Sensing, 1993.14(13): 2501-2508.
39. Dawsan,F.W., Steven,M.D., Malthus,T.J. ,and Clark,J.A.,1992,High spectral resolution data for determining leaf water content. International Journal of Remote Sensing, 13:461-470.
40. Dawsan,T.P., Curran,P.J., and Plummer, S.E.,1998,Liberty-modelling the effects of leaf biochemical concentration on reflectance spectra. Remote Sensing of Environment, 65:50-60
41. Dungan,J., Johnson,L., and Vanderbilt,V.,1996,High spectral reflectance of douglas fir grown under different fertilization treatments: experiment design and treatment effects, Remote Sensing of Environment,55:217-228
42. Demetriades,et al, High Resolution Derivative Spectra in Remote Sensing, Remote Sensing of Environment, Vol 33:55—64,1990..
43. Danson, F. M. Red Edge Response to leaf Area Index, International Journal of Remote Sensing, Vol 16,183—188,1995.
44. Das,D.K.et al, 1993, Assessing growth and yield of wheat using remotely-sensinged canopy temperature and spectral indices. International Journal Remote Sensing 14:3081—3092.
45. Daughtry, C.S.T, 1992, Spectral estimates of absorbed radiation and photomas production in corn and soybean canopies. Remote Sensinge Environment 39:141—152.
46. Daughtry, C.S.T. 1990. Direct measurements of canopy structure. Remote Sensing Rev,
5:45—60.
47. Datt B., Visible/near fared reflectance and chlorophyll content in Eucalyptus leaves, Int J. Remote Sensing., 1999,20(14):27412459.
48. Elvidge, C.D.,Chen,Z.K.,and Groeneveld,D,D.P.,1993,Detection of trace quantities of green vegetation 1990 AVIRIS data. Remote Sensing of Environment, 44,271-297.
49. Elvidge, C.D.,Chen,Z.K.,and Groeneveld,D,D.P.,1993,Detection of trace quantities of green vegetation 1990 AVIRIS data .Remote Sensing of Environment ,44,271-297.
50. Elvidge,C.D, and Chen Z., 1995, Comparison of broadband and narrow-band red and near-infared vegetation indices. Remote Sensing of Environment,,54,38-48.
51. Enumal et al., Spectral blue shift of red edge monitors damage class of beech trees, Remote Sensing. Envien., 1992,39.81-84.
52. Filella, I., and Penuelas,J., 1994,The red edge position and shape as indicators plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15,1459-1470.
53. Filella, I. L. Serrano. J. et al, 1995, Evaluating wheat N status with canopy reflectance indices and discriminated analysis, Crop Science, 35:1400—1405.
54. Ferencz, C.; Tarcsai, G.; et al. Correction of atmospheric effects of satellite remote Sensing data (Land sat MSS-NOAA AVHRR) for surface canopy investigations. International Journal of Remote Sensing, 1993.14(18): 3417-3431.
55. Fernandez, S.; et al. Radiometric characteristics of tritium aestivum c v. Astral under water and nitrogen stress. International Journal of Remote Sensing, 1994.15(9): 1867-1884.
56. Goetz,A.F.H., Vane,G.,Solomon,J.E., and Rock B.N.,1985, Imaging spectrometry for earth remote Sensing.Science,228,1147-1153.
57. Gong,p.,PU,R.,and Miller, J.R.,1995,Coniferous forest leaf index estimation along the oregon transact using compact airborne spectrographic imager data. Photogrammetric Engineering and Remote Sensing, 61,1107-1117.
58. Grossman, Y.L., Ustin, S.L., Jacquemod, S., Sanderson, E.W., Schmuck, G., and Verdebout,J.,1996, Critique of stepwise linear regression for the extraction of leaf biochemistry information form leaf reflectance data. Remote Sensing of Environment, 56,182-193.
59. Gregory A. Carter. Primary and Secondary Effects of Water Content on the Spectral Reflectance of Leaves [J]. American Journal of Botany, 1991,78(7): 916-924.
60. Gilabert, M.A.et al. Analysis of spectral-biophysical Relationships for a Corn Canopy, Remote Sensing of Environment, Vol 55,11—20,1996.
61. Gao B.C., NDWI: a normalized difference water index for remote Sensing vegetation liquid water from space, Remote Sensing. Environ., 1996.58:257-266.
62. Green, C.F.,1987, Nitrogen nutrition and wheat growth in relation to absorbed solar radiation, Agric. For. Meteorol. 41:207—248.
63. Gallo,K .P. et al, 1985, Spectral estimation of absorbed photo synthetically active radiation in corn canopies, Remote Sensing. Environ, 17:221—232.
64. Groward, S.N. et al, 1992, Vegetation canopy PAR absorptance and the normalized difference vegetation Index: An assessment using the sail model. Remote Sensing. Environ, 39:131—140.
65. Horler, D. N. H. et al.1983C, The red edge of plant leaf reflectance, International Journal of Remote Sensing, 4,273—288.
66. Holben, B.N Schutt, J.B and Mcmurtrey J., Leaf Water Stress Detection Utilizing Thematic mapper bands 3,4 and 5 in Soybean Plants [J], International Journal of Remote Sensing, 1983,4:289-297.
67. Hunt G R. Electromagnetic radiation: The communication link in remote Sensing. In: Remote Sensing in geology. Siegel, Gillespie A. Wiley New York, 1980,702.
68. Hunt, E.R.,Jr., Ronk, B.N., and Nobel, P.S., 1987, Measurement of leaf relative water content by infrared reflectance, Remote Sensing Environ, 22:429—435.
69. Hunt, E.R., Jr., and Rock, B.N, 1990, Detection of changes in leaf water content using near-and middle-infrared reflectance, Remote Sensing. Environ.
70. Hope, A.S.,1988,Estimation of wheat canopy resistance using combined remotely sensing spectral reflectance and thermal observations, Remote Sensing. Environ, 24:369—383.
71. Inoue, Y., Morinaga, S.,Shibayama,M.. Non-destructive Estimation of Water Status of Intact Crop Leaves Based on Spectral Reflectance Measurements [J]. Japanese Journal of Crop Science, 1993,62:462-469.
72. Jackson, R.D., Moran, M.S., Slater R.N., and Bigger, S.F., 1987, Field calibration of reflectance panels. Remote Sensing of Environment, 22,145-158.
73. Jacquwmond, S., Verdebout,J., Schmuch, G., Andreoli,g., and Hosgood,B., 1995, Investigation of leaf biochemistry by statistics. Remote Sensing of Environment, 54, 180-188.
74. Johnson, L.F., Hlavka, C.A., and Peterson, D.L., 1994, Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect, Remote Sensing of Environment, 47, 216-230.
75. Jackson,R.D. and Ezra.C.E. Spectral Response of cotton to suddenly induced water stress [J]. International Journal of Remote Sensing, 1985,6:177-185.
76. Johnson_LF, Billow-CR. Spectrometric estimation of total nitrogen concentration in Douglaa_fir forliage[J]. International Journal of Sensing, 1972,64:11-13.
77. Jackson, r.D., and Pinter, Jr.,P.J. Spectral response of a plant canopy with different canopies [J]. Remote Sensing of Enviroment, 1986,20:43-56.
78. Jayarama V. et al, The invariance of red-edge inflection wavelength derived from ground based spectra-radiometer and space-borne IRS-P3: MOS_B data. Int. J. Remote Sensing.,
23(14),2741-2765.
79. Johnson,J.F.,Nitrogen influence on fresh-leafNIR spectra Remote Sensing. EnViron., 2001.78(3):314-320.
80. Jensen, A.B, Lorenzen, H. et al, 1990, Radiometric estimation of biomass and N content of barley grown at different N levels, International Journal Remote Sensing 11:1809—1820.
81. Kuusk, A. A fast, invertible canopy reflectance model. Remote Sensing of Environment, 1995.51(3): 342-350.
82. Lacapra, V.C., Melack, J.M., Gastil, M. And Valeriano, D., 1996, Remote Sensing of foliar chemistry of inundated rice with imaging spectrometry. Remote Sensing of Environment, 55, 50-58.
83. Lee Tarpley, et al,Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science, 2000, 40: 1814-1819.
84. Lilienthal, H.; Haneklaus, S.; et al. Utilization of hyperspectral data for the evaluation of the spatial variability of the nitrogen status of wheat. Aspects of Applied Biology, 2000. No.60: 189-194.
85. Lamb D.W., Steyn-ross D.W., Steyn-ross M., Schaare P. et al, Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: theoretical modeling and experiment observations, Int. J. Remote Sensing., 2002,23 (18),3619-3648.
86. Munden, R., Curran, P.J., and Catt, J.A., 1994, The relationship between red edge and chlorophyll connection in the Broadbalk winter wheat experiment at Rothamsted. International Journal of Remote Sensing, 15, 705-709.
87. M.Susan Moran, Paul J.Pinter, Jr., Brent E.Clothier and Stephen G. Allen. Effect of water stress on the canopy architecture and spectral Indices of irrigated Alfalfa[J]. remote Sensing of Environment, 1989,29:251～261.
88. Michio shibayama, Tsuyoshi Akiyama, Estimating grain yield of maturing reflectance measuremets. Remote Sensing of Environment, 6:45—53, 1991.
89. Malthus, T.J. et al .High Resolution Spectroradio-metry: Spectral Reflectance of Field Bean Leaves Infected by Botrytis Fabea, Remote Sensing of Environment, Vol 45: 107—116, 1993.
90. Michio Shibayuma, Tsuyoshi Akiyama, Seasonal Visible, Near-infrared and Mid infrared spectra Of Rice Canopies in Relation to LAI and above-ground Dry phytomass., Remote Sensing of Environment,Vol 27,119—127,1989.
91. Moran, M.S.Y, Inoue, et al, 1997, Opportunities and limitations for image-based remote Sensing in precision crop management. Remote Sensinge Environment 61: 319—346.
92. Manjunath; Potdar;Purohit, Large area operational wheat yield model development and validation based on spectral and meteorological data, Int. J. Remote Sensing
23.no. 15,2002:3023_3038.
93. Maselli; Romanelli; Bottai et al, Processing of GAC NDVI data for yield forecasting in the Sahelian region,Int.J.Remote Sensing.21,no. 18,2000:3509-3523.
94. Miller J.R.et al., Quantitative characterization of the vegetation red edge reflectance model, Int. J. remote Sensing. 1990,11,1775-1795.
95. O'Neill, P. E.; et al.Use of active and passive microwave remote Sensing for soil moisture estimation through com. International Journal of Remote Sensing, 1996. 17(10): 1851-1865.
96. Patrice B., et al,A method of biophysical parameter tetrieval at global scale by inversion of a vegetation reflectance model. Remote Sensing Environment, 1999,67(3):251-266.
97. Plant, R.E; Munk, D. Set al. Relationships between remotely Sensinged reflectance data and cotton growth and yield, Transactions of the ASAE, 2000.43(3): 535-546.
98. Penuelas, J.I., Filella, C..Biel, L.Serano and R.Save. The Reflectance at 950-970nm Region as an Indicator of Plant water Status [J]. International Journal of Remote Sensing, 1993,14:1887-1905.
99. Penuelas, J.,J.Pinol,R.Ogaya and I.Filella. Estimatiom of Plant Water Concentration by the Reflectance Water Index W, I(R900/R970)[J].International Journal of Remote Sensing, 1997,18:2869-2872.
100. Penuelas, J. et al,Cell Wall Elasticity and Water Index (R970nm/R900nm) in Wheat Under Different Nitrogen Availabilities, International Journal of Remote Sensing, Vol 17(2):373—382,1996.
101. Pinar, A. Grass Chlorophyll and the Reflectance Red Edge, International of Remote Sensing, 17, 351—357, 1996.
102. Penuelas, J, J.A. Gamon, et al, 1994, Reflectance indices associated with physiological changes in N and water-limited sunflower leaves. Remote Sensing Environment, 46:100—118.
103. Price, J. C.; Bausch, W. C. Leaf area index estimation from visible and near-infrared reflectance data. Remote Sensing of Environment, 1995.52(1): 55-65.
104. Penuelas, J., and I. Filella, 1998, Visible and near-infrared reflectance techniques for dignosing plant physiological status, Trends Plant Sci. 3: 151—156
105. Qi, J.; Chehbouni, A.; et al. A modified soil adjusted vegetation index. Remote Sensing of Environment, 1994. 48(2): 119-126.
106. Railyan,V.Y, red edge structure of canopy reflectance spectra of triticale, Remote Sensing. Environ., 1993,46,173-182.
107. Ripple, W.J. ,1986, Spectral reflectance relationships to leaf water stress. Photogramm. Remote Sensing, 52: 1669—1775.
108. Shibayama, -M and Akiyama, -T.A Spectroradiometer for field use. Ⅶ. Radiometric Estimation of Nitrogen Levels in field Rice Canopies [J]. Japanese journal of Crop
Science, 1986,55(4): 439-455.
109. Stone, M.L., J.B. Solie, W.R.Raun, R.W.Whthitney, S.Ltaylor, J.D.Ringer. Use of spectral radiance for correcting In-season fertilizer nitrogen deficiencies in winter wheat [J]. Transaction of the ASAE, 1996,39(5): 1623-1631.
110. Sellers, P.J.1987, Canopy reflectance, photosynthesis and transpiration. Ⅱ. The role of biophysics in The linearity of their interdependence, Remote Sensing Environment, 21: 143—183.
111. Senay, G. B.; Lyon, J.G. Using high spatial resolution multispectral data to classify corn and soybean crops. Rhotogrammetric Engineering & Remote Sensing, 2000.66(3): 319-327.
112. Shiibayama et al., Estimating grain yield of maturing rice canopies using high spectra resolution refiectance measurements, Remote Sensing Environ, 1991,36,45-53
113. Singh; Semwal; Rai, and others, Small area estimation of crop yield using remote Sensing satellite data, Int J. Remote Sensing.23,no. 1,2002:49-56
114. Strachan I.B., Pattey E., Boisvert J.B., Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance,Remote Semote Sensing. Environ.,2002, 80: 213-224
115. Tian, Q.,Tong,Q.,Pu,R.,Guo X., and Zhao,C.,Spectroscopic determination of wheat water status using 1650-1850nm spectral absorption features, Int J. Remote Sensing., 2001. 22(12), 2329-2338.
116. Thomas, J.r., and g.F.Oerther. Estimating nitrogen content of sweet pepper leaves by reflectance measurements [J]. Agronomy Journal, 1972,64: 11-13.
117. Thomas J.R., Namken L.N., Oerther G.F., and Brown R.G.,Estimation leaf water content by reflectance measurements [J]. Agronomy Journal 1971,63:845-847.
118. Tanimoto, T.; Nakayabu, M.; Estimation of yield and nitrogen absorption of paddy rice in the ripening period using a spectrometer. Bulletin of the Hiroshima Prefectural Agriculture Research Center, 1997. No. 65: 1-8.
119. Thomasson, J. A.; Chenjianan; Wooten, J. R.; Cotton yield prediction improvement with remote. Memphis, USA; National cotton council, 2000.419-421.
120. Vane, G. et al. Terrestrial Imaging Spectrometry, Current Status, Furore Trend, Remote Sensing of Environment, Vol 44,109—127,1993.
121. Vane G., Goetz A.F.H. Terrestrial imaging spectroscopy, Remote Sensing Enviroment, 1988,24:1-29.
122. Van Leeuwen, W.J.D., and A.R.Huete, 1996, Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices, Remote Sensing Environment, 55: 123—138.
123. Wessman, C.A., Aber, J.d., Peterson,D.L., and Mellilo,J.M., 1988, Remote Sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature,
335,154-156.
124. Williards R S,Southworth C S. Remote Sensing makes important gains, Geotimes, 1984,8:13-15.
125. Walburg, G.; M.E.Bauer et al, 1982, Effects of N nutrition on the growth, yield and reflectance characteristics of corn canopies. Agronomy Journal 74:677—683.
126. Wiegand, C.L.et al, 1990, Multisite analyses of spectral-biophysical data for corn. Remote Sensinge. Environ. 33: 1—16.
127. Wen Wang; Wang Chang Yao. The estimation of crop water potential. Aspects of Applied Biology, 2000. No.60: 201-207.
128. Wiegand, C. L.; Rhoades, J. D.; et al. Photographic and video graphic observations for determining and mapping the response of cotton to soil salinity. Remote Sensing of Environment, 1994. 49(3): 212-223.
129. Yoder, B.J and Pettigrew-Crosby, R. E., 1995, Predicting nitrogen and chlorophyll content and concentration from reflectance spectra(400-2500nm) at leaf and canopy scales. Remote Sensing Enviroment, 53, 199-211.