水稻BRDF模型集成与应用研究
摘要
水稻是中国的主要粮食作物，其总产量占世界第一位，因此，对水稻的长
势监测和产量预测历来都受到了人们的关注。而遥感技术提供了周期、迅速、
大面积获取农业信息的手段，能够从宏观上实现对作物的全面监测，因此，在
农作物长势监测和产量预测方面是大有作为的。但水稻卫星遥感估产有许多特
殊困难，成为国际性难题。浙江大学农业遥感与信息技术应用研究所，经过连
续18年的研究，完成了浙江省水稻卫星遥感估产运行系统，但估产精度的稳定
度仍不够理想，其主要原因是遥感信息机理研究还不够深入。本论文正是希望
通过对水稻冠层二向反射特性的研究，增强人们对水稻冠层光谱反射特性的理
解，以利于提高遥感估产的精度，促进遥感技术在农业中的应用。
●植被二向反射研究进展
植被二向反射特性与观测角和入射角、植被冠层结构（如：冠层厚度、冠
层叶角分布、叶的形态结构和空间分布）、植被冠层构成要素的光谱特性和植被
下垫面特性之间有密切的联系。对植被二向反射特性的研究，正是希望通过对
植被冠层反射率进行多角度的观测，掌握植被冠层反射率随观测角和入射角的
变化规律。通过建立植被二向反射模型，模拟光在植被冠层内的传输过程，掌
握植被冠层二向反射率、冠层厚度、冠层叶角分布、叶的形态结构和空间分布
和植被下垫面特性之间的关系。并通过模型反演，获得丰富的冠层结构信息，
从而可通过非破坏性手段，实现对作物的长势监测和产量估算。
科学家们对植被二向反射特性的研究始于70年代，并从8O年代中期逐渐
浙江大学博士学位论文 中义摘要
成为遥感界的热I’刁研究领域之一。目前，植被二向反射模型的研究己经步入一个
稳定期，研究工作己不局限于对理论的创新与推导，研究范围扩展到从地面实测
到遥感数据的实际应用等诸多方面，其主要特点有：
门）理论工作大的突破的可能性减小，工作集中于对理论的修补、细化和完善
上，由于简化的理论容易反演，所以格外引起重视。
（2）随着具有斜视角度观测能力的星载和机载传感器（如 POLDER、
VEGETATION、MODIS、MISR、MERIS、ADEOS、AVNIR、ASAS等）的应
用，植被二向反射模型己应用于星载和机载传感器，这将大大地推动二向反射
模型的应用和发展。
门）植被二向反射地面实测工作开展得较少，缺乏大量的地面实测数据来对各
种模型进行验证，这将不利于植被二向反射模型的发展。
目前所建立的植被二向反射模型可分为三类：辐射传输模型、几何光学模
型和计算机模拟模型。
．试验设计
（1）田间试验 1999年和 2000年进行两次大田试验。1999年对一种氮素水平
进行观测，2000年对五种氮素水平进行观测。
（2）观测数据 观测数据包括：冠层二向反射率的测量。D－＋片光谱的测量。
冠层结构的测量、叶片生物化学成分的测量。
．数据分析
门）用实测数据分析水稻冠层二向反射特性的一般规律，包括冠层二向反射特
性随观测天顶角、观测方位角和冠层结构变化的变化规律；冠层二向反射特性
随不同氮素水平而变化的变化规律。
（2）由椭圆模型。PROSPECT模型、FCR模型和太阳高度角计算模型组成水
11
b
浙江大学博d：学位论义 中文摘要
稻BRDF模型，可实现冠层叶倾角分布模拟、叶片光谱特性模拟、冠层垂
直反射率模拟和冠层二向反射率模拟。
臼）利用POWELL法建立反演模型。可反演叶片生物化学含量（叶绿素、蛋
白质、纤维素含量）和冠层结构参数（叶面积指数、叶的形状参数）。
．植被光谱模拟系统的集成
将椭圆模型、PROSPECT模型。FCR模型、反演模型及太阳高度角计算模
型集成为植被光谱模拟系统，该系统既可以完成植被叶片光谱模拟，又可完成
植被冠层结构及冠层光谱的模拟，模拟结果可用文本及图形方式显示。
．论文的创新之处
门）本项研究填补了水稻冠层二向反射特性研究的空白，对用遥感手段进行
水稻长势监测和产量预测是非常有意义的。
（2）不同氮素营养水平的水稻冠层进行研究，总结出水稻冠层二向反射率随
不同氮素水平而变化的规律。
（3）将系统集成生成植被光谱模拟系统，在系统中，只需输入叶片的生物化
学含量、椭圆模型参数及观测角度和观测时间，便可实现冠层叶倾角分布、叶
片光谱、冠层垂直反射率和冠层二向反射率的模拟，反之，通过输入冠层二向
反射率、椭圆模型参数及观测角度和观测时间，便可反演冠层结构参数和叶片
生物化学含量。
Rice is the main crop of china, and its product is the first in the world. So rice
growing monitoring and yield estimating have been arising people抯 attention. Since
remote sensing technique can get agricultural information rapidly and wide
applicability, it has great advantages in crop growing monitoring and yielding
estimation. But rice yield estimating by satellite is very difficult and has become the
world wide question. The Zhejiang province rice satellite yield estimating system
has been studied by the institute of agricultural remote sensing and information
application of Zhejiang university for 18 years, but the estimating accuracy is still
uneven. The principle reason is that there are short of deeply studying on mechanic
of remote sensing information. We hope we can know more about the rice canopy
reflectance character by studying on rice canopy bi-directional reflectance, so that
can enhance the accuracy of remote sensing yield estimating, improve the
application of remote sensing in agriculture.
? The development of bi-directional reflectance
There are intensive relations by plant bi-directional reflectance, view azimuth
and zenith angle, canopy structures (such as canopy depth, canopy angle distribution,
123
leaf relative size and spatial distribution), the spectrum character of canopy
components and the spectrum character of under plant. The bi-directional reflectance
studying is to know the changing of canopy reflectance by view angles and sun
angles. To study the hi-directional reflectance model we will modeling the photon
transition in plant canopy. So we can know more about the relation between the
bi-directional and the canopy depth, canopy angle distribution, leaf relative size and
spatial distribution and the spectrum character of under plant. By inversing we can
get abundant information about canopy structures. So we can monitoring plant
growth and forecasting the yield.
The studying of hi-directional reflectance began in 7O~, and became to the most
popular region of remote sensing in 80th Now the development is steady, the
studying is concentrating in ground measurment and application but not in new
theory development. The main characters are:
(1) Great development in theory is not very possible. The studying
concentrats in theory mending, detailing and consummating. The
simple theory arose more attention because it抯 easy to be inversed.
(2) The application of inclination remote sensor (such as
POLDER,VEGETATION, MQDIS, MJSR, MERIS, ADEQS, AVNIR
ASAS, et al ) will push forward its development and application, and
some hi-directional reflectance models have been used in these remote
sensor.
(3) Fewer ground measuring has been done, so we lack of abundant datum
to validate the models, its no good at the development of bi-directional
reflectance.
Now the hi-directional model can be classified into three kinds: transitive
124
model, geometry model and compute model.
? Experiment designing
(1) Experiment in field: the experiments were done in 1999 and 2000. The same
dealt rice canopy was measured in 1999, then five different kind of n
势监测和产量预测历来都受到了人们的关注。而遥感技术提供了周期、迅速、
大面积获取农业信息的手段，能够从宏观上实现对作物的全面监测，因此，在
农作物长势监测和产量预测方面是大有作为的。但水稻卫星遥感估产有许多特
殊困难，成为国际性难题。浙江大学农业遥感与信息技术应用研究所，经过连
续18年的研究，完成了浙江省水稻卫星遥感估产运行系统，但估产精度的稳定
度仍不够理想，其主要原因是遥感信息机理研究还不够深入。本论文正是希望
通过对水稻冠层二向反射特性的研究，增强人们对水稻冠层光谱反射特性的理
解，以利于提高遥感估产的精度，促进遥感技术在农业中的应用。
●植被二向反射研究进展
植被二向反射特性与观测角和入射角、植被冠层结构（如：冠层厚度、冠
层叶角分布、叶的形态结构和空间分布）、植被冠层构成要素的光谱特性和植被
下垫面特性之间有密切的联系。对植被二向反射特性的研究，正是希望通过对
植被冠层反射率进行多角度的观测，掌握植被冠层反射率随观测角和入射角的
变化规律。通过建立植被二向反射模型，模拟光在植被冠层内的传输过程，掌
握植被冠层二向反射率、冠层厚度、冠层叶角分布、叶的形态结构和空间分布
和植被下垫面特性之间的关系。并通过模型反演，获得丰富的冠层结构信息，
从而可通过非破坏性手段，实现对作物的长势监测和产量估算。
科学家们对植被二向反射特性的研究始于70年代，并从8O年代中期逐渐
浙江大学博士学位论文 中义摘要
成为遥感界的热I’刁研究领域之一。目前，植被二向反射模型的研究己经步入一个
稳定期，研究工作己不局限于对理论的创新与推导，研究范围扩展到从地面实测
到遥感数据的实际应用等诸多方面，其主要特点有：
门）理论工作大的突破的可能性减小，工作集中于对理论的修补、细化和完善
上，由于简化的理论容易反演，所以格外引起重视。
（2）随着具有斜视角度观测能力的星载和机载传感器（如 POLDER、
VEGETATION、MODIS、MISR、MERIS、ADEOS、AVNIR、ASAS等）的应
用，植被二向反射模型己应用于星载和机载传感器，这将大大地推动二向反射
模型的应用和发展。
门）植被二向反射地面实测工作开展得较少，缺乏大量的地面实测数据来对各
种模型进行验证，这将不利于植被二向反射模型的发展。
目前所建立的植被二向反射模型可分为三类：辐射传输模型、几何光学模
型和计算机模拟模型。
．试验设计
（1）田间试验 1999年和 2000年进行两次大田试验。1999年对一种氮素水平
进行观测，2000年对五种氮素水平进行观测。
（2）观测数据 观测数据包括：冠层二向反射率的测量。D－＋片光谱的测量。
冠层结构的测量、叶片生物化学成分的测量。
．数据分析
门）用实测数据分析水稻冠层二向反射特性的一般规律，包括冠层二向反射特
性随观测天顶角、观测方位角和冠层结构变化的变化规律；冠层二向反射特性
随不同氮素水平而变化的变化规律。
（2）由椭圆模型。PROSPECT模型、FCR模型和太阳高度角计算模型组成水
11
b
浙江大学博d：学位论义 中文摘要
稻BRDF模型，可实现冠层叶倾角分布模拟、叶片光谱特性模拟、冠层垂
直反射率模拟和冠层二向反射率模拟。
臼）利用POWELL法建立反演模型。可反演叶片生物化学含量（叶绿素、蛋
白质、纤维素含量）和冠层结构参数（叶面积指数、叶的形状参数）。
．植被光谱模拟系统的集成
将椭圆模型、PROSPECT模型。FCR模型、反演模型及太阳高度角计算模
型集成为植被光谱模拟系统，该系统既可以完成植被叶片光谱模拟，又可完成
植被冠层结构及冠层光谱的模拟，模拟结果可用文本及图形方式显示。
．论文的创新之处
门）本项研究填补了水稻冠层二向反射特性研究的空白，对用遥感手段进行
水稻长势监测和产量预测是非常有意义的。
（2）不同氮素营养水平的水稻冠层进行研究，总结出水稻冠层二向反射率随
不同氮素水平而变化的规律。
（3）将系统集成生成植被光谱模拟系统，在系统中，只需输入叶片的生物化
学含量、椭圆模型参数及观测角度和观测时间，便可实现冠层叶倾角分布、叶
片光谱、冠层垂直反射率和冠层二向反射率的模拟，反之，通过输入冠层二向
反射率、椭圆模型参数及观测角度和观测时间，便可反演冠层结构参数和叶片
生物化学含量。
Rice is the main crop of china, and its product is the first in the world. So rice
growing monitoring and yield estimating have been arising people抯 attention. Since
remote sensing technique can get agricultural information rapidly and wide
applicability, it has great advantages in crop growing monitoring and yielding
estimation. But rice yield estimating by satellite is very difficult and has become the
world wide question. The Zhejiang province rice satellite yield estimating system
has been studied by the institute of agricultural remote sensing and information
application of Zhejiang university for 18 years, but the estimating accuracy is still
uneven. The principle reason is that there are short of deeply studying on mechanic
of remote sensing information. We hope we can know more about the rice canopy
reflectance character by studying on rice canopy bi-directional reflectance, so that
can enhance the accuracy of remote sensing yield estimating, improve the
application of remote sensing in agriculture.
? The development of bi-directional reflectance
There are intensive relations by plant bi-directional reflectance, view azimuth
and zenith angle, canopy structures (such as canopy depth, canopy angle distribution,
123
leaf relative size and spatial distribution), the spectrum character of canopy
components and the spectrum character of under plant. The bi-directional reflectance
studying is to know the changing of canopy reflectance by view angles and sun
angles. To study the hi-directional reflectance model we will modeling the photon
transition in plant canopy. So we can know more about the relation between the
bi-directional and the canopy depth, canopy angle distribution, leaf relative size and
spatial distribution and the spectrum character of under plant. By inversing we can
get abundant information about canopy structures. So we can monitoring plant
growth and forecasting the yield.
The studying of hi-directional reflectance began in 7O~, and became to the most
popular region of remote sensing in 80th Now the development is steady, the
studying is concentrating in ground measurment and application but not in new
theory development. The main characters are:
(1) Great development in theory is not very possible. The studying
concentrats in theory mending, detailing and consummating. The
simple theory arose more attention because it抯 easy to be inversed.
(2) The application of inclination remote sensor (such as
POLDER,VEGETATION, MQDIS, MJSR, MERIS, ADEQS, AVNIR
ASAS, et al ) will push forward its development and application, and
some hi-directional reflectance models have been used in these remote
sensor.
(3) Fewer ground measuring has been done, so we lack of abundant datum
to validate the models, its no good at the development of bi-directional
reflectance.
Now the hi-directional model can be classified into three kinds: transitive
124
model, geometry model and compute model.
? Experiment designing
(1) Experiment in field: the experiments were done in 1999 and 2000. The same
dealt rice canopy was measured in 1999, then five different kind of n
引文
1． 冯定原等．水稻生长和产量形成的数值模式[J]．南京气象学院学报．1987，10(2)：201-211
2． 高亮之，金之庆，黄耀等．水稻计算机模拟模型及其应用之一 水稻钟模型——水稻发育动态的计算机模型[J]．中国农业气象．1989，10(3)：3～10
3． 高亮之，黄耀，金之庆．水稻计算机模拟模型及其应用之二 水稻最适群体动态的决策模型[J]．中国农业气象．1989，10(4)：1～6
4． 黄耀，高亮之，金之庆．水稻计算机模拟模型及其应用之三 水稻群体光合生产的动态模拟模型[J]．中国农业气象．1990，11(1)：10～15
5． 黄耀，高亮之，李林．水稻计算机模拟模型及其应用之四 长江中下游水稻生产的最适季节与光合产量[J]．中国农业气象．1990，11(1)：16～21
6． 金之庆，高亮之，李林等．水稻计算机模拟模型及其应用之五 估算水稻产量成分和库产量的模拟模型[J]．中国农业气象．1990，11(2)：20～26
7． 金之庆，高亮之，黄耀等．水稻计算机模拟模型及其应用之六 长江流域水稻穗粒结构的气候生态特征及库源关系协调性分析[J]．中国农业气象．1990，11(2)：27～34
8． 国家气象局．农业气象观测规范(上卷)[M]，气象出版社．1993,212pp
9． 何光渝．《FORTRAN 77 算法手册》[M]．北京：科学技术出版社．1993，813pp
10．黄策等．水稻群体物质生产过程的计算机模拟[J]．作物学报，1986，12(1)：1-8
11．黄洪峰．土壤植物大气相互作用原理及模拟研究[M]．北京：气象出版社，1997，1～43
12．李春强．水稻生长模拟模型研究综述[J]．中国农业气象．1992，13(6)
13．李小文．植被光学遥感模型与植被结构参数化[M]．科学出版社．118pp
14．李小文，A．Strahler，朱启疆等，地物二向性反射几何光学模型和观测的进展[J]．国土资源遥感．1991，7(1)：9～19
15．牛铮．植被二向反射特性研究新进展．遥感技术与应用[J]．1997，12(3)：49～57
16．覃文汉．遥感植被双向反射光谱的理论研究与应用展望．环境遥感[J]．1992，7(4)：290-299
17．户刈义次主编．作物的光合作用与物质生产[M]．北京：科学出版社．1981，17～53
18．P．S．Nobell，S．P．Long(1986)．冠层结构和光的截获[M]．J．库姆斯等主编．北京：科学出版社．45～48
19．上海师范大学生物系，上海农业学校．水稻栽培生理[M]．上海：上海科学技术出版社．1983，450pp
20．沈掌泉．水稻冠层反射特性的动态变化研究[J]． 国土资源遥感，1996,4：40～44
21．上海植物生理学会编．植物生理学实验手册[M]．上海：上海科学技术出版社．1985，644pp
22．谈小生，葛成辉．太阳角的计算方法及其在遥感中的应用[J]．国土资源遥感．1995，24(2)：48～56
23．王炳忠．太阳辐射能的测量与标准[M]．北京：科学出版社．1993，37～39
24．王人潮，陈铭臻，蒋亨显．水稻遥感的农学机理研究 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．1993，19(Sup)：7～14
25．王人潮，陈铭臻，蒋亨显．水稻遥感的农学机理研究 Ⅱ．农学参数与光谱变量的相关分析[J]．浙江农业大学学报．1993，19(Sup)：15～22
26．吴永莲．气象学基础[M]．北京：北京师范大学出版社．1987，13～32
27．项月琴，周允华，崔景芳．冬小麦群丛对总辐射截获的测量．中国科学院北京农业生态系统试验站．农田作物环境实验研究[M]．北京：气象出版社．1990，104～115
28．项月琴，田国良．遥感估算水稻产量 Ⅰ、产量与辐射截获量间关系的研究[J]．环境遥感．1988，3(4)，308～316
29．解可心，韩立兴，林友联著．最优化方法[M]，天津大学出版社．1997，321pp
30．徐正进，董克，水稻叶片基角、开张角和披垂度的同时测量方法．沈阳农业大学学报．1991，185～187
31．周启发，王人潮．水稻氮素营养水平与光谱特性的关系[J]．浙江农业大学学报．1993，19(sup．)，40～45
32．张仁华．实验遥感模型及地面基础，科学出版社[M]．1996，129-147
33. Ahmad S.P., Derring D.W. A simple analytical function for bidirectional reflectance[J]. J. Goephys. Res. Atmos. 1992,97(D17): 18867～18886
34. Anderssen R.S., Jackeet D.R.,Jupp,L.B., et al. Interpretation of a simple formulas for some key linear functionals of the foliage angle distribution[J]. Agric. Forest Meteorol. 1985,36:165～188
35. Antyufeev V.S., Marshak A.L..Monte carlo methed and transfer equation in plant canopies[J]. Remote Sens. Environ. 1990,31:183～191
36. Asrar G..,Kanemasu E.R.,Yoshida M..Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle[J]. Remote Sens. Environ. 1985,17:1～11
37. Baret F.,Strahler A.H.,Morris K.,et al..The robustness of canopy gap fraction estimates from red and near-infrared reflectances:A comparison of approaches[J]. Remote Sens. Environ. 1995,54:141～151
38. Bourages B..Technical note improvement in solar declination computation[J]. Solar Energy. 1985,35(4):367～369
39. Brakke T.W.,Otterman J..Canopy bidiretional reflectance dependence on leaf
orientation[J]. Int. J. Remote Sens.. 1990,11:1023-1032
40． Breece H.T., Holmes R.A.. Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo [J]. Appl. Opt. 1971,10:119-127
41． Campbell G.S.. Extintiction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution[J], Agricultural and forest meteorology.1986,36:317-321
42． Cihlar J., Manak D., Voisin N. AVHRR bidirectional reflectance effects and compositing[J]. Remote Sens. Environ.. 1994,48:77-88
43． Clevers J.G.P.W., The derivation of a simplified reflectance model for the estimation of leaf area index[J]. Remote Sens. Environ. 1988,295(1) :53-70
44． Clevers J.G.P.W.. The application of a weighted infrared-red vegetation index for estimating leaf area index[J]. Remote Sens. Environ. 1988,295(3) :47-55
45． Copper K., Smith J.A., Pitts,D.. Reflectance of a vegetation canopy using the adding methed[J]. Appl. Opt..1982,21:4112-4118
46． Current P.J., Dungan, J.L., Gholz,H.L.. Seasonal LAI of slash pine estimated with Landsat TM[J]. Remote Sens. Environ.1992, 39:1-13
47． Dawson T., Curran P., Plummer S.. LIBERTY:modeling the effects of leaf biochemistry on reflectance spectra[J]. Remote Sens. Environ. 1998,65:50-60
48． Deering D.W.. Field measurements of bidirectional reflectance, in theory and applications of optical remote sensing[J]. G.Asrar, Ed. 1989,14-65
49． Deering D.W., Leone P. A Sphere-scanning Radiometer for Rapid Directional Measurments of Sky and Ground Radiance [J]. Remote Sens. Environ.. 1986,19:1-24
50． Demarez V, Gastellu-Etchegorry J.P., Mougin E., et al.. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT
model. Int. J. Remote Sens. Environ.. 1999,20(5) :879-894
51． Dubayah R., Loechel S.. Modeling topographic solar radiation using GOES data[J]. J.Appl. Meteorol. 1997,36:141-154
52． Duchemin B.. NOAA/AVHRR bidirectional reflectance: modeling and application for the monitoring of a temperate forest. Remote Sens. Environ.. 1999,67:51-67
53． Eaton F.D., Dirmhirm I.. Reflected irradiances of natural surfaces and their effects on albedo [J].Appl. Opt.. 1977,18:994-1008
54． Francoise N., Francois P., Marc R S.. Bidirectional Reflectivity in AVHRR Channel 3:Application to a Region in Northern Africa[J]. Remote Sens. Environ. 1998,66:298-316
55． Ganapol B.D., Myneni R.B.. The FN methed for the one-angle radiative transfer equation applied to plant canopies[J]. Remote Sens. Environ. 1992,39:213-231
56． Gerard F.F., North P.R.J.. Analyzing the Effect of Structural Variability and Canopy Gaps on Forest BRDF Using a GEOMETRIC-Optical Model[J]. Remote Sens. Environ. 1997,62:46-62
57． Goel N. S., Dearing D. W.. Evaluation of a canopy reflectance model for LAI estimation through its inversion[J]. IEEE tranctions on geoscience and remote sensing. 1985,23(5) :674-683．
58． Goel N.S.. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data[J]. Remote sensing review. 1988,4,1-212
59． Goels N.S., Grier T.. Estimation of canopy parameters from inhomogeneous vegetation canopies from reflectance data,II:Estimation of leaf area index and percentage of ground cover for row canopies [J]. Int. J. Remote Sens.
Environ.. 1986,7:1263-1286
60． Goels N.S., Grier T.. Estimation of canopy parameters from inhomogeneous vegetation canopies from reflectance data,III:TRIM: a model for radiative transfer in heterogeneous three-dimensional canopies[J].. Remote Sens. Environ. 1988,25:255-293
61． Goels N.S., Rozehnal I., Thompson R.L., A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sens. Environ. 1991,36:73-104
62． Goel N.S., Strebel D.E. Inversion of vegetation canopy reflectance models for estimating agronomic variables. I. Problem definition and initial results using the suits model[J]. Remote Sens. Environ. 1983,13:487-507
63． Goel N.S., Strebel D.E. Inversion of vegetation canopy reflectance models for estimating agronomic variables, IV:Total inversion of the SAIL model[J]. Remote Sens. Environ. 1984,15:237-253
64． Goel N.S., Thompson R.L.. Inversion of vegetation canopy reflectance models for estimating agronomic variables, V : Estimation of LAI and average leaf angle using measured canopy reflectances[J]. Remote Sens. Environ. 1984,16:69-85
65． Gong P., Wang D. Liang S.. Inverting a canopy reflectance model using an artificial neural network[J]. Int. J. Remote Sens. Environ.. 1999,20(1) : 111-122
66． Hapke B.. Bidirectional reflectance spectroscopy. I. Theory[J], J. Geophys. Res. 86(B4) :3039-3054
67． Hatfield J.L., Kanemasu E.T., Asrar G, et al. Leaf-area estimates from spectral measurements over various planting dates of wheat[J]. Int. J. Remote Sens. 1985,6(1) :267-175
68． Irons J.R., Banson K.J., Williams D.L., et al.. An off-nadir-pointing imaging
spectroradiometer for terrestrial ecosystem studies[J].IEEE Trans Remote Sens.. 1991,29:66-74
69． Jackson R.D., Teillet P.M., Slater P.N., et al.. Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery[J]. Remote Sens. Environ. 1990,32:189-202
70． Jacquemoud S., Baret F. PROSPECT: A model of leaf optical properties spectra[J]. Remote Sens.Environ 1990,34:75-91
71． 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
72． Jacquemoud S.. Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study. Remote Sens. Environ.. 1993,44:281-292
73． Jacquemoud S.,Verdebout J.,Schmuck G. Investigation of leaf biochemistry by statistics[J]. Remote Sens. Environ. 1995,54:180-188
74． Jupp D.I.B., Strahler A.H. A Hotspot Model for Leaf Canopies[J]. Remote Sens. Environ. 1991,38:193-210
75． Kaufman Y., Gao B.. Remote sensing of water vapor in the near-IR from EOS/MODIS[J]. IEEE Trans. Geosci. Remote Sens.. 1992,30:871-884
76． Kimes D., Ranson J., Sun G. Inversion of a forest backscattering model using neural networks[J]. Int. J. Remote Sens. Environ.. 1997,18:2181-2199
77． Kimes D.S.. Dynamics of directional reflectance factor distributions for vegetation canopies[J]. Applied optics. 1993,22(9) :1364-1372
78． Kimes D.S., Newcomb W.W., Tucker C.J., et al.. Directional reflectance factor distribution for cover types of Northern Africa[J]. Remote Sens. Environ.
1985,18:1-19
79． Kirchner J.A., Kimes, Mcmurtrey J.E.. III. Variation of directional reflectance factors with structural changes of a developing alfalfa canopy[J]. Applied optics. 1982,21(20) :3766-3774
80． Knyazikhin Y.V., Marshak A.L., Myneni R.B.. Interaction of photons in a canopy of finite-dimensional leaves[J]. Remote Sens. Environ. 1992,39:61-74
81． Knyazikhin Y.V., Martonchik J., Diner D., et al.. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmospherically-corrected MISR data[J]. J. Geophys. Res.. 1998,103:32239-32256
82． Knyazikhin Y.V., Martonchik J., Diner D., et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data[J]. J. Geophys. Res.. 1998,103:32257-32275
83． Kropff M., Van J., Ten Berge H.F.M.. ORYZAI a basic model for irrigated rice production[M] .IRRI,Philippines, 1993,89
84． Kuusk A..A Multispectral Canopy Reflectance Model[J]. Remote Sens. Environ. 1994,50:75-82
85． Kuusk A..A Fast Invertible Canopy Reflectance Model[J]. Remote Sens. Environ. 1995,51:342-350
86． Kuusk A.. Determination of Vegetation Canopy Parameters from Optical Measurements[J]. Remote Sens. Environ. 1991,37:207-218
87． Kuusk A.. The angular distribution of reflectance and vegetation indices in barley and clover canopies[J]. Remote Sens. Environ. 1991,37:143-151
88． Leory M., Roujean J.L.. Sun and view angle corrections on reflectances derived
from NOAA/AVHRR data in the vivible band[J]. Int. J. Remote Sens.. 1994,32:684-697
89． Liang S., Strahler A.H.. The calculation of the radiance distribution of the coupled atmosphere-canopy [J]. IEEE Trans. Geos. Remote Sens. 1993,31:491-502
90． Liang S., Strahler A.H.. An analytic BRDF model of canopy radiative transfer and its inversion[J].IEEE Trans. Geos. Remote Sens. 1993,31:1081-1092
91． Grant L. Diffuse and specular characteristics of leaf reflectance [J]. Remote Sens. Environ, 1987,22:309-322
92． Liang S., Strahler A.H.. Four-stream solution for atmospheric radiative transfer over an non-lambertian surface[J].Appl. Opt.. 1994,33:5745-5753
93． Major D.J., Schaalje G.B., Wiegand C., et al.. Accuracy and sensitivity analyses of SAIL model-predicted reflectance of maize[J]. Remote Sens. Environ, 1992,41:61-70
94． Myneni R.B., Ross J., Asrar G. A review on the theory of photon transport in lesf canopies [J].Agric. For. Meteorol. 1989,45:1-153
95． Myneni R.B., Asrar G. Photon Interaction Cross Sections for Aggregations of Finite-Dimensional Leaves[J], Remote Sens. Environ. 1991,37:219-224
96． Nilson T., Kuusk A.. A Reflectance Model for the Homogeneous Plant Canopy and its Inversion[J]. Remote Sens. Environ. 1989,27:157-167
97． Norman J.M., Wells J.M., Walter E.A.. Contrasts among bidirectional reflectance of leaves, canopies, and soils[J]. IEEE Trans. on Geosci. Remote Sensing. 1985,GE-23:659-688
98． North P.. Three dimensional forest light interaction model using a monte carlo method[J], IEEE Trans. on Geosci. Remote Sensing.. 1996,34:946-956
99． Otterman J.. Inferring Parameters for Canopies Nonuniform in Azimuth by Model Inversion[J]. Remote Sens. Environ. 1990,33:940-943
100． Patrice B., Marc L.. A method of biophysical parameter retrieval at global scale by inversion of a vegetation reflectance model [J]. Remote Sens. Environ. 1999,
101． Pinty B., Verstraete M.M., Dickinson R.E.. A physcial model of the bidirectional reflectance of vegetation canopies.2． inversion and validation[J]. J. Geophys. Res. 1990,95(D8) : 11767-11775
102． Pinty B., Verstraete M.M.. Extracting information on surface properties from bi-directional reflectance measurements [J], J.Geophys.Res. 1991, 96(D2) : 2865-2874
103． Press W., Teukolsky S., Vetterling W., et al. Numerical recipes in C: The art of scientific computing[M]. Second edition, Cambridge University Press. 1996．
104． Qi J., Cabot F. , Moran M.S.,et al.. Biophysical parameter estimations using multidirectional spectral measurements [J]. Remote Sens. Environ. 1995,54:71-83
105． Qi J., Huete A.R., Moran M.S., et al.. Interpretation of vegetation indices derived from multi-temporal SPOT images[J], Remote Sens. Environ. 1993,44:89-101
106． 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. Remote Sens. Environ, 2000,73:18-30
107． Qin W.H.. Modeling Bidirectional Reflectance of Multicomponent Vegetation Canopies[J]. Remote Sens. Environ. 1993,46:235-245
108． Qin W.H., Jupp D.L.B.. An analytical and computationally efficient reflectance model for leaf canopies [J]. Agric. For. Meteorol. 1993,66(1-2) :31-64
109． Qin W.H.,Xiang Y.Q.. On the hotspot effect of leaf canopies: modeling study and influence of leaf shap. Remote Sens. Environ,1994,50:95-106
110． Raham H., Pinty B., Verstraete M.M.. A coupled surface-atmosphere reflectance(CSAR)model. Part 1:Model description and inversion on synthetic data[J]. J.Geophys. Res. 98:20779-20789
111． Ranson K. J., Biehl L. L., Bauer M. E.. Variation in spectral response of Soybeans with respect to illumination, view and canopy geometry [J]. Int. J. Remote Sens.. 1985,6:1827-1842,
112． Ranson K.J., Daughtry C.S.T. , Biehl L.L.et al.. Sun-view angle effects on reflectance factors of corn canopies[J]. Remote Sens. Environ. 1985,18:147-161
113． Ranson K.J., Irons J.R., Williams D.L.. Multispectral bidirectional reflectance of northern forest canopies with the advanced solid-state array spectroradiometer(ASAS)[J]. Remote Sens. Environ. 1994,47:276-289
114． Ritchie J. T.. IBSNAT and the CERES-Rice model[M], Weather and rice, LRRI.1987
115． Rosemary A.J., Mark E.J., Paul J.C.. Estimeting canopy chlorophyll concentration from field and airborn spectra. Remote Sens. Environ.. 1999,68:217-224
116． Ross J., Nilson T. Radiation regime and architecture of plant stands[J]. The hague:Dr.W. Junk Publishers. 1981,391pp
117． Shibayama M., Wiegand C.L.. View azimuth and zenith, and solar angle effects on wheat canopy reflectance [J]. Remote Sens. Environ.. 1985,18:91-103
118． Strahler A.H., Jupp D.L.B.. Modeling directional reflectance of forests and woodland using boolean models and geometric optics [J]. Remote Sens. Environ.. 1990,34:153-166
119． Shibayama M., Wiegand C.L..View azimuth and zenith, and solar angle effects on wheat canopy reflectance[J]. Remote Sens. Environ. 1985,18:91-103
120． Stephen J.H., Andrew R.H., Malcolm T.. Assessment of Biophysical VEGETATION Properties through Spectral Decomposition Techniques[J]. Remote Sens. Environ. 1996,56:203-214
121． Strahler A.H.. Vegetation canopy reflectance modeling__recent developments and remote sensing perspectives. Remote Sens. Review. 1997,15:179-194
122． Strahler A.M., Jupp D.L.B.. Modeling Directional Reflectance of Forests and Woolean Models and Geometric Optics[J]. Remote Sens. Environ. 1990,34:153-166
123． Suits G.H. The calculation of the directional reflectance of vegetative canopy[J]. Remote Sens. Environ. 1972,2:117-125
124． Vanderbilt V.C., Grant L. Plant canopy specular reflectance model[J]. IEEE Trans. on Geosci. Remote Sensing. 1985,GE-23:722-730
125． Verbrugghe M., Cierniewski J.. Effects of sun and view geometries on cotton bidirectional reflectance test of a geometrical model[J]. Remote Sens. Environ. 1995,54:189-197
126． Verhoef W.. Light Scattering by Leaf Layers with Application to Canopy Reflectane Modeling:the SAIL Model[J]. Remote Sens. Environ. 1984,16:125-141
127． Verstraete M.M., Pinty B., Dickinson R.E.. A physical model of the bidirectance of vegetation canopies. I. Theory[J]. J. Geophys. Res. 1990, 95(D8) : 11755-11765
128． Walthall C.L., Norman J.M., Well J.M., et al. Simple equation to approximate the bidirectional reflectance from vegetative canopies and bare soil surfaces [J].
Appl. Opt. 1985,24:383-387
129． Wooley J.T..Reflectance and transmittance of light by leaves[J]. Plant physiol. 1971,47:656-662．
130． Xue Y. Cracknell A.P.. Operational bi-angle approach to retrieve the earth surface albedo from AVHRR data in the visible band[J]. Int. J. Remote Sens, 1995,16:417-429
131． Yoder B.J., Crosby E.P.. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra(400-2500nm) at leaf and canopy scales. Remote Sens. Environ, 1995,53:199-211
2． 高亮之，金之庆，黄耀等．水稻计算机模拟模型及其应用之一 水稻钟模型——水稻发育动态的计算机模型[J]．中国农业气象．1989，10(3)：3～10
3． 高亮之，黄耀，金之庆．水稻计算机模拟模型及其应用之二 水稻最适群体动态的决策模型[J]．中国农业气象．1989，10(4)：1～6
4． 黄耀，高亮之，金之庆．水稻计算机模拟模型及其应用之三 水稻群体光合生产的动态模拟模型[J]．中国农业气象．1990，11(1)：10～15
5． 黄耀，高亮之，李林．水稻计算机模拟模型及其应用之四 长江中下游水稻生产的最适季节与光合产量[J]．中国农业气象．1990，11(1)：16～21
6． 金之庆，高亮之，李林等．水稻计算机模拟模型及其应用之五 估算水稻产量成分和库产量的模拟模型[J]．中国农业气象．1990，11(2)：20～26
7． 金之庆，高亮之，黄耀等．水稻计算机模拟模型及其应用之六 长江流域水稻穗粒结构的气候生态特征及库源关系协调性分析[J]．中国农业气象．1990，11(2)：27～34
8． 国家气象局．农业气象观测规范(上卷)[M]，气象出版社．1993,212pp
9． 何光渝．《FORTRAN 77 算法手册》[M]．北京：科学技术出版社．1993，813pp
10．黄策等．水稻群体物质生产过程的计算机模拟[J]．作物学报，1986，12(1)：1-8
11．黄洪峰．土壤植物大气相互作用原理及模拟研究[M]．北京：气象出版社，1997，1～43
12．李春强．水稻生长模拟模型研究综述[J]．中国农业气象．1992，13(6)
13．李小文．植被光学遥感模型与植被结构参数化[M]．科学出版社．118pp
14．李小文，A．Strahler，朱启疆等，地物二向性反射几何光学模型和观测的进展[J]．国土资源遥感．1991，7(1)：9～19
15．牛铮．植被二向反射特性研究新进展．遥感技术与应用[J]．1997，12(3)：49～57
16．覃文汉．遥感植被双向反射光谱的理论研究与应用展望．环境遥感[J]．1992，7(4)：290-299
17．户刈义次主编．作物的光合作用与物质生产[M]．北京：科学出版社．1981，17～53
18．P．S．Nobell，S．P．Long(1986)．冠层结构和光的截获[M]．J．库姆斯等主编．北京：科学出版社．45～48
19．上海师范大学生物系，上海农业学校．水稻栽培生理[M]．上海：上海科学技术出版社．1983，450pp
20．沈掌泉．水稻冠层反射特性的动态变化研究[J]． 国土资源遥感，1996,4：40～44
21．上海植物生理学会编．植物生理学实验手册[M]．上海：上海科学技术出版社．1985，644pp
22．谈小生，葛成辉．太阳角的计算方法及其在遥感中的应用[J]．国土资源遥感．1995，24(2)：48～56
23．王炳忠．太阳辐射能的测量与标准[M]．北京：科学出版社．1993，37～39
24．王人潮，陈铭臻，蒋亨显．水稻遥感的农学机理研究 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．1993，19(Sup)：7～14
25．王人潮，陈铭臻，蒋亨显．水稻遥感的农学机理研究 Ⅱ．农学参数与光谱变量的相关分析[J]．浙江农业大学学报．1993，19(Sup)：15～22
26．吴永莲．气象学基础[M]．北京：北京师范大学出版社．1987，13～32
27．项月琴，周允华，崔景芳．冬小麦群丛对总辐射截获的测量．中国科学院北京农业生态系统试验站．农田作物环境实验研究[M]．北京：气象出版社．1990，104～115
28．项月琴，田国良．遥感估算水稻产量 Ⅰ、产量与辐射截获量间关系的研究[J]．环境遥感．1988，3(4)，308～316
29．解可心，韩立兴，林友联著．最优化方法[M]，天津大学出版社．1997，321pp
30．徐正进，董克，水稻叶片基角、开张角和披垂度的同时测量方法．沈阳农业大学学报．1991，185～187
31．周启发，王人潮．水稻氮素营养水平与光谱特性的关系[J]．浙江农业大学学报．1993，19(sup．)，40～45
32．张仁华．实验遥感模型及地面基础，科学出版社[M]．1996，129-147
33. Ahmad S.P., Derring D.W. A simple analytical function for bidirectional reflectance[J]. J. Goephys. Res. Atmos. 1992,97(D17): 18867～18886
34. Anderssen R.S., Jackeet D.R.,Jupp,L.B., et al. Interpretation of a simple formulas for some key linear functionals of the foliage angle distribution[J]. Agric. Forest Meteorol. 1985,36:165～188
35. Antyufeev V.S., Marshak A.L..Monte carlo methed and transfer equation in plant canopies[J]. Remote Sens. Environ. 1990,31:183～191
36. Asrar G..,Kanemasu E.R.,Yoshida M..Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle[J]. Remote Sens. Environ. 1985,17:1～11
37. Baret F.,Strahler A.H.,Morris K.,et al..The robustness of canopy gap fraction estimates from red and near-infrared reflectances:A comparison of approaches[J]. Remote Sens. Environ. 1995,54:141～151
38. Bourages B..Technical note improvement in solar declination computation[J]. Solar Energy. 1985,35(4):367～369
39. Brakke T.W.,Otterman J..Canopy bidiretional reflectance dependence on leaf
orientation[J]. Int. J. Remote Sens.. 1990,11:1023-1032
40． Breece H.T., Holmes R.A.. Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo [J]. Appl. Opt. 1971,10:119-127
41． Campbell G.S.. Extintiction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution[J], Agricultural and forest meteorology.1986,36:317-321
42． Cihlar J., Manak D., Voisin N. AVHRR bidirectional reflectance effects and compositing[J]. Remote Sens. Environ.. 1994,48:77-88
43． Clevers J.G.P.W., The derivation of a simplified reflectance model for the estimation of leaf area index[J]. Remote Sens. Environ. 1988,295(1) :53-70
44． Clevers J.G.P.W.. The application of a weighted infrared-red vegetation index for estimating leaf area index[J]. Remote Sens. Environ. 1988,295(3) :47-55
45． Copper K., Smith J.A., Pitts,D.. Reflectance of a vegetation canopy using the adding methed[J]. Appl. Opt..1982,21:4112-4118
46． Current P.J., Dungan, J.L., Gholz,H.L.. Seasonal LAI of slash pine estimated with Landsat TM[J]. Remote Sens. Environ.1992, 39:1-13
47． Dawson T., Curran P., Plummer S.. LIBERTY:modeling the effects of leaf biochemistry on reflectance spectra[J]. Remote Sens. Environ. 1998,65:50-60
48． Deering D.W.. Field measurements of bidirectional reflectance, in theory and applications of optical remote sensing[J]. G.Asrar, Ed. 1989,14-65
49． Deering D.W., Leone P. A Sphere-scanning Radiometer for Rapid Directional Measurments of Sky and Ground Radiance [J]. Remote Sens. Environ.. 1986,19:1-24
50． Demarez V, Gastellu-Etchegorry J.P., Mougin E., et al.. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT
model. Int. J. Remote Sens. Environ.. 1999,20(5) :879-894
51． Dubayah R., Loechel S.. Modeling topographic solar radiation using GOES data[J]. J.Appl. Meteorol. 1997,36:141-154
52． Duchemin B.. NOAA/AVHRR bidirectional reflectance: modeling and application for the monitoring of a temperate forest. Remote Sens. Environ.. 1999,67:51-67
53． Eaton F.D., Dirmhirm I.. Reflected irradiances of natural surfaces and their effects on albedo [J].Appl. Opt.. 1977,18:994-1008
54． Francoise N., Francois P., Marc R S.. Bidirectional Reflectivity in AVHRR Channel 3:Application to a Region in Northern Africa[J]. Remote Sens. Environ. 1998,66:298-316
55． Ganapol B.D., Myneni R.B.. The FN methed for the one-angle radiative transfer equation applied to plant canopies[J]. Remote Sens. Environ. 1992,39:213-231
56． Gerard F.F., North P.R.J.. Analyzing the Effect of Structural Variability and Canopy Gaps on Forest BRDF Using a GEOMETRIC-Optical Model[J]. Remote Sens. Environ. 1997,62:46-62
57． Goel N. S., Dearing D. W.. Evaluation of a canopy reflectance model for LAI estimation through its inversion[J]. IEEE tranctions on geoscience and remote sensing. 1985,23(5) :674-683．
58． Goel N.S.. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data[J]. Remote sensing review. 1988,4,1-212
59． Goels N.S., Grier T.. Estimation of canopy parameters from inhomogeneous vegetation canopies from reflectance data,II:Estimation of leaf area index and percentage of ground cover for row canopies [J]. Int. J. Remote Sens.
Environ.. 1986,7:1263-1286
60． Goels N.S., Grier T.. Estimation of canopy parameters from inhomogeneous vegetation canopies from reflectance data,III:TRIM: a model for radiative transfer in heterogeneous three-dimensional canopies[J].. Remote Sens. Environ. 1988,25:255-293
61． Goels N.S., Rozehnal I., Thompson R.L., A computer graphics based model for scattering from objects of arbitrary shapes in the optical region. Remote Sens. Environ. 1991,36:73-104
62． Goel N.S., Strebel D.E. Inversion of vegetation canopy reflectance models for estimating agronomic variables. I. Problem definition and initial results using the suits model[J]. Remote Sens. Environ. 1983,13:487-507
63． Goel N.S., Strebel D.E. Inversion of vegetation canopy reflectance models for estimating agronomic variables, IV:Total inversion of the SAIL model[J]. Remote Sens. Environ. 1984,15:237-253
64． Goel N.S., Thompson R.L.. Inversion of vegetation canopy reflectance models for estimating agronomic variables, V : Estimation of LAI and average leaf angle using measured canopy reflectances[J]. Remote Sens. Environ. 1984,16:69-85
65． Gong P., Wang D. Liang S.. Inverting a canopy reflectance model using an artificial neural network[J]. Int. J. Remote Sens. Environ.. 1999,20(1) : 111-122
66． Hapke B.. Bidirectional reflectance spectroscopy. I. Theory[J], J. Geophys. Res. 86(B4) :3039-3054
67． Hatfield J.L., Kanemasu E.T., Asrar G, et al. Leaf-area estimates from spectral measurements over various planting dates of wheat[J]. Int. J. Remote Sens. 1985,6(1) :267-175
68． Irons J.R., Banson K.J., Williams D.L., et al.. An off-nadir-pointing imaging
spectroradiometer for terrestrial ecosystem studies[J].IEEE Trans Remote Sens.. 1991,29:66-74
69． Jackson R.D., Teillet P.M., Slater P.N., et al.. Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery[J]. Remote Sens. Environ. 1990,32:189-202
70． Jacquemoud S., Baret F. PROSPECT: A model of leaf optical properties spectra[J]. Remote Sens.Environ 1990,34:75-91
71． 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
72． Jacquemoud S.. Inversion of the PROSPECT+SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study. Remote Sens. Environ.. 1993,44:281-292
73． Jacquemoud S.,Verdebout J.,Schmuck G. Investigation of leaf biochemistry by statistics[J]. Remote Sens. Environ. 1995,54:180-188
74． Jupp D.I.B., Strahler A.H. A Hotspot Model for Leaf Canopies[J]. Remote Sens. Environ. 1991,38:193-210
75． Kaufman Y., Gao B.. Remote sensing of water vapor in the near-IR from EOS/MODIS[J]. IEEE Trans. Geosci. Remote Sens.. 1992,30:871-884
76． Kimes D., Ranson J., Sun G. Inversion of a forest backscattering model using neural networks[J]. Int. J. Remote Sens. Environ.. 1997,18:2181-2199
77． Kimes D.S.. Dynamics of directional reflectance factor distributions for vegetation canopies[J]. Applied optics. 1993,22(9) :1364-1372
78． Kimes D.S., Newcomb W.W., Tucker C.J., et al.. Directional reflectance factor distribution for cover types of Northern Africa[J]. Remote Sens. Environ.
1985,18:1-19
79． Kirchner J.A., Kimes, Mcmurtrey J.E.. III. Variation of directional reflectance factors with structural changes of a developing alfalfa canopy[J]. Applied optics. 1982,21(20) :3766-3774
80． Knyazikhin Y.V., Marshak A.L., Myneni R.B.. Interaction of photons in a canopy of finite-dimensional leaves[J]. Remote Sens. Environ. 1992,39:61-74
81． Knyazikhin Y.V., Martonchik J., Diner D., et al.. Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmospherically-corrected MISR data[J]. J. Geophys. Res.. 1998,103:32239-32256
82． Knyazikhin Y.V., Martonchik J., Diner D., et al. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data[J]. J. Geophys. Res.. 1998,103:32257-32275
83． Kropff M., Van J., Ten Berge H.F.M.. ORYZAI a basic model for irrigated rice production[M] .IRRI,Philippines, 1993,89
84． Kuusk A..A Multispectral Canopy Reflectance Model[J]. Remote Sens. Environ. 1994,50:75-82
85． Kuusk A..A Fast Invertible Canopy Reflectance Model[J]. Remote Sens. Environ. 1995,51:342-350
86． Kuusk A.. Determination of Vegetation Canopy Parameters from Optical Measurements[J]. Remote Sens. Environ. 1991,37:207-218
87． Kuusk A.. The angular distribution of reflectance and vegetation indices in barley and clover canopies[J]. Remote Sens. Environ. 1991,37:143-151
88． Leory M., Roujean J.L.. Sun and view angle corrections on reflectances derived
from NOAA/AVHRR data in the vivible band[J]. Int. J. Remote Sens.. 1994,32:684-697
89． Liang S., Strahler A.H.. The calculation of the radiance distribution of the coupled atmosphere-canopy [J]. IEEE Trans. Geos. Remote Sens. 1993,31:491-502
90． Liang S., Strahler A.H.. An analytic BRDF model of canopy radiative transfer and its inversion[J].IEEE Trans. Geos. Remote Sens. 1993,31:1081-1092
91． Grant L. Diffuse and specular characteristics of leaf reflectance [J]. Remote Sens. Environ, 1987,22:309-322
92． Liang S., Strahler A.H.. Four-stream solution for atmospheric radiative transfer over an non-lambertian surface[J].Appl. Opt.. 1994,33:5745-5753
93． Major D.J., Schaalje G.B., Wiegand C., et al.. Accuracy and sensitivity analyses of SAIL model-predicted reflectance of maize[J]. Remote Sens. Environ, 1992,41:61-70
94． Myneni R.B., Ross J., Asrar G. A review on the theory of photon transport in lesf canopies [J].Agric. For. Meteorol. 1989,45:1-153
95． Myneni R.B., Asrar G. Photon Interaction Cross Sections for Aggregations of Finite-Dimensional Leaves[J], Remote Sens. Environ. 1991,37:219-224
96． Nilson T., Kuusk A.. A Reflectance Model for the Homogeneous Plant Canopy and its Inversion[J]. Remote Sens. Environ. 1989,27:157-167
97． Norman J.M., Wells J.M., Walter E.A.. Contrasts among bidirectional reflectance of leaves, canopies, and soils[J]. IEEE Trans. on Geosci. Remote Sensing. 1985,GE-23:659-688
98． North P.. Three dimensional forest light interaction model using a monte carlo method[J], IEEE Trans. on Geosci. Remote Sensing.. 1996,34:946-956
99． Otterman J.. Inferring Parameters for Canopies Nonuniform in Azimuth by Model Inversion[J]. Remote Sens. Environ. 1990,33:940-943
100． Patrice B., Marc L.. A method of biophysical parameter retrieval at global scale by inversion of a vegetation reflectance model [J]. Remote Sens. Environ. 1999,
101． Pinty B., Verstraete M.M., Dickinson R.E.. A physcial model of the bidirectional reflectance of vegetation canopies.2． inversion and validation[J]. J. Geophys. Res. 1990,95(D8) : 11767-11775
102． Pinty B., Verstraete M.M.. Extracting information on surface properties from bi-directional reflectance measurements [J], J.Geophys.Res. 1991, 96(D2) : 2865-2874
103． Press W., Teukolsky S., Vetterling W., et al. Numerical recipes in C: The art of scientific computing[M]. Second edition, Cambridge University Press. 1996．
104． Qi J., Cabot F. , Moran M.S.,et al.. Biophysical parameter estimations using multidirectional spectral measurements [J]. Remote Sens. Environ. 1995,54:71-83
105． Qi J., Huete A.R., Moran M.S., et al.. Interpretation of vegetation indices derived from multi-temporal SPOT images[J], Remote Sens. Environ. 1993,44:89-101
106． 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. Remote Sens. Environ, 2000,73:18-30
107． Qin W.H.. Modeling Bidirectional Reflectance of Multicomponent Vegetation Canopies[J]. Remote Sens. Environ. 1993,46:235-245
108． Qin W.H., Jupp D.L.B.. An analytical and computationally efficient reflectance model for leaf canopies [J]. Agric. For. Meteorol. 1993,66(1-2) :31-64
109． Qin W.H.,Xiang Y.Q.. On the hotspot effect of leaf canopies: modeling study and influence of leaf shap. Remote Sens. Environ,1994,50:95-106
110． Raham H., Pinty B., Verstraete M.M.. A coupled surface-atmosphere reflectance(CSAR)model. Part 1:Model description and inversion on synthetic data[J]. J.Geophys. Res. 98:20779-20789
111． Ranson K. J., Biehl L. L., Bauer M. E.. Variation in spectral response of Soybeans with respect to illumination, view and canopy geometry [J]. Int. J. Remote Sens.. 1985,6:1827-1842,
112． Ranson K.J., Daughtry C.S.T. , Biehl L.L.et al.. Sun-view angle effects on reflectance factors of corn canopies[J]. Remote Sens. Environ. 1985,18:147-161
113． Ranson K.J., Irons J.R., Williams D.L.. Multispectral bidirectional reflectance of northern forest canopies with the advanced solid-state array spectroradiometer(ASAS)[J]. Remote Sens. Environ. 1994,47:276-289
114． Ritchie J. T.. IBSNAT and the CERES-Rice model[M], Weather and rice, LRRI.1987
115． Rosemary A.J., Mark E.J., Paul J.C.. Estimeting canopy chlorophyll concentration from field and airborn spectra. Remote Sens. Environ.. 1999,68:217-224
116． Ross J., Nilson T. Radiation regime and architecture of plant stands[J]. The hague:Dr.W. Junk Publishers. 1981,391pp
117． Shibayama M., Wiegand C.L.. View azimuth and zenith, and solar angle effects on wheat canopy reflectance [J]. Remote Sens. Environ.. 1985,18:91-103
118． Strahler A.H., Jupp D.L.B.. Modeling directional reflectance of forests and woodland using boolean models and geometric optics [J]. Remote Sens. Environ.. 1990,34:153-166
119． Shibayama M., Wiegand C.L..View azimuth and zenith, and solar angle effects on wheat canopy reflectance[J]. Remote Sens. Environ. 1985,18:91-103
120． Stephen J.H., Andrew R.H., Malcolm T.. Assessment of Biophysical VEGETATION Properties through Spectral Decomposition Techniques[J]. Remote Sens. Environ. 1996,56:203-214
121． Strahler A.H.. Vegetation canopy reflectance modeling__recent developments and remote sensing perspectives. Remote Sens. Review. 1997,15:179-194
122． Strahler A.M., Jupp D.L.B.. Modeling Directional Reflectance of Forests and Woolean Models and Geometric Optics[J]. Remote Sens. Environ. 1990,34:153-166
123． Suits G.H. The calculation of the directional reflectance of vegetative canopy[J]. Remote Sens. Environ. 1972,2:117-125
124． Vanderbilt V.C., Grant L. Plant canopy specular reflectance model[J]. IEEE Trans. on Geosci. Remote Sensing. 1985,GE-23:722-730
125． Verbrugghe M., Cierniewski J.. Effects of sun and view geometries on cotton bidirectional reflectance test of a geometrical model[J]. Remote Sens. Environ. 1995,54:189-197
126． Verhoef W.. Light Scattering by Leaf Layers with Application to Canopy Reflectane Modeling:the SAIL Model[J]. Remote Sens. Environ. 1984,16:125-141
127． Verstraete M.M., Pinty B., Dickinson R.E.. A physical model of the bidirectance of vegetation canopies. I. Theory[J]. J. Geophys. Res. 1990, 95(D8) : 11755-11765
128． Walthall C.L., Norman J.M., Well J.M., et al. Simple equation to approximate the bidirectional reflectance from vegetative canopies and bare soil surfaces [J].
Appl. Opt. 1985,24:383-387
129． Wooley J.T..Reflectance and transmittance of light by leaves[J]. Plant physiol. 1971,47:656-662．
130． Xue Y. Cracknell A.P.. Operational bi-angle approach to retrieve the earth surface albedo from AVHRR data in the visible band[J]. Int. J. Remote Sens, 1995,16:417-429
131． Yoder B.J., Crosby E.P.. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra(400-2500nm) at leaf and canopy scales. Remote Sens. Environ, 1995,53:199-211