地表热红外辐射背景场建模与成像模拟研究
|
摘要
地表热辐射场景生成机理与遥感成像过程的模拟分析一直是热红外遥感领域高度关注的研究热点。新型航天红外遥感成像系统研发与设计需要大量的前期论证,传感器参数的设计离不开多种场景、成像条件下红外辐射场景特性分析及所成图像的评估,这就需要对热红外遥感过程进行模拟。同时,正确认识和建立热红外遥感前向模型也是进行地表温度反演的基础,通过研究地表能量的产生机理和变化规律,以及成像过程对红外影像的影响,可以为提高地表温度反演精度提供参考依据和解决途径。
本文以地表热辐射的产生、传递为主线,以热辐射动态场景的建模、分析为重点,围绕热红外遥感成像过程链路进行了系统的研究。首先结合一维地表导热方程与地表能量平衡模型,模拟植被、裸土、柏油路面和混凝土地面四种典型地表组份热辐射日变化规律；在此基础上,利用随机分形的思想建立三维地形场景,基于能量线性混合的假设,以高空间分辨率光学遥感图像(IKONOS、AVIRIS)丰度分解为基础,构建了5m/10m分辨率的热辐射背景场,并结合辐射换热理论探索表面间能量的交换,解析地描述了表面间多次散射引起的目标有效辐射增量,并建立多次散射模型MSM(Multi-scattering Model)以分析目标红外辐射特性；基于热辐射大气传输模型和湍流MTF模型分析大气对地表热辐射场景的影响,最后根据能量传递的物理过程模拟红外传感器对地表热辐射场景的响应。
通过本文的研究,得到以下主要结论和认识：
(1)地表温度日变化规律受物侯特性影响,且与近地表气象条件相关
二次谐波形式可以较好地表达裸露地表温度日变化特征,模拟值与实测值最大标准偏差在3℃以内,误差主要来源于地表热力学参数测量、估算方面。裸露地表的导热系数越低,昼夜温度起伏越大；对流换热系数越小,温度出现峰值的时刻越接近太阳辐照度峰值时刻；对于植被冠层,白天温度还受叶面积指数的影响。近地面大气相对湿度、温度与地表温度之间具有较高的线性关系,尤其是大气温度对地表温度很高的指示作用。植被和水体温度在昼夜周期内的变化相对平缓,裸露地表日温度变化相对剧烈,且受入射辐射影响较大；日出前和日落后一段时间内各类地表温度差异最小,正午前后一段时间内温度差异最大。
(2)地表热辐射场景随地表温度、类型分布以及观测方位产生动态变化
基于分形地形和地表丰度/覆盖度数据,结合地物温度与发射率特性,可简单有效地模拟出地表热辐射场景分布与方向亮温变化,且不丧失地表纹理细节信息MSM模型考虑了像元组分间多次散射辐射的影响,可提高混合像元有效辐射的模拟精度,亦可用来定义非同温混合元的有效发射率。地表热辐射场景细节丰富程度以及亮温方向特性均随时间发生变化：温差越大时地物纹理越清晰,场景亮温方向特性越明显。地表单元的有效辐射与单元内组分温度、材质、几何结构以以及组分细分程度等有关,并随观测角度而发生改变；组分间的多次散射增强了地表有效辐射,但同时又对辐射方向特性产生平滑作用；大气下行辐射对地表辐射方向特性无明显影响,但增加了像元的有效辐射,使像元亮温更接近表面温度。
(3)大气对地表热辐射场景产生模糊作用
大气辐射与消光作用削弱了传感器入瞳处地表热辐射场景的动态范围,大气湍流使热辐射传递路径发生抖动,进一步模糊了传感器观测到的热辐射影像,且空间分辨率越高,大气湍流对影像的模糊作用越强。夜间条件下大气辐射一般为正效应,增强了地表辐射表观辐亮度,而白天时大气一般起负作用。大气水汽、观测角度、通道设置以及地表比辐射率是地表热辐射场景大气作用模拟需要重视的关键参量,而对大气湍流作用的模拟需要注意垂直廓线方向上湍流折射结构系数变化。
(4)场景热辐射信号经探测器系统成像被进一步退化
增大红外相机的孔径可以增加探测器焦平面上聚集到的能量,有效降低光学系统衍射造成的模糊效应；探测器对辐射能量的响应受积分时间、平台振动的干扰,使得获得的图像质量进一步退化；噪声产生于成像系统整个过程中,对于集成度较高的当代红外遥感成像系统,可以用等效噪声温差来表征系统的噪声水平系统产生噪声对低温条件下成像的图像质量的影响显著。
The modeling of land surface thermal radiance scene and the simulation of the remote sensing imaging procedure play an important role in the studies of thermal remote sensing fields. Recently, with active renewals for remote sensors, the application fields meet more actuate observation precision for the Earth land. In this situation, the sensor designers need to develop various thermal imaging systems to meet the different and complex land radiance conditions, and the plentiful early demonstrations of the new remote sensing system are desired. The definitions of the sensors'parameters depend on various evaluations for the land surface radiance and imaging characteristics in different natural conditions, which require lots of field experiments. An economic and effective way is computer modeling and simulation. Meanwhile, with the correct understanding of the origin, reactive, and atmospheric transfer of surface energy flux senses, and the systematic analysis of the factors affecting the final thermal imagery, it is useful to improve the accuracy of land surface temperatures retrieval from thermal remotely sensed imageries.
This research is made around the whole link of the thermal remote sense imaging system, and the main purpose of this paper is to investigate the thermal radiant characteristics of the typical land surface and to analyze the main factors for thermal remote sense imaging procedures. The main line of this paper is the origin and transfer of the land surface energy flux, where the focal points are the modeling and analyzing of the dynamic thermal radiance scenes.
Assuming that the land surface background is composed by 5 typical components, including the vegetation canopy, the bare soil, the water body, the asphalt pavement and the concrete surface. Land surface temperatures for different land covers are the key parameters for land thermal radiance scenes modeling. Firstly, the temperature daily variations for four typical solid surfaces are modeled and analyzed, which are the vegetation canopy, the bare soil, the asphalt pavement and the concrete surface. The harmonic model is built to simulate the daily change of the temperature for bare soil, asphalt pavement and concrete surface, and a model of SVAT (soil-vegetation-atmosphere transfer), CUPID, is employed to simulate the vegetation canopy temperature. The biological and thermal proprieties of the foresaid four kinds of land surface are measured or evaluated, which are then used as inputs of the temperature models respectively, and 24-hour observations of the surface temperatures are carried to calibrate the modeled results. The sensitive analysis for the harmonic and CUPID model is made to exhibit the main impact factors for surface temperature.
Based on the radiation heat exchange equation over surfaces and the linear-energy-mixed theory, the thermal radiance of land scenes with 5m/10 m spatial resolutions are modeled. The main inputs of the model are surface temperature, emissivity and spatial distribution of the components. The fractal-based method is applied to simulate the 3-Dimentional natural terrain; the proportion of each component is evaluated or extracted from multi/hyper-spectral optical remote sensing imageries. Daily variation of the directional brightness temperatures (DBT) for a 5 m resolution scene is simulated, and the influence of downwelling atmospheric radiant to the scene DBT is analyzed.
The thermal reflectance for bare surface is not negligible during the scene radiance modeling, since the thermal radiance that emitted and reflected synchronously at each component are at the similar levels.. The concept of configuration factor is applied, and the multiple scattering effects between heterogeneous non-isothermal surfaces are described rigorously, based on which a directional thermal radiance model named Multi-Scattering Model (MSM) is built, and the numerical calculation of the MSM is discussed. The MSM is applied to modeling the DBT of row crops, and the results are compared with measured DBTs. The MSM is also used to describe the effective emissivity over non-isothermal targets, and a simulation of the remotely sensed pixels with "V" structure is performed, respectively.
The atmospheric attenuation of land surface thermal radiation is simulated with 3 ways according to the abundance of the meteorological data, which is the radiative transfer method, the empirical method and the lookup table (LUT) method. The atmospheric effects simulations are carried out with a Gaussian-Triangular filter as the sensor channel response function, which takes the radiation wavelength range of 10.5μm-12.5μm. The degeneration of the land surface radiance by the atmospheric turbulence is simulated using very short exposure atmospheric modulate transfer function (MTF), and then the land scene thermal radiance at the top of the atmosphere is obtained. The physical procedure of the at-sensor radiance transfer in the thermal remote sensing camera system is analyzed briefly, and the halo and diffractive effects caused by the optical imaging system, and the image movement that is brought by the dither of the remote sensor platform are simulated. The stochastic noise of imaging system is indicated by the noise equivalent temperature difference (NETD) and the SiTF. The NETD is modeled and calculated according to the parameters of a remote sensor, and then the system noise is injected to the digital image. Finally, the response and degenerate of the radiance scene by the thermal remote sensor is simulated.
With the modeling, simulation and analysis of the thermal radiance scene and the imaging procedure, some of results and conclusions can be drawn as follows: (1) The daily variation of the land surface temperatures are fluctuated by the solar radiance and the near surface metrological conditions. Meanwhile, they are restricted by the surface thermal/biological properties.
The analytical temperature model with second harmonic terms presented in this paper can be used to predict the daily bare ground surface temperature variation with sufficient accuracy, and the result is restricted by the measurement or estimation veracity of the ground thermal properties. The solar radiance is the basic reason that makes the ground temperatures changing temporally. For bare surfaces, the daily diversity of the temperatures increases with the rise of the thermal conductivity, while the time that the peak value of the daily temperatures occursclose to midday as the convective heat transfer coefficient decreased.In addition, for vegetation canopies, the temperature in daytime is also affected by the leaf area indexes (LAI). There is a high correlation between surface and the air temperature, and a good correlation between temperatures and the relative humidity. In daily scale, the fluctuation of surface temperature is smoothly for vegetation canopy and water body, and rough for bare and dry surfaces; the minimized differs of all kinds of ground surface occurs at a short period of time before sunrise or after sundown. (2) The land radiance scene dynamically changed with the distribution of surface temperature, land cover and the observation directions.
Base on the fractal terrain simulation and proportion information extraction of surface components, the land radiance scene and the DBT variation of it can be modeled easily and effectively, which the texture details of the land radiance distribution is remained. The multi-scattering effects over components of pixels can be completely described by the MSM model, with which the accuracy for effective radiance modeling of mixed pixels is enhanced. The effective emissivity for non-isothermal targets can also be defined by the usage of MSM. The results of thermal scene modeling show that both the detailed grade of the scene texture and the DBT characteristic of the whole scene are changing with solar-time, especially in a time period after sunrise. The more acuteness of the thermal diversity over pixels, the more details of the scene radiant textures and the variations of the scene DBTs. The effective radiance of land surface takes directional property, which is correlated with the temperatures, emissivity, spatial structure and subdivision extent of the components in each pixel. The effective radiance is aggrandized because of the multi-scattering effects, whereas the change range of DBT is smoothed. At the same time; the effective radiance is also enhanced by the downward atmospheric irradiation, which makes the brightness temperature close to the surface temperature, while the influence of the atmosphere for the scene directional brightness character can be neglected. The effective emissivity is magnified by the multi-scattering effects; the distinctness of the directional effective emissivity for non-isothermal pixel increases with the rise of the diversion for the components temperatures, the isomerous and subdivide state of the substructure, and decreases with the emissivity of components. (3) The land radiance scene is fainted by the atmospheric effects.
The dynamic range of the thermal radiance scene is reduced by atmospheric extinction and radiation. The surface radiance transfer path is shuddered by the turbulent diffusion of atmosphere, and then the image that is projected to the infrared focal plane assembling (FPA) of sensor is shortly moved, which blurs the detected scene. The blurring effect drought by the atmospheric turbulence is increased with the rise of the spatial resolution for land scene and the atmospheric refractive index turbulent structure constant. The contribution of the atmospheric effects for ground radiance at TOA is positive at night while negative in daytime. Some parameters should be regarded during the simulation of atmospheric effects, which are the total water vapor contents, the observation angle, the setting of the response channels and the ground surface emissivity. (4) The land radiance scene image is deteriorated further after the response, transmission, processing of the signals by the sensor system.
With the increase of the aperture size of the infrared camera, the energy focused to the FPA increases accordingly, and the image blurring brought by diffraction of the optical system is reduced consequently. The radiance response of the sensor is disturbed by detector integral time and vibration of the platform, and the remotely sensed imagery is deteriorated. The noise originated from the entire process of the land radiance imaging can be characterized by the NETD for highly integrated thermal infrared remote sensing system. With the simulation of the main steps of the sensor imaging procedures, the results show that image drift by the movement of platform is the key reason for which the image is blurred by sensor; the system noise is influenced by the ground radiant levels and the atmospheric transmittance, therefore the noise has significant effect on the image quality for night thermal scene.
本文以地表热辐射的产生、传递为主线,以热辐射动态场景的建模、分析为重点,围绕热红外遥感成像过程链路进行了系统的研究。首先结合一维地表导热方程与地表能量平衡模型,模拟植被、裸土、柏油路面和混凝土地面四种典型地表组份热辐射日变化规律；在此基础上,利用随机分形的思想建立三维地形场景,基于能量线性混合的假设,以高空间分辨率光学遥感图像(IKONOS、AVIRIS)丰度分解为基础,构建了5m/10m分辨率的热辐射背景场,并结合辐射换热理论探索表面间能量的交换,解析地描述了表面间多次散射引起的目标有效辐射增量,并建立多次散射模型MSM(Multi-scattering Model)以分析目标红外辐射特性；基于热辐射大气传输模型和湍流MTF模型分析大气对地表热辐射场景的影响,最后根据能量传递的物理过程模拟红外传感器对地表热辐射场景的响应。
通过本文的研究,得到以下主要结论和认识：
(1)地表温度日变化规律受物侯特性影响,且与近地表气象条件相关
二次谐波形式可以较好地表达裸露地表温度日变化特征,模拟值与实测值最大标准偏差在3℃以内,误差主要来源于地表热力学参数测量、估算方面。裸露地表的导热系数越低,昼夜温度起伏越大；对流换热系数越小,温度出现峰值的时刻越接近太阳辐照度峰值时刻；对于植被冠层,白天温度还受叶面积指数的影响。近地面大气相对湿度、温度与地表温度之间具有较高的线性关系,尤其是大气温度对地表温度很高的指示作用。植被和水体温度在昼夜周期内的变化相对平缓,裸露地表日温度变化相对剧烈,且受入射辐射影响较大；日出前和日落后一段时间内各类地表温度差异最小,正午前后一段时间内温度差异最大。
(2)地表热辐射场景随地表温度、类型分布以及观测方位产生动态变化
基于分形地形和地表丰度/覆盖度数据,结合地物温度与发射率特性,可简单有效地模拟出地表热辐射场景分布与方向亮温变化,且不丧失地表纹理细节信息MSM模型考虑了像元组分间多次散射辐射的影响,可提高混合像元有效辐射的模拟精度,亦可用来定义非同温混合元的有效发射率。地表热辐射场景细节丰富程度以及亮温方向特性均随时间发生变化：温差越大时地物纹理越清晰,场景亮温方向特性越明显。地表单元的有效辐射与单元内组分温度、材质、几何结构以以及组分细分程度等有关,并随观测角度而发生改变；组分间的多次散射增强了地表有效辐射,但同时又对辐射方向特性产生平滑作用；大气下行辐射对地表辐射方向特性无明显影响,但增加了像元的有效辐射,使像元亮温更接近表面温度。
(3)大气对地表热辐射场景产生模糊作用
大气辐射与消光作用削弱了传感器入瞳处地表热辐射场景的动态范围,大气湍流使热辐射传递路径发生抖动,进一步模糊了传感器观测到的热辐射影像,且空间分辨率越高,大气湍流对影像的模糊作用越强。夜间条件下大气辐射一般为正效应,增强了地表辐射表观辐亮度,而白天时大气一般起负作用。大气水汽、观测角度、通道设置以及地表比辐射率是地表热辐射场景大气作用模拟需要重视的关键参量,而对大气湍流作用的模拟需要注意垂直廓线方向上湍流折射结构系数变化。
(4)场景热辐射信号经探测器系统成像被进一步退化
增大红外相机的孔径可以增加探测器焦平面上聚集到的能量,有效降低光学系统衍射造成的模糊效应；探测器对辐射能量的响应受积分时间、平台振动的干扰,使得获得的图像质量进一步退化；噪声产生于成像系统整个过程中,对于集成度较高的当代红外遥感成像系统,可以用等效噪声温差来表征系统的噪声水平系统产生噪声对低温条件下成像的图像质量的影响显著。
The modeling of land surface thermal radiance scene and the simulation of the remote sensing imaging procedure play an important role in the studies of thermal remote sensing fields. Recently, with active renewals for remote sensors, the application fields meet more actuate observation precision for the Earth land. In this situation, the sensor designers need to develop various thermal imaging systems to meet the different and complex land radiance conditions, and the plentiful early demonstrations of the new remote sensing system are desired. The definitions of the sensors'parameters depend on various evaluations for the land surface radiance and imaging characteristics in different natural conditions, which require lots of field experiments. An economic and effective way is computer modeling and simulation. Meanwhile, with the correct understanding of the origin, reactive, and atmospheric transfer of surface energy flux senses, and the systematic analysis of the factors affecting the final thermal imagery, it is useful to improve the accuracy of land surface temperatures retrieval from thermal remotely sensed imageries.
This research is made around the whole link of the thermal remote sense imaging system, and the main purpose of this paper is to investigate the thermal radiant characteristics of the typical land surface and to analyze the main factors for thermal remote sense imaging procedures. The main line of this paper is the origin and transfer of the land surface energy flux, where the focal points are the modeling and analyzing of the dynamic thermal radiance scenes.
Assuming that the land surface background is composed by 5 typical components, including the vegetation canopy, the bare soil, the water body, the asphalt pavement and the concrete surface. Land surface temperatures for different land covers are the key parameters for land thermal radiance scenes modeling. Firstly, the temperature daily variations for four typical solid surfaces are modeled and analyzed, which are the vegetation canopy, the bare soil, the asphalt pavement and the concrete surface. The harmonic model is built to simulate the daily change of the temperature for bare soil, asphalt pavement and concrete surface, and a model of SVAT (soil-vegetation-atmosphere transfer), CUPID, is employed to simulate the vegetation canopy temperature. The biological and thermal proprieties of the foresaid four kinds of land surface are measured or evaluated, which are then used as inputs of the temperature models respectively, and 24-hour observations of the surface temperatures are carried to calibrate the modeled results. The sensitive analysis for the harmonic and CUPID model is made to exhibit the main impact factors for surface temperature.
Based on the radiation heat exchange equation over surfaces and the linear-energy-mixed theory, the thermal radiance of land scenes with 5m/10 m spatial resolutions are modeled. The main inputs of the model are surface temperature, emissivity and spatial distribution of the components. The fractal-based method is applied to simulate the 3-Dimentional natural terrain; the proportion of each component is evaluated or extracted from multi/hyper-spectral optical remote sensing imageries. Daily variation of the directional brightness temperatures (DBT) for a 5 m resolution scene is simulated, and the influence of downwelling atmospheric radiant to the scene DBT is analyzed.
The thermal reflectance for bare surface is not negligible during the scene radiance modeling, since the thermal radiance that emitted and reflected synchronously at each component are at the similar levels.. The concept of configuration factor is applied, and the multiple scattering effects between heterogeneous non-isothermal surfaces are described rigorously, based on which a directional thermal radiance model named Multi-Scattering Model (MSM) is built, and the numerical calculation of the MSM is discussed. The MSM is applied to modeling the DBT of row crops, and the results are compared with measured DBTs. The MSM is also used to describe the effective emissivity over non-isothermal targets, and a simulation of the remotely sensed pixels with "V" structure is performed, respectively.
The atmospheric attenuation of land surface thermal radiation is simulated with 3 ways according to the abundance of the meteorological data, which is the radiative transfer method, the empirical method and the lookup table (LUT) method. The atmospheric effects simulations are carried out with a Gaussian-Triangular filter as the sensor channel response function, which takes the radiation wavelength range of 10.5μm-12.5μm. The degeneration of the land surface radiance by the atmospheric turbulence is simulated using very short exposure atmospheric modulate transfer function (MTF), and then the land scene thermal radiance at the top of the atmosphere is obtained. The physical procedure of the at-sensor radiance transfer in the thermal remote sensing camera system is analyzed briefly, and the halo and diffractive effects caused by the optical imaging system, and the image movement that is brought by the dither of the remote sensor platform are simulated. The stochastic noise of imaging system is indicated by the noise equivalent temperature difference (NETD) and the SiTF. The NETD is modeled and calculated according to the parameters of a remote sensor, and then the system noise is injected to the digital image. Finally, the response and degenerate of the radiance scene by the thermal remote sensor is simulated.
With the modeling, simulation and analysis of the thermal radiance scene and the imaging procedure, some of results and conclusions can be drawn as follows: (1) The daily variation of the land surface temperatures are fluctuated by the solar radiance and the near surface metrological conditions. Meanwhile, they are restricted by the surface thermal/biological properties.
The analytical temperature model with second harmonic terms presented in this paper can be used to predict the daily bare ground surface temperature variation with sufficient accuracy, and the result is restricted by the measurement or estimation veracity of the ground thermal properties. The solar radiance is the basic reason that makes the ground temperatures changing temporally. For bare surfaces, the daily diversity of the temperatures increases with the rise of the thermal conductivity, while the time that the peak value of the daily temperatures occursclose to midday as the convective heat transfer coefficient decreased.In addition, for vegetation canopies, the temperature in daytime is also affected by the leaf area indexes (LAI). There is a high correlation between surface and the air temperature, and a good correlation between temperatures and the relative humidity. In daily scale, the fluctuation of surface temperature is smoothly for vegetation canopy and water body, and rough for bare and dry surfaces; the minimized differs of all kinds of ground surface occurs at a short period of time before sunrise or after sundown. (2) The land radiance scene dynamically changed with the distribution of surface temperature, land cover and the observation directions.
Base on the fractal terrain simulation and proportion information extraction of surface components, the land radiance scene and the DBT variation of it can be modeled easily and effectively, which the texture details of the land radiance distribution is remained. The multi-scattering effects over components of pixels can be completely described by the MSM model, with which the accuracy for effective radiance modeling of mixed pixels is enhanced. The effective emissivity for non-isothermal targets can also be defined by the usage of MSM. The results of thermal scene modeling show that both the detailed grade of the scene texture and the DBT characteristic of the whole scene are changing with solar-time, especially in a time period after sunrise. The more acuteness of the thermal diversity over pixels, the more details of the scene radiant textures and the variations of the scene DBTs. The effective radiance of land surface takes directional property, which is correlated with the temperatures, emissivity, spatial structure and subdivision extent of the components in each pixel. The effective radiance is aggrandized because of the multi-scattering effects, whereas the change range of DBT is smoothed. At the same time; the effective radiance is also enhanced by the downward atmospheric irradiation, which makes the brightness temperature close to the surface temperature, while the influence of the atmosphere for the scene directional brightness character can be neglected. The effective emissivity is magnified by the multi-scattering effects; the distinctness of the directional effective emissivity for non-isothermal pixel increases with the rise of the diversion for the components temperatures, the isomerous and subdivide state of the substructure, and decreases with the emissivity of components. (3) The land radiance scene is fainted by the atmospheric effects.
The dynamic range of the thermal radiance scene is reduced by atmospheric extinction and radiation. The surface radiance transfer path is shuddered by the turbulent diffusion of atmosphere, and then the image that is projected to the infrared focal plane assembling (FPA) of sensor is shortly moved, which blurs the detected scene. The blurring effect drought by the atmospheric turbulence is increased with the rise of the spatial resolution for land scene and the atmospheric refractive index turbulent structure constant. The contribution of the atmospheric effects for ground radiance at TOA is positive at night while negative in daytime. Some parameters should be regarded during the simulation of atmospheric effects, which are the total water vapor contents, the observation angle, the setting of the response channels and the ground surface emissivity. (4) The land radiance scene image is deteriorated further after the response, transmission, processing of the signals by the sensor system.
With the increase of the aperture size of the infrared camera, the energy focused to the FPA increases accordingly, and the image blurring brought by diffraction of the optical system is reduced consequently. The radiance response of the sensor is disturbed by detector integral time and vibration of the platform, and the remotely sensed imagery is deteriorated. The noise originated from the entire process of the land radiance imaging can be characterized by the NETD for highly integrated thermal infrared remote sensing system. With the simulation of the main steps of the sensor imaging procedures, the results show that image drift by the movement of platform is the key reason for which the image is blurred by sensor; the system noise is influenced by the ground radiant levels and the atmospheric transmittance, therefore the noise has significant effect on the image quality for night thermal scene.
引文
[1]Woodcock C, E, Strahler A, H. The Factor of Scale in RemoteSensing. Remote Sensing of Environment,1987,21:311-332
[2]Voogt J A, Oke T R. Thermal remote sensing of urban climates. Remote Sensing of Environment,2003,86(3):370-384
[3]Weng Q. Thermal infrared remote sensing for urban climate and environmental studies:Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing,2009,64(4):335-344
[4]Garnier C, Collorec R. Flifla J. General framework for infrared sensor modeling. Proc. of SPIE,1998,3701:59-70
[5]徐中民,光学遥感器的数字仿真研究,硕士,中国科学院长春光学精密机械与物理研究所,中国科学院,长春,2002,2-4.
[6]William R S. AEDC aerospace chamber 7V-an advanced test capability for infrared surveillance and seeker sensors. Proceedings of SPIE,1993,2470:369-379
[7]Larry M. Larry S. Multispectral imagery simulation. Proc. of SPIE,1993,1904: 144-160
[8]杨贵军.基于场景模型的热红外遥感成像模拟方法 红外与毫米波学报,2007,26(1)：15-21
[9]路远.地表红外辐射建模研究.红外技术.2008,30(2)：75-78
[10]吕相银,凌永顺,黄超超.地面目标表面温度及红外辐射的计算.红外与激光工程.2006,35(5)：563-567
[11]魏玺章,黎湘,庄钊文,等.红外目标背景及温度场的计算.红外与毫米波学报，2000,19(2)：139-141
[12]杨德贵,魏玺章,黎湘.草地红外辐射特性研究.系统工程与电子技术,2000,22(8)：63-65
[13]杨德贵,黎湘,庄钊文.基于统一模型的典型地表红外辐射特性对比研究.红外与毫米波学报，2001,20(4)：263-266
[14]刘建勋,陈重.目标表面热红外辐射与地面倾角关系的计算分析.表面技术,2006,35(3)：74-75
[15]张建奇,方小平,张海兴，等.雪面辐射温度预测模型.红外与毫米波学报，1997,16(3)：206-210
[16]张建奇,方小平,张海兴,等.植被红外辐射统计特性理论模型.西安电子科技大学学报.1997,24(3)：386-390
[17]邵晓鹏,杨威,张建奇.自然地面背景红外图像生成方法研究.红外与激光工程，2000,29(3)：72-74
[18]邵晓鹏,郑宏斌，徐军,等.光裸地表红外辐射统计模型及纹理特征分析.西安电子科技大学学报(自然科学版).2007,34(6)：994-996
[19]邵晓鹏.土壤与植被混合型地表的温度场特性分析.西安电子科技大学学报(自然科学版),2009,36(1)：122-126
[20]黄华国.辛晓洲,柳钦火,等.用CUPID模型模拟小麦组分温度分布：敏感性分析与验证.遥感学报,2007,11(1)：94-102
[21]黄华国,辛晓洲,柳钦火,等.扩展CUPID模型模拟土壤组分温度分布.农业工程学报.2007,23(1)：139-145
[22]Yang G, Liu Q, Liu Q, et al. Simulation of high-resolution mid-infrared (3-5um) images using an atmosphere radiative transfer analytic model. International Journal of Remote Sensing,2009,30(22):6003-6022
[23]朱文勇.舰船红外成像模拟.红外与毫米波学报,1998,17(2)：129-134
[24]寇蔚.一种舰船红外特征的动态模拟方法研究.红外与毫米波学报.2004，23(2)：148-152
[25]宣益民.桥梁红外热特征分析.红外技术,2000,22(4)：10-14
[26]韩玉阁.坦克红外辐射特性影响因素的灵敏度分析.红外与激光工程,2003,32(3)：255-258
[27]蒋一明.坦克目标的红外成像建模与仿真.红外技术,2008,30(1)：39-42
[28]王建坤,军用车辆的红外辐射特性分析,硕士学位论文,南京理工大学,2008.
[29]朱寿远,魏德孟,姚军田,等.主战坦克与地物背景红外辐射特性研究.红外技术,2000,22(5)：45-50
[30]邹振宁,周芸.装甲车辆红外辐射特性分析.电光与控制,2005,12(2)：78-82
[31]江照意,典型目标场景的红外成像仿真研究,博士,理学院,浙江大学,杭州,2007.
[32]吕建伟.飞行器表面温度和发射率分布对红外辐射特征的影响.光电工程,2009,36(2)：50-54
[33]梁欢,地面背景的红外辐射特性计算及红外景象生成,硕士学位论文,南京理工大学.2009.
[34]Ersi K, Guodong C, Kechao S, et al. Simulation of energy and water balance in Soil-Vegetation-Atmosphere Transfer system in the mountain area of Heihe River Basin at Hexi Corridor of northwest China. SCIENCE IN CHINA (Series D),2005,48(4):538-548
[35]Li X, Strahler A H. Modeling the gap probability of a discontinuous vegetation canopy. IEEE transactions on geoscience and remote sensing,1988,26(2):161-170
[36]Chen L, Liu Q, Fan W, et al. A bi-directional gap model for simulating the directional thermal radiance of row crops. Science in China Series D:Earth Sciences,2002,45(12):1087-1098
[37]Yu T, Gu X, Tian G, et al. Modeling directional brightness temperature over a maize canopy in row structure. IEEE transactions on geoscience and remote sensing,2004,42(10):2290-2304
[38]Liu Q, Huang H, Qin W, et al. An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy. IEEE transactions on geoscience and remote sensing,2007,45(9):2900-2918
[39]陈良富，范闻捷，柳钦火.地表热辐射方向性研究进展.地理科学进展,2001,20(3)：262-267
[40]周纪.城市区域热辐射方向性研究进展.地球科学进展,2009，24(5)：497-505
[41]华建军,张建强.空中目标红外特性的计算方法.激光与红外,2001,31(3)：166-168
[42]严和平,沈同圣,周晓东,等LOWTRAN7在动态红外图像仿真系统中的应用与系统集成.红外与激光工程,1998,27(4)：14-17
[43]笪邦友,地面目标背景的红外成像仿真研究,硕士学位论文,华中科技大学,武汉,2006.
[44]桑农,陈艳菲,张天序.一种实用的红外图像模拟生成方法.华中科技大学学报(自然科学版),2005,33(5)：55-57
[45]刘林华,董士奎,余其铮,等.红外1～14μm波长间隔0.1μm上大气平均透过率(Ⅱ).水蒸气的透过率.哈尔滨工业大学学报，1999,31(6)：75-78
[46]路远,凌永顺.红外辐射大气透射比的简易计算.红外技术,2003,25(5)：45-49
[47]周国辉.一种计算红外辐射大气透过率的数学模型.红外技术，2008,30(6)：331-334
[48]陈良富.热红外遥感中大气下行辐射效应的一种近似计算与误差估计.遥感学报,1999,3(3)：165-170
[49]黄妙芬,邢旭峰,朱启疆.等.定量遥感地表净辐射通量所需大气下行长波辐射估算模型改进.地理研究,2005，24(5)：757-766
[50]史忠彦.红外多元探测器的噪声仿真研宄.红外技术,2003,25(6)
[51]曹移明.星载红外凝视相机信噪比计算分析.宇航学报,2007,28(4)
[52]李福巍.积分时间对红外焦平面成像系统的影响.应用光学,2008,29(5)
[53]胡明鹏.参数设置对噪声等效温差(NETD)测试影响分析.红外技术，2009,31(1)
[54]Sheffer A D, Cathcart J M. Computer generated IR imagery:A first principles modeling approach In:Multispectral image processing and enhancement. Orlando:Proceedings of SPIE,1988,933:199-206
[55]Yee B K,3-D Visualization of the Physieally Reasonable Infrared Signature Model. presented at the the Fourth AnnualGround Target Modeling & Validation Conferenee, Houghton,1993.
[56]T.Gonda, PRISM Based thermal Signature Modeling Simulation. presented at the Symposium on Ground Vehiele Signature,1987.
[57]Stets J, Conant J, Gruninger J, et al. Synthetic IR scene generation. Proc. SPIE, 1988,890:130-146
[58]Gerhart G, Martin G, T.Gonda. Thermal Image Modeling. Proceedings of SPIE, 1987,782:3-9
[59]Hinderer J. Model for Generating Synthetic Three-dimensional(3D) Images of Small Vehicles. Proc. of SPIE Conf. On Infared Sensor Fusion,1987,782
[60]Biesel H. Real-time Simulated Forward Looking Infrared(FLIR) imagery for training. Proc. of SPIE Conf. On Infared image processing and Enhancement. 1987,781
[61]Ben-Yosef N, Wilner K, Fuchs L, et al. Natural Terrain Infrared Radiance Statistics:Daily Variation. APPLIED OPTICS,1985,24(23):4167-4171
[62]Balfour L S, Bushlin Y. Semi-empirical model-based approach for IR scene simulation. Proc. of SPIE,1997,3061:616-623
[63]Poglio T, E.Savaria. Specifications and conceptual architecture of a thermal infrared simulator of landscapes. Proc. of SPIE,2001,4540:488-497
[64]Poglio T, Savaria E, Wald L, Outdoor Scene Synthesis in the Infrared Range for Remote Sensing Applications. presented at the CISST'02:International Conference on Imaging Science, Systems, and Technology, Las Vages, Nevada 2002.
[65]Pentecost H T A, Recognition T. Identification and Tracking Using Real and Synthetic IR Imagery. In:SPIE:Proc. SPIE,1996,2744:520-525
[66]Kustas W P, Norman J M. Anderson M C, et al. Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sensing of Environment,2003,85(4):429-440
[67]Kustas W P, Li F, Jackson T J, et al. Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment,2004,92(4):535-547
[68]谈和平,夏新林,刘林华,等.红外辐射特性与传输的数值计算：计算热辐射学.哈尔滨：哈尔滨工业大学出版社,2006.43-44
[69]Howell J R, Perlmutter M. Monte carlo solution of thermal transfer through radiant media between gray walls. Journal of Heat Transfer:Transactions of the ASME,1964,86(1):116-122
[70]程强,周怀春.具有漫反射边界一维灰性平行板介质中辐射传递方程的求解.工程热物理学报,2004,25(5)：831-833
[71]Olivier J, Yves K. SEISM,Scene Electro-optical Image generator.and Sensor Model. Proc. of SPIE,1997,3063:297-289
[72]Johnson K R, A.Curran, Less D, et al. MuSES:A New Heat and Signature Management Design Tool for Virtual Prototyping. In:Ninth Annual Ground Target Modeling & Validation Conference. Houghton,1998
[73]Krayenhoff E S, Voogt J A. A microscale three-dimensional urban energy balance model for studying surface temperatures. Boundary-Layer Meteorology, 2007,123:433-461
[74]Gastellu-Etchegorry J P, Martin E, Gascon F. DART:A 3-D model for simulating satellite images and surface radiation budget. International Journal of Remote Sensing,2004,25(1):75-96
[75]Poglio T, Mathieu-Marni S, Ranchin T, et al. OSIrIS:a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment,2006,104(2): 238-246
[76]Jacobs P a M. Simulation of the thermal behaviour of object and its nearby surroundings. TNO publication PHL,1980,8
[77]Sugawara H, Takamura T. Longwave radiation flux from an urban canopy: Evaluation via measurements of directional radiometric temperature. Remote Sensing of Environment,2006,104(2):226-237
[78]Mihalakakou G. On estimating soil surface temperature profiles. Energy and Buildings,2002.34(2002):251-259
[79]Qin Z, Pedro B, Arnon K. Numerical solution of a complete surface energy balance model for simulation of heat fluxes and surface temperature under bare soil environment. Applied Mathematics and Computation.2002,130(2002):171-200
[80]Sanchez J M, Kustas W P, Caselles V, et al. Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sensing of Environment,2008,112(3):1130-1143
[81]Herb W R, Janke B, Mohseni O, et al. Ground surface temperature simulation for different land covers. Journal of Hydrology,2008,356(3-4):327-343
[82]Sepulcre-Canto G, Zarco-Tejada P J, Sobrino J A, et al. Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. Agricultural and Forest Meteorology,2009,149(6-7):962-975
[83]Norman J M. Modeling the complete crop canopy. In:B.J. BarfieldJ.F. Gerber, eds. Modification of the Aerial Environment of Plants. St. Joseph, MI.:ASAE Monogr. Am. Soc. Agric. Eng.,1979.249-277
[84]Norman J M. Synthesis of canopy processes. In:G. Russell, et al., eds. Plant canopies:Their growth, form and function. New York:Soc. Exp. Biol., Seminar Series 31, Cambridge University Press,1988.161-175
[85]Norman J M, Kustas W P, Humes K S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radio metric surface temperature. Agricultural and Forest Meteorology,1995,77(3-4):263-293
[86]Kustas W, Anderson M. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology,2009,149(12):2071-2081
[87]田国良.热红外遥感.北京：电子工业出版社,2006.72-75
[88]Labed J, Stoll M P. Angular variation of land surface spectral emissivity in the thermal infrared:Labor investigation on bare soils. International Journal of Remote Sensing,1991.12(11):2299-2310
[89]Rees W G, James S P. Angular variation of the infrared emissivity of ice and water surfaces. International Journal of Remote Sensing,1992,13(8):2873-2886
[90]Cuenca J, Sobrino J A. Experimental measurements for studying angular and spectral variation of thermal infrared emissivity. Appl Opt,2004,43(23):4598-4602
[91]Francois C, Ottle C, Prevot L. Analytical parametrisation of canopy emissivity and directional radiance in the thermal infrared:Application on the retrieval of soil and foliage temperatures using two directional measurements. International Journal of Remote Sensing,1997,18(2):2587-2621
[92]Kimes D S, Smith J A. Directional radio metric measurements of row-crop temperatures. International Journal of Remote Sensing,1983.4(2):299-311
[93]Li X. Strahler A H, Friedl M A. A conceptual model for effective directional emissivity from non-isothermal surface. IEEE transactions on geoscience and remote sensing,1999,37:2508-2517
[94]Yan G, Jiang L, Wang J, et al. Thermal bidirectional gap probability model for row crop canopies and validation. Science in China Series D:Earth Sciences. 2003,46(12):1241-1249
[95]Kimes D S. Remote sensing of row crop structure and component temperatures using directional radiometric temperatures and inversion techniques. Remote Sensing of Environment,1983,13(1):33-55
[96]Caselles V, Sobrino J A, Coll C. A physical model for interpreting the land surface temperature obtained by remote sensors over incomplete canopies. Remote Sensing of Environment,1992,39(3):203-211
[97]Voogt J A. Assessment of an Urban Sensor View Model for thermal anisotropy. Remote Sensing of Environment,2008.112(2):482-495
[98]Henon. SOLENE software.2008; Available from: http://www.cerma.archi.fr/CERMA/Expertise/solene/.
[99]Lagouarde J P, Henon A, Kurz B, et al. Modelling daytime thermal infrared directional anisotropy over Toulouse city centre. Remote Sensing of Environment,2010.114(1):87-105
[100]Sobrino J A, Jimenez-Munoz J C, Verhoef W. Canopy directional emissivity: Comparison between models. Remote Sensing of Environment,2005,99(3):304-314
[101]Menenti M, Jia L, Li Z L. Multi-angular thermal infrared observations of terrestrial vegetation. In:S. Liang, eds. Advances in Land Remote Sensing: System. Modeling. Inversion and Application. Berlin:Springer,2008.51-93
[102]Liang S. Quantitative Remote Sensing of Land Surfaces. Hoboken:John Wiley & Sons, Inc.,2004.256-260
[103]Clough S A, Iacono M J, Moncet J L. Line-by-line calculations of atmospheric fluxes and cooling rates:Application to water vapor. Journal of Geophysical Research,1992,97:15761-15785
[104]Kobayashi H. Line-by-line calculation using the Fourier-transformed Voigt function. J. Quant. Spectrosc. Radiat. Transfer,1999,62:477-483
[105]Hartmann J M, Levi Di Leon R, Taine J. Line-by-line and narrow-band statistical model calculations for H2O. J. Quant. Spectrosc. Radiat. Transfer,1984,32:119-127
[106]Lacis A A, Oinas V. A description of the correlated k distribution method for modeling nongray gaseous absorption, thermal emission, and multiple scattering in vertically inhomogeneous atmospheres. Journal of Geophysical Research, 1991,96:9027-9063
[107]Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for imhomogeneous atmospheres:RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research,1997,102:16663-16682
[108]Niemela S, Raisanen P, Savijarvi H. Comparison of surface radiative flux parameterizations:Part I:Longwave radiation. Atmospheric Research,2001, 58(1):1-18
[109]Iziomon M G, Mayer H, Matzarakis A. Downward atmospheric longwave irradiance under clear and cloudy skies:Measurement and parameterization. Journal of Atmospheric and Solar-Terrestrial Physics,2003,65(10):1107-1116
[110]Jordan J B. Watkins W R, Palacios F R, et al. Simulated dynamic effects of atmospheric turbulence on IR digital imagery. Infrared Physics & Technology, 1996,37(5):607-617
[111]Kopeika N S, Sadot D. Imaging through the atmosphere:practical instrumentation-based theory and verification of aerosol MTF. J. Opt. Soc. AM. A,1995,12(5):1017-1025
[112]Sadot D, Kopeika N S. Thermal imaging through the atmosphere:atmosphere MTF theory and verification. Opt. Eng.,1994,33(3):880-887
[113]Ricklin J C, Tomlison T G. Current challenges in atmospheric propagation research. Proc. of SPIE,2006,6215
[114]Christelle G. Physically-based infrared sensor efforts modeling. Proc. of SPIE, 1999,3701:81-94
[115]Noilhan J, Mahfouf J F. The ISBA land surface parameterization scheme Global and Plan. Change,1996,13:145-159
[116]Repasi E, Greif H J. Generation of dynamic IR-Scenes for ground-based systems and missile applications. Proc. of SPIE,1998,3436:460-461
[117]Li X W, Wang J D, Strahler A H. Scale effect of Planck's law over nonisothermal blackbody surface. SCIENCE IN CHINA (Series E),2000,42(6): 12-17
[118]Wan Z, Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE transactions on geoscience and remote sensing,1996,34(4):892-905
[119]Norman J M, Becker F. Terminology in thermal infrared remote sensing of natural surfaces. Agriculture and Forest Meteorology,1995,77(3-4):153-166
[120]Becker F, Li Z. Surface temperature and emissivity at various scales:Definition, measurement and related problems. Remote Sensing Reviews,1995,12:225-253
[121]Li X W, Wang J D. The definition of effective emissivity of land surface at the scale of remote sensing pixels. Chinese Science Bulletin,1999,44(23):2154-2154
[122]李新,程国栋,陈贤章.任意地形条件下太阳辐射模型的改进.科学通报,1999,44(9)：993-998
[123]李净.基于DEM的坡地太阳总辐射估算.太阳能学报,2007,28(8):905-911
[124]Wang Z, Peng Q, Lu Y, et al. A Global Infrared Image Synthesis Model for Large-Scale Complex Urban Scene. International Journal of Infrared and Millimeter Waves,2001,22(8):1193-1208
[125]Prata A J. A new long-wave formula for estimating downward clear-sky radiation at the surface. Quarterly Journal of the Royal Meteorological Society,1996. 122(533):1127-1151
[126]Kreith M, Bohn S M. Principles of Heat Transfer:West Publishing Company, 1993
[127]赵英时.遥感应用分析原理与方法.北京：科学出版社,2003
[128]Penman H L. Vegetation and Hydrology. Farnham:Royal Commonwealth Agricultural Bureaux,1963
[129]Kimball B A, Jackson R D. Soil heat flux. In:B.J. BarfieldJ.F. Gerber, eds. Modification of the Aerial Environment of Plants. St. Joseph, MI:American Society of Agricultural Engineers, 1979.211-229
[130]Hares M A, Novak M D. Simulation of surface energy balance and soil temperature under strip tillage:I Model description. Soil Sci. Soc. Am. J.,1992, 56:22-29
[131]Marshall T J, Holmes J W. Soil Physics. Cambridge, UK:Cambridge University Press,1979
[132]杨世铭,陶文铨.传热学.北京：高等教育出版社,1998
[133]Krarti M, Lopez-Alonzo C, Claridge D E, et al. Analytical model to predict annual soil surface temperature variation. J. Solar Energy Engng.,1995,117:91-99
[134]Patankar S V. Numerical Heat Transfer and Fluid Flow. New York:McGraw-Hill,1980
[135]Carslaw H S, Jaeger J C. Conduction of Heat in Solids.2nd ed. Oxford:Oxford Science Publishers,1980
[136]Mihalakakou G, Santamouris M, Lewis J O, et al. On the application of the energy balance equation to predict ground temperature profiles. Solar Energy, 1996,60(3-4):181-190
[137]Geiger R. The Climate Near Ground. Cambridge, MA:Harvard University Press, 1961
[138]Jacquemoud S, Verhoef W, Baret F, et al. PROSPECT+SAIL models:A review of use for vegetation characterization. Remote Sensing of Environment,2009, 113(Supplement 1):S56-S66
[139]张建奇.方小平,张海兴.植被红外辐射特性理论模拟.西安电子科技大学学报,1997,24：386-390
[140]刘强,陈良富,柳钦火.作物冠层的热红外辐射传输模型.遥感学报,2003,7(3)：161-167
[141]Inclana M G, Forkela B R. Comparison of Energy Fluxes Calculated with the Penman-Monteith Equation and the Vegetation Models SiB and Cupid. Journal of Hydrology,1995,166(3-4):193-211
[142]Voss R D, FRACTALS in NATURE:characterization, measurement, and simulation. presented at the SIGGRAPH 1987,1987.
[143]英振华,基于分形理论的地形场景实时真实感绘制，硕士,重庆大学,2005.
[144]Miller G S P, The Definition and Rendering of Terrain Maps. presented at the SIGGRAPH 1986,1986.
[145]陈明昊，三维分形地形生成关键技术的研究与实现.硕士工学,电子技术学院,解放军信息工程大学,郑州,2008.
[146]齐敏，郝重阳,佟明安.三维地形生成及实时显示技术研究进展.中国图象图形学报,2000,5(A)(4)：269-271
[147]何方容,戴光明.三维分形地形生成技术综述.武汉化工学院学报,2002,24(3)：85-88
[148]Maeder R E. Two-Dimensional Fractional Brownian Motion. the Wolfram Demonstrations Project; Available from: http://demonstrations.wolfram.com/TwoDimensionalFractionalBrownianMition/.
[149]Nash E B, Conel J E. Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite. Journal of Geophysical Research,1974, 79:1615-1621
[150]Singer R B, Mccord T B. Mars:Large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance. In:10th Lunar and Planetary Science Conference:Proc. of Conference,1979:1835-1848
[151]Segl K, Roessner S, Heiden U, et al. Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing,2003, 58:99-112
[152]Schowengerdt R A. Remote Sensing:Models and Methods for Image Processings. New York:Academic Press,1997.522
[153]Richards J A, Jia X. Remote Sensing Digital Image Analysis. New York: Springer-Verlag,1999.363
[154]Valor E, Caselles V. Mapping land surface emissivity from NDVI:Application to European, African, and South American areas. Remote Sensing of Environment, 1996,57(3):167-184
[155]Mcfeeters S K. The use of Normalized Defference Water Index(NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 1996,17(7):1425-1432
[156]吴宏安,蒋建军,张海龙.比值居民地指数在城镇信息提取中的应用.南京师大学报(自然科学版)，2006,29(3)：118-121
[157]Jimenez-Munoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research,2003.108(D22):4688-4695
[158]Dozier J, Frew J. Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE transactions on geoscience and remote sensing, 1990,28(5):963-969
[159]牛铮,柳钦火,高彦春,等.零散射近似的冠层热红外辐射间隙率模型.中国科学E辑,2000,30(z1)：89-98
[160]王锦地,李小文,苏红波,等.两组分非同温像元热辐射中多次散射影响的解析模型和验证.遥感学报,2003,7(1)：1-7
[161]Chen L F, Zhuang J L, Xu X R, et al. The concept of effective emissivity of nonisothermal mixed pixel and its test. Chinese Science Bulletin,2000,45(9): 788-795
[162]余其铮.辐射换热原理.哈尔滨：哈尔滨工业大学出版社,2000.41-45
[163]杨贤荣,马庆芳,原庚新,et al辐射换热角系数手册.北京：国防工业出版社,1982.44-74
[164]Li Z-L, Zhang R, Sun X, et al. Experimental system for the study of the directional thermal emission of natural surfaces. International Journal of Remote Sensing,2004,25(1):195-204
[165]Zhang R H, Sun X M, Li Z L, et al. Revealing of major factors in the directional thermal radiation of ground objects. SCIENCE IN CHINA (Series E),2000, 43(supplement):34-40
[166]Dozier J, Wagner S. Effect of viewing angle on the infrared brightness temperature of snow. Water Resource Research,1982,18(5):1424-1434
[167]李小文,王锦地,Strahler A H尺度效应及几何光学模型用于尺度纠正.中国科学E辑：技术科学,2000,30(增刊)：12-17
[168]苏理宏,李小文,王锦地.地表非同温像元发射率的Monte Carlo模拟.自然科学进展，2002,12(4)：446-448
[169]苏理宏,李小文，王锦地.三维结构非同温像元热辐射的尺度效应.自然科学进展,2002,12(1)：51-55
[170]朱利.顾行发,陈良富,等.高分辨率红外相机单窗与劈窗陆表温度反演精度分析研究.红外与毫米波学报,2008,27(5)：346-353
[171]Fried D. Optical resolution through a randomly imhomogeneous medium for very long and very short exposure. J.Opt.Soc.Am,1966,56(10):1372-1379
[172]Xiaoying L, Xingfa G, Tao Y, et al. Atmospheric scattering and turbulence modulation transfer function for CCD cameras on CBERS-02b and HJ-1 A/1 B. International Journal of Remote Sensing,2011, in Press
[173]赵利民,余涛,田庆久,等HJ-1B热红外遥感数据陆表温度反演误差分析.光谱学与光谱分析，2010,30(12)：3359-3362
[174]Zhao L-M, Yu T, Tian Q-J, et al. Atmospheric Sensitivity on Land Surface Temperature Retrieval Using Single Channel Thermal Infrared Remote Sensing Data:Comparison between Models. In:Geoinformatics2010. Beijing,2010
[175]陈玻若.红外系统.北京：国防工业出版社,1987
[176]王波，黄曦.基于OGRE的红外成像传感器典型物理效应仿真.电子科技，2010，23(3)：36-39
[177]宁殿艳，星载推扫型红外成像传感器建模与仿真,硕士学位论文,西安电子科技大学，西安,2008.
[178]韩意.用Vega生成真实传感器效果的光电成像模拟方法.计算机应用,2009,29(z1)：342-344
[179]Hadar O, Robbins M, Novogrozhy Y, et al. Image motion restoration from a sequense of images. Opt. Eng.,1996,35(10):2898-2904
[180]Fiete R D, Tantalo T A. Image quality of increased along-sampling for remote sensing system. Opt. Eng.,1999,38:815-820
[181]公发全.亚像元线阵CCD推扫成像系统调制传递函数分析方法.光电技术,2002,28(3)：293-244
[182]徐鹏,黄长宁,王涌天.卫星振动对成像质量影响的仿真分析.宇航学报,2003,24(3)：259-263
[183]赵贵军,陈长征,万志.推扫型TDI CCD光学遥感器动态成像研究.光学精密工程，2006,14(2)：291-296
[184]张建奇,方小平.红外物理——研究生系列教材.西安：西安电子科技大学出版社,2004
[185]韩心志.航天遥感CCD扫帚式成像系统.哈尔滨：哈尔滨工业大学出版社,1990
[186]韩心志,焦世举.航天光学遥感辐射度学：国防科技出版社,1994
[187]齐怀川,黄巧林,胡永力.空问光学遥感运动模糊仿真方法研究.航天返回与遥感,2010.31(2)：51-56
[188]周虎.航天光学遥感器抖动补偿方法研究，硕士学位论文，中国空间技术研究院,2008.
[189]邹前进.红外成像系统噪声测量仿真研究.红外技术,2008,30(6)
[190]Wenaas H. Computer-generated correlated noise images for various statistical distributions. Proc.SPIE.,1991,1569:410-421
[191]C.Holst. G. Testing and Evaluation of Infrared Imaging Systems.2nd ed:JCD Publishing and SPIE Optical Engineering Press,1998
[192]O'shea P, Sousk S. Practical Issue with 3D-Noise Measurements and Application to Modern Infrared Sensors. Proc. SPIE.2005,5784:262-271
[193]Yue-Ping Z, Jian-Qi Z, Xi H. Influence of detector noise on infrared images. Proc.SPIE.,2001,4548:381-386
[194]李旭东,胡铁力,岳文龙,等.红外热像仪SiTF的测试研究.应用光学.2005,26(5)：21-24
[195]Richardson P, Miller B. Third Generation FLIR Simulation at NVESD. Proc. SPIE,2007,6543:65430K-1-65430K-11
[196]李旭东.扫描热成像系统NETD数学模型的研究.应用光学,2004，25(4)
[197]Markham B, Townshend J, R.G. Land Cover Classification Accuracy as a Function of Sensor Spatial Resolution. Proceedings 15th International Symposium on Remote Sensing of Environment,1981
[198]龚明劫.卫星遥感制图最佳影像空间分辨率与地图比例尺关系探讨.测绘科学,2009,34(4)
[2]Voogt J A, Oke T R. Thermal remote sensing of urban climates. Remote Sensing of Environment,2003,86(3):370-384
[3]Weng Q. Thermal infrared remote sensing for urban climate and environmental studies:Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing,2009,64(4):335-344
[4]Garnier C, Collorec R. Flifla J. General framework for infrared sensor modeling. Proc. of SPIE,1998,3701:59-70
[5]徐中民,光学遥感器的数字仿真研究,硕士,中国科学院长春光学精密机械与物理研究所,中国科学院,长春,2002,2-4.
[6]William R S. AEDC aerospace chamber 7V-an advanced test capability for infrared surveillance and seeker sensors. Proceedings of SPIE,1993,2470:369-379
[7]Larry M. Larry S. Multispectral imagery simulation. Proc. of SPIE,1993,1904: 144-160
[8]杨贵军.基于场景模型的热红外遥感成像模拟方法 红外与毫米波学报,2007,26(1)：15-21
[9]路远.地表红外辐射建模研究.红外技术.2008,30(2)：75-78
[10]吕相银,凌永顺,黄超超.地面目标表面温度及红外辐射的计算.红外与激光工程.2006,35(5)：563-567
[11]魏玺章,黎湘,庄钊文,等.红外目标背景及温度场的计算.红外与毫米波学报，2000,19(2)：139-141
[12]杨德贵,魏玺章,黎湘.草地红外辐射特性研究.系统工程与电子技术,2000,22(8)：63-65
[13]杨德贵,黎湘,庄钊文.基于统一模型的典型地表红外辐射特性对比研究.红外与毫米波学报，2001,20(4)：263-266
[14]刘建勋,陈重.目标表面热红外辐射与地面倾角关系的计算分析.表面技术,2006,35(3)：74-75
[15]张建奇,方小平,张海兴，等.雪面辐射温度预测模型.红外与毫米波学报，1997,16(3)：206-210
[16]张建奇,方小平,张海兴,等.植被红外辐射统计特性理论模型.西安电子科技大学学报.1997,24(3)：386-390
[17]邵晓鹏,杨威,张建奇.自然地面背景红外图像生成方法研究.红外与激光工程，2000,29(3)：72-74
[18]邵晓鹏,郑宏斌，徐军,等.光裸地表红外辐射统计模型及纹理特征分析.西安电子科技大学学报(自然科学版).2007,34(6)：994-996
[19]邵晓鹏.土壤与植被混合型地表的温度场特性分析.西安电子科技大学学报(自然科学版),2009,36(1)：122-126
[20]黄华国.辛晓洲,柳钦火,等.用CUPID模型模拟小麦组分温度分布：敏感性分析与验证.遥感学报,2007,11(1)：94-102
[21]黄华国,辛晓洲,柳钦火,等.扩展CUPID模型模拟土壤组分温度分布.农业工程学报.2007,23(1)：139-145
[22]Yang G, Liu Q, Liu Q, et al. Simulation of high-resolution mid-infrared (3-5um) images using an atmosphere radiative transfer analytic model. International Journal of Remote Sensing,2009,30(22):6003-6022
[23]朱文勇.舰船红外成像模拟.红外与毫米波学报,1998,17(2)：129-134
[24]寇蔚.一种舰船红外特征的动态模拟方法研究.红外与毫米波学报.2004，23(2)：148-152
[25]宣益民.桥梁红外热特征分析.红外技术,2000,22(4)：10-14
[26]韩玉阁.坦克红外辐射特性影响因素的灵敏度分析.红外与激光工程,2003,32(3)：255-258
[27]蒋一明.坦克目标的红外成像建模与仿真.红外技术,2008,30(1)：39-42
[28]王建坤,军用车辆的红外辐射特性分析,硕士学位论文,南京理工大学,2008.
[29]朱寿远,魏德孟,姚军田,等.主战坦克与地物背景红外辐射特性研究.红外技术,2000,22(5)：45-50
[30]邹振宁,周芸.装甲车辆红外辐射特性分析.电光与控制,2005,12(2)：78-82
[31]江照意,典型目标场景的红外成像仿真研究,博士,理学院,浙江大学,杭州,2007.
[32]吕建伟.飞行器表面温度和发射率分布对红外辐射特征的影响.光电工程,2009,36(2)：50-54
[33]梁欢,地面背景的红外辐射特性计算及红外景象生成,硕士学位论文,南京理工大学.2009.
[34]Ersi K, Guodong C, Kechao S, et al. Simulation of energy and water balance in Soil-Vegetation-Atmosphere Transfer system in the mountain area of Heihe River Basin at Hexi Corridor of northwest China. SCIENCE IN CHINA (Series D),2005,48(4):538-548
[35]Li X, Strahler A H. Modeling the gap probability of a discontinuous vegetation canopy. IEEE transactions on geoscience and remote sensing,1988,26(2):161-170
[36]Chen L, Liu Q, Fan W, et al. A bi-directional gap model for simulating the directional thermal radiance of row crops. Science in China Series D:Earth Sciences,2002,45(12):1087-1098
[37]Yu T, Gu X, Tian G, et al. Modeling directional brightness temperature over a maize canopy in row structure. IEEE transactions on geoscience and remote sensing,2004,42(10):2290-2304
[38]Liu Q, Huang H, Qin W, et al. An extended 3-D radiosity-graphics combined model for studying thermal-emission directionality of crop canopy. IEEE transactions on geoscience and remote sensing,2007,45(9):2900-2918
[39]陈良富，范闻捷，柳钦火.地表热辐射方向性研究进展.地理科学进展,2001,20(3)：262-267
[40]周纪.城市区域热辐射方向性研究进展.地球科学进展,2009，24(5)：497-505
[41]华建军,张建强.空中目标红外特性的计算方法.激光与红外,2001,31(3)：166-168
[42]严和平,沈同圣,周晓东,等LOWTRAN7在动态红外图像仿真系统中的应用与系统集成.红外与激光工程,1998,27(4)：14-17
[43]笪邦友,地面目标背景的红外成像仿真研究,硕士学位论文,华中科技大学,武汉,2006.
[44]桑农,陈艳菲,张天序.一种实用的红外图像模拟生成方法.华中科技大学学报(自然科学版),2005,33(5)：55-57
[45]刘林华,董士奎,余其铮,等.红外1～14μm波长间隔0.1μm上大气平均透过率(Ⅱ).水蒸气的透过率.哈尔滨工业大学学报，1999,31(6)：75-78
[46]路远,凌永顺.红外辐射大气透射比的简易计算.红外技术,2003,25(5)：45-49
[47]周国辉.一种计算红外辐射大气透过率的数学模型.红外技术，2008,30(6)：331-334
[48]陈良富.热红外遥感中大气下行辐射效应的一种近似计算与误差估计.遥感学报,1999,3(3)：165-170
[49]黄妙芬,邢旭峰,朱启疆.等.定量遥感地表净辐射通量所需大气下行长波辐射估算模型改进.地理研究,2005，24(5)：757-766
[50]史忠彦.红外多元探测器的噪声仿真研宄.红外技术,2003,25(6)
[51]曹移明.星载红外凝视相机信噪比计算分析.宇航学报,2007,28(4)
[52]李福巍.积分时间对红外焦平面成像系统的影响.应用光学,2008,29(5)
[53]胡明鹏.参数设置对噪声等效温差(NETD)测试影响分析.红外技术，2009,31(1)
[54]Sheffer A D, Cathcart J M. Computer generated IR imagery:A first principles modeling approach In:Multispectral image processing and enhancement. Orlando:Proceedings of SPIE,1988,933:199-206
[55]Yee B K,3-D Visualization of the Physieally Reasonable Infrared Signature Model. presented at the the Fourth AnnualGround Target Modeling & Validation Conferenee, Houghton,1993.
[56]T.Gonda, PRISM Based thermal Signature Modeling Simulation. presented at the Symposium on Ground Vehiele Signature,1987.
[57]Stets J, Conant J, Gruninger J, et al. Synthetic IR scene generation. Proc. SPIE, 1988,890:130-146
[58]Gerhart G, Martin G, T.Gonda. Thermal Image Modeling. Proceedings of SPIE, 1987,782:3-9
[59]Hinderer J. Model for Generating Synthetic Three-dimensional(3D) Images of Small Vehicles. Proc. of SPIE Conf. On Infared Sensor Fusion,1987,782
[60]Biesel H. Real-time Simulated Forward Looking Infrared(FLIR) imagery for training. Proc. of SPIE Conf. On Infared image processing and Enhancement. 1987,781
[61]Ben-Yosef N, Wilner K, Fuchs L, et al. Natural Terrain Infrared Radiance Statistics:Daily Variation. APPLIED OPTICS,1985,24(23):4167-4171
[62]Balfour L S, Bushlin Y. Semi-empirical model-based approach for IR scene simulation. Proc. of SPIE,1997,3061:616-623
[63]Poglio T, E.Savaria. Specifications and conceptual architecture of a thermal infrared simulator of landscapes. Proc. of SPIE,2001,4540:488-497
[64]Poglio T, Savaria E, Wald L, Outdoor Scene Synthesis in the Infrared Range for Remote Sensing Applications. presented at the CISST'02:International Conference on Imaging Science, Systems, and Technology, Las Vages, Nevada 2002.
[65]Pentecost H T A, Recognition T. Identification and Tracking Using Real and Synthetic IR Imagery. In:SPIE:Proc. SPIE,1996,2744:520-525
[66]Kustas W P, Norman J M. Anderson M C, et al. Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote Sensing of Environment,2003,85(4):429-440
[67]Kustas W P, Li F, Jackson T J, et al. Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa. Remote Sensing of Environment,2004,92(4):535-547
[68]谈和平,夏新林,刘林华,等.红外辐射特性与传输的数值计算：计算热辐射学.哈尔滨：哈尔滨工业大学出版社,2006.43-44
[69]Howell J R, Perlmutter M. Monte carlo solution of thermal transfer through radiant media between gray walls. Journal of Heat Transfer:Transactions of the ASME,1964,86(1):116-122
[70]程强,周怀春.具有漫反射边界一维灰性平行板介质中辐射传递方程的求解.工程热物理学报,2004,25(5)：831-833
[71]Olivier J, Yves K. SEISM,Scene Electro-optical Image generator.and Sensor Model. Proc. of SPIE,1997,3063:297-289
[72]Johnson K R, A.Curran, Less D, et al. MuSES:A New Heat and Signature Management Design Tool for Virtual Prototyping. In:Ninth Annual Ground Target Modeling & Validation Conference. Houghton,1998
[73]Krayenhoff E S, Voogt J A. A microscale three-dimensional urban energy balance model for studying surface temperatures. Boundary-Layer Meteorology, 2007,123:433-461
[74]Gastellu-Etchegorry J P, Martin E, Gascon F. DART:A 3-D model for simulating satellite images and surface radiation budget. International Journal of Remote Sensing,2004,25(1):75-96
[75]Poglio T, Mathieu-Marni S, Ranchin T, et al. OSIrIS:a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment,2006,104(2): 238-246
[76]Jacobs P a M. Simulation of the thermal behaviour of object and its nearby surroundings. TNO publication PHL,1980,8
[77]Sugawara H, Takamura T. Longwave radiation flux from an urban canopy: Evaluation via measurements of directional radiometric temperature. Remote Sensing of Environment,2006,104(2):226-237
[78]Mihalakakou G. On estimating soil surface temperature profiles. Energy and Buildings,2002.34(2002):251-259
[79]Qin Z, Pedro B, Arnon K. Numerical solution of a complete surface energy balance model for simulation of heat fluxes and surface temperature under bare soil environment. Applied Mathematics and Computation.2002,130(2002):171-200
[80]Sanchez J M, Kustas W P, Caselles V, et al. Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sensing of Environment,2008,112(3):1130-1143
[81]Herb W R, Janke B, Mohseni O, et al. Ground surface temperature simulation for different land covers. Journal of Hydrology,2008,356(3-4):327-343
[82]Sepulcre-Canto G, Zarco-Tejada P J, Sobrino J A, et al. Discriminating irrigated and rainfed olive orchards with thermal ASTER imagery and DART 3D simulation. Agricultural and Forest Meteorology,2009,149(6-7):962-975
[83]Norman J M. Modeling the complete crop canopy. In:B.J. BarfieldJ.F. Gerber, eds. Modification of the Aerial Environment of Plants. St. Joseph, MI.:ASAE Monogr. Am. Soc. Agric. Eng.,1979.249-277
[84]Norman J M. Synthesis of canopy processes. In:G. Russell, et al., eds. Plant canopies:Their growth, form and function. New York:Soc. Exp. Biol., Seminar Series 31, Cambridge University Press,1988.161-175
[85]Norman J M, Kustas W P, Humes K S. Source approach for estimating soil and vegetation energy fluxes in observations of directional radio metric surface temperature. Agricultural and Forest Meteorology,1995,77(3-4):263-293
[86]Kustas W, Anderson M. Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology,2009,149(12):2071-2081
[87]田国良.热红外遥感.北京：电子工业出版社,2006.72-75
[88]Labed J, Stoll M P. Angular variation of land surface spectral emissivity in the thermal infrared:Labor investigation on bare soils. International Journal of Remote Sensing,1991.12(11):2299-2310
[89]Rees W G, James S P. Angular variation of the infrared emissivity of ice and water surfaces. International Journal of Remote Sensing,1992,13(8):2873-2886
[90]Cuenca J, Sobrino J A. Experimental measurements for studying angular and spectral variation of thermal infrared emissivity. Appl Opt,2004,43(23):4598-4602
[91]Francois C, Ottle C, Prevot L. Analytical parametrisation of canopy emissivity and directional radiance in the thermal infrared:Application on the retrieval of soil and foliage temperatures using two directional measurements. International Journal of Remote Sensing,1997,18(2):2587-2621
[92]Kimes D S, Smith J A. Directional radio metric measurements of row-crop temperatures. International Journal of Remote Sensing,1983.4(2):299-311
[93]Li X. Strahler A H, Friedl M A. A conceptual model for effective directional emissivity from non-isothermal surface. IEEE transactions on geoscience and remote sensing,1999,37:2508-2517
[94]Yan G, Jiang L, Wang J, et al. Thermal bidirectional gap probability model for row crop canopies and validation. Science in China Series D:Earth Sciences. 2003,46(12):1241-1249
[95]Kimes D S. Remote sensing of row crop structure and component temperatures using directional radiometric temperatures and inversion techniques. Remote Sensing of Environment,1983,13(1):33-55
[96]Caselles V, Sobrino J A, Coll C. A physical model for interpreting the land surface temperature obtained by remote sensors over incomplete canopies. Remote Sensing of Environment,1992,39(3):203-211
[97]Voogt J A. Assessment of an Urban Sensor View Model for thermal anisotropy. Remote Sensing of Environment,2008.112(2):482-495
[98]Henon. SOLENE software.2008; Available from: http://www.cerma.archi.fr/CERMA/Expertise/solene/.
[99]Lagouarde J P, Henon A, Kurz B, et al. Modelling daytime thermal infrared directional anisotropy over Toulouse city centre. Remote Sensing of Environment,2010.114(1):87-105
[100]Sobrino J A, Jimenez-Munoz J C, Verhoef W. Canopy directional emissivity: Comparison between models. Remote Sensing of Environment,2005,99(3):304-314
[101]Menenti M, Jia L, Li Z L. Multi-angular thermal infrared observations of terrestrial vegetation. In:S. Liang, eds. Advances in Land Remote Sensing: System. Modeling. Inversion and Application. Berlin:Springer,2008.51-93
[102]Liang S. Quantitative Remote Sensing of Land Surfaces. Hoboken:John Wiley & Sons, Inc.,2004.256-260
[103]Clough S A, Iacono M J, Moncet J L. Line-by-line calculations of atmospheric fluxes and cooling rates:Application to water vapor. Journal of Geophysical Research,1992,97:15761-15785
[104]Kobayashi H. Line-by-line calculation using the Fourier-transformed Voigt function. J. Quant. Spectrosc. Radiat. Transfer,1999,62:477-483
[105]Hartmann J M, Levi Di Leon R, Taine J. Line-by-line and narrow-band statistical model calculations for H2O. J. Quant. Spectrosc. Radiat. Transfer,1984,32:119-127
[106]Lacis A A, Oinas V. A description of the correlated k distribution method for modeling nongray gaseous absorption, thermal emission, and multiple scattering in vertically inhomogeneous atmospheres. Journal of Geophysical Research, 1991,96:9027-9063
[107]Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for imhomogeneous atmospheres:RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research,1997,102:16663-16682
[108]Niemela S, Raisanen P, Savijarvi H. Comparison of surface radiative flux parameterizations:Part I:Longwave radiation. Atmospheric Research,2001, 58(1):1-18
[109]Iziomon M G, Mayer H, Matzarakis A. Downward atmospheric longwave irradiance under clear and cloudy skies:Measurement and parameterization. Journal of Atmospheric and Solar-Terrestrial Physics,2003,65(10):1107-1116
[110]Jordan J B. Watkins W R, Palacios F R, et al. Simulated dynamic effects of atmospheric turbulence on IR digital imagery. Infrared Physics & Technology, 1996,37(5):607-617
[111]Kopeika N S, Sadot D. Imaging through the atmosphere:practical instrumentation-based theory and verification of aerosol MTF. J. Opt. Soc. AM. A,1995,12(5):1017-1025
[112]Sadot D, Kopeika N S. Thermal imaging through the atmosphere:atmosphere MTF theory and verification. Opt. Eng.,1994,33(3):880-887
[113]Ricklin J C, Tomlison T G. Current challenges in atmospheric propagation research. Proc. of SPIE,2006,6215
[114]Christelle G. Physically-based infrared sensor efforts modeling. Proc. of SPIE, 1999,3701:81-94
[115]Noilhan J, Mahfouf J F. The ISBA land surface parameterization scheme Global and Plan. Change,1996,13:145-159
[116]Repasi E, Greif H J. Generation of dynamic IR-Scenes for ground-based systems and missile applications. Proc. of SPIE,1998,3436:460-461
[117]Li X W, Wang J D, Strahler A H. Scale effect of Planck's law over nonisothermal blackbody surface. SCIENCE IN CHINA (Series E),2000,42(6): 12-17
[118]Wan Z, Dozier J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE transactions on geoscience and remote sensing,1996,34(4):892-905
[119]Norman J M, Becker F. Terminology in thermal infrared remote sensing of natural surfaces. Agriculture and Forest Meteorology,1995,77(3-4):153-166
[120]Becker F, Li Z. Surface temperature and emissivity at various scales:Definition, measurement and related problems. Remote Sensing Reviews,1995,12:225-253
[121]Li X W, Wang J D. The definition of effective emissivity of land surface at the scale of remote sensing pixels. Chinese Science Bulletin,1999,44(23):2154-2154
[122]李新,程国栋,陈贤章.任意地形条件下太阳辐射模型的改进.科学通报,1999,44(9)：993-998
[123]李净.基于DEM的坡地太阳总辐射估算.太阳能学报,2007,28(8):905-911
[124]Wang Z, Peng Q, Lu Y, et al. A Global Infrared Image Synthesis Model for Large-Scale Complex Urban Scene. International Journal of Infrared and Millimeter Waves,2001,22(8):1193-1208
[125]Prata A J. A new long-wave formula for estimating downward clear-sky radiation at the surface. Quarterly Journal of the Royal Meteorological Society,1996. 122(533):1127-1151
[126]Kreith M, Bohn S M. Principles of Heat Transfer:West Publishing Company, 1993
[127]赵英时.遥感应用分析原理与方法.北京：科学出版社,2003
[128]Penman H L. Vegetation and Hydrology. Farnham:Royal Commonwealth Agricultural Bureaux,1963
[129]Kimball B A, Jackson R D. Soil heat flux. In:B.J. BarfieldJ.F. Gerber, eds. Modification of the Aerial Environment of Plants. St. Joseph, MI:American Society of Agricultural Engineers, 1979.211-229
[130]Hares M A, Novak M D. Simulation of surface energy balance and soil temperature under strip tillage:I Model description. Soil Sci. Soc. Am. J.,1992, 56:22-29
[131]Marshall T J, Holmes J W. Soil Physics. Cambridge, UK:Cambridge University Press,1979
[132]杨世铭,陶文铨.传热学.北京：高等教育出版社,1998
[133]Krarti M, Lopez-Alonzo C, Claridge D E, et al. Analytical model to predict annual soil surface temperature variation. J. Solar Energy Engng.,1995,117:91-99
[134]Patankar S V. Numerical Heat Transfer and Fluid Flow. New York:McGraw-Hill,1980
[135]Carslaw H S, Jaeger J C. Conduction of Heat in Solids.2nd ed. Oxford:Oxford Science Publishers,1980
[136]Mihalakakou G, Santamouris M, Lewis J O, et al. On the application of the energy balance equation to predict ground temperature profiles. Solar Energy, 1996,60(3-4):181-190
[137]Geiger R. The Climate Near Ground. Cambridge, MA:Harvard University Press, 1961
[138]Jacquemoud S, Verhoef W, Baret F, et al. PROSPECT+SAIL models:A review of use for vegetation characterization. Remote Sensing of Environment,2009, 113(Supplement 1):S56-S66
[139]张建奇.方小平,张海兴.植被红外辐射特性理论模拟.西安电子科技大学学报,1997,24：386-390
[140]刘强,陈良富,柳钦火.作物冠层的热红外辐射传输模型.遥感学报,2003,7(3)：161-167
[141]Inclana M G, Forkela B R. Comparison of Energy Fluxes Calculated with the Penman-Monteith Equation and the Vegetation Models SiB and Cupid. Journal of Hydrology,1995,166(3-4):193-211
[142]Voss R D, FRACTALS in NATURE:characterization, measurement, and simulation. presented at the SIGGRAPH 1987,1987.
[143]英振华,基于分形理论的地形场景实时真实感绘制，硕士,重庆大学,2005.
[144]Miller G S P, The Definition and Rendering of Terrain Maps. presented at the SIGGRAPH 1986,1986.
[145]陈明昊，三维分形地形生成关键技术的研究与实现.硕士工学,电子技术学院,解放军信息工程大学,郑州,2008.
[146]齐敏，郝重阳,佟明安.三维地形生成及实时显示技术研究进展.中国图象图形学报,2000,5(A)(4)：269-271
[147]何方容,戴光明.三维分形地形生成技术综述.武汉化工学院学报,2002,24(3)：85-88
[148]Maeder R E. Two-Dimensional Fractional Brownian Motion. the Wolfram Demonstrations Project; Available from: http://demonstrations.wolfram.com/TwoDimensionalFractionalBrownianMition/.
[149]Nash E B, Conel J E. Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite. Journal of Geophysical Research,1974, 79:1615-1621
[150]Singer R B, Mccord T B. Mars:Large scale mixing of bright and dark surface materials and implications for analysis of spectral reflectance. In:10th Lunar and Planetary Science Conference:Proc. of Conference,1979:1835-1848
[151]Segl K, Roessner S, Heiden U, et al. Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing,2003, 58:99-112
[152]Schowengerdt R A. Remote Sensing:Models and Methods for Image Processings. New York:Academic Press,1997.522
[153]Richards J A, Jia X. Remote Sensing Digital Image Analysis. New York: Springer-Verlag,1999.363
[154]Valor E, Caselles V. Mapping land surface emissivity from NDVI:Application to European, African, and South American areas. Remote Sensing of Environment, 1996,57(3):167-184
[155]Mcfeeters S K. The use of Normalized Defference Water Index(NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 1996,17(7):1425-1432
[156]吴宏安,蒋建军,张海龙.比值居民地指数在城镇信息提取中的应用.南京师大学报(自然科学版)，2006,29(3)：118-121
[157]Jimenez-Munoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research,2003.108(D22):4688-4695
[158]Dozier J, Frew J. Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE transactions on geoscience and remote sensing, 1990,28(5):963-969
[159]牛铮,柳钦火,高彦春,等.零散射近似的冠层热红外辐射间隙率模型.中国科学E辑,2000,30(z1)：89-98
[160]王锦地,李小文,苏红波,等.两组分非同温像元热辐射中多次散射影响的解析模型和验证.遥感学报,2003,7(1)：1-7
[161]Chen L F, Zhuang J L, Xu X R, et al. The concept of effective emissivity of nonisothermal mixed pixel and its test. Chinese Science Bulletin,2000,45(9): 788-795
[162]余其铮.辐射换热原理.哈尔滨：哈尔滨工业大学出版社,2000.41-45
[163]杨贤荣,马庆芳,原庚新,et al辐射换热角系数手册.北京：国防工业出版社,1982.44-74
[164]Li Z-L, Zhang R, Sun X, et al. Experimental system for the study of the directional thermal emission of natural surfaces. International Journal of Remote Sensing,2004,25(1):195-204
[165]Zhang R H, Sun X M, Li Z L, et al. Revealing of major factors in the directional thermal radiation of ground objects. SCIENCE IN CHINA (Series E),2000, 43(supplement):34-40
[166]Dozier J, Wagner S. Effect of viewing angle on the infrared brightness temperature of snow. Water Resource Research,1982,18(5):1424-1434
[167]李小文,王锦地,Strahler A H尺度效应及几何光学模型用于尺度纠正.中国科学E辑：技术科学,2000,30(增刊)：12-17
[168]苏理宏,李小文,王锦地.地表非同温像元发射率的Monte Carlo模拟.自然科学进展，2002,12(4)：446-448
[169]苏理宏,李小文，王锦地.三维结构非同温像元热辐射的尺度效应.自然科学进展,2002,12(1)：51-55
[170]朱利.顾行发,陈良富,等.高分辨率红外相机单窗与劈窗陆表温度反演精度分析研究.红外与毫米波学报,2008,27(5)：346-353
[171]Fried D. Optical resolution through a randomly imhomogeneous medium for very long and very short exposure. J.Opt.Soc.Am,1966,56(10):1372-1379
[172]Xiaoying L, Xingfa G, Tao Y, et al. Atmospheric scattering and turbulence modulation transfer function for CCD cameras on CBERS-02b and HJ-1 A/1 B. International Journal of Remote Sensing,2011, in Press
[173]赵利民,余涛,田庆久,等HJ-1B热红外遥感数据陆表温度反演误差分析.光谱学与光谱分析，2010,30(12)：3359-3362
[174]Zhao L-M, Yu T, Tian Q-J, et al. Atmospheric Sensitivity on Land Surface Temperature Retrieval Using Single Channel Thermal Infrared Remote Sensing Data:Comparison between Models. In:Geoinformatics2010. Beijing,2010
[175]陈玻若.红外系统.北京：国防工业出版社,1987
[176]王波，黄曦.基于OGRE的红外成像传感器典型物理效应仿真.电子科技，2010，23(3)：36-39
[177]宁殿艳，星载推扫型红外成像传感器建模与仿真,硕士学位论文,西安电子科技大学，西安,2008.
[178]韩意.用Vega生成真实传感器效果的光电成像模拟方法.计算机应用,2009,29(z1)：342-344
[179]Hadar O, Robbins M, Novogrozhy Y, et al. Image motion restoration from a sequense of images. Opt. Eng.,1996,35(10):2898-2904
[180]Fiete R D, Tantalo T A. Image quality of increased along-sampling for remote sensing system. Opt. Eng.,1999,38:815-820
[181]公发全.亚像元线阵CCD推扫成像系统调制传递函数分析方法.光电技术,2002,28(3)：293-244
[182]徐鹏,黄长宁,王涌天.卫星振动对成像质量影响的仿真分析.宇航学报,2003,24(3)：259-263
[183]赵贵军,陈长征,万志.推扫型TDI CCD光学遥感器动态成像研究.光学精密工程，2006,14(2)：291-296
[184]张建奇,方小平.红外物理——研究生系列教材.西安：西安电子科技大学出版社,2004
[185]韩心志.航天遥感CCD扫帚式成像系统.哈尔滨：哈尔滨工业大学出版社,1990
[186]韩心志,焦世举.航天光学遥感辐射度学：国防科技出版社,1994
[187]齐怀川,黄巧林,胡永力.空问光学遥感运动模糊仿真方法研究.航天返回与遥感,2010.31(2)：51-56
[188]周虎.航天光学遥感器抖动补偿方法研究，硕士学位论文，中国空间技术研究院,2008.
[189]邹前进.红外成像系统噪声测量仿真研究.红外技术,2008,30(6)
[190]Wenaas H. Computer-generated correlated noise images for various statistical distributions. Proc.SPIE.,1991,1569:410-421
[191]C.Holst. G. Testing and Evaluation of Infrared Imaging Systems.2nd ed:JCD Publishing and SPIE Optical Engineering Press,1998
[192]O'shea P, Sousk S. Practical Issue with 3D-Noise Measurements and Application to Modern Infrared Sensors. Proc. SPIE.2005,5784:262-271
[193]Yue-Ping Z, Jian-Qi Z, Xi H. Influence of detector noise on infrared images. Proc.SPIE.,2001,4548:381-386
[194]李旭东,胡铁力,岳文龙,等.红外热像仪SiTF的测试研究.应用光学.2005,26(5)：21-24
[195]Richardson P, Miller B. Third Generation FLIR Simulation at NVESD. Proc. SPIE,2007,6543:65430K-1-65430K-11
[196]李旭东.扫描热成像系统NETD数学模型的研究.应用光学,2004，25(4)
[197]Markham B, Townshend J, R.G. Land Cover Classification Accuracy as a Function of Sensor Spatial Resolution. Proceedings 15th International Symposium on Remote Sensing of Environment,1981
[198]龚明劫.卫星遥感制图最佳影像空间分辨率与地图比例尺关系探讨.测绘科学,2009,34(4)