长白山地区森林土壤含水量定量遥感研究
|
摘要
长白山地区大部分是森林生态系统,是我国东北地区和东北亚地区最重要的生态屏障,也是我国北方生态环境最好的区域之一。在长白山森林生态系统中,森林土壤是整个系统的重要基础,而土壤含水量是土壤中的一个重要组成部分,对陆地与大气间的热量平衡、陆地表面大气环流和土壤温度均产生显著的影响,同时也是评价土壤资源优劣的主要特征之一。运用遥感技术的宏观、准确、动态等显著优势来研究长白山地区森林土壤,有着常规监测方法不可替代的优点,尤其是利用多角度、多时相、高光谱、偏振信息来获取土壤在2π空间内的三维光谱特征,是当今遥感技术的重要研究发展方向。
本论文从微观角度和宏观角度出发,对长白山森林土壤同时进行研究与分析。遥感技术的基础是物体对电磁波所呈现的固有特性(包括波谱强度和偏振特性以及物体本身的集合特性特征)的描述。从微观角度出发,本文对长白山地区森林土壤进行了多角度偏振高光谱的光谱测量,充分考虑光线天顶角、光线方位角、探测天顶角、探测方位角以及波长的变化对土壤特征的影响,通过偏振反射特征获得与其对应的二向性反射信息、土壤含水量信息等,对长白山森林土壤建立土壤含水量与光谱信息的数学模型,同时把多角度遥感、偏振遥感以及高光谱遥感结合起来,为定量遥感的发展提供有力地支持。从宏观角度出发,本文利用MODIS产品数据中的NDVI与LST,建立TVDI三角形,构成三角形的“干湿边”,反演长白山地区大范围的土壤湿度,与实测数据相对比分析。论文主要工作内容与结论如下:
(1)长白山森林土壤不同含水量条件下的多角度偏振高光谱特征
通过对比分析,发现干燥的森林土壤没有明显的偏振反射特性;入射角度一定时,森林土壤的偏振反射的总体趋势是随着探测角的减小而略有减小,波形变化不大,而且不会因探测角度的改变而产生不同的偏振反射特征。在不同含水量的状况下,森林土壤的偏振反射曲线发生变化,当探测角与入射角度相同时,在180°方位角时的偏振反射比时最大的;不同偏振状态下,偏振角45°(即二向性反射比)时的偏振反射比位于偏振角度为0°和90°的偏振反射曲线中间;在入射角、探测角、方位角和偏振状态一致情况下,不同含水量土壤光谱曲线产生差异,随着含水量的增大,其多角度偏振高光谱反射比减小,达到某含水量后,随着含水量的增大其反射比也随之增大。
(2)建立多角度偏振高光谱与土壤含水量之间的回归模型
经过对长白山地区森林土壤众多曲线的研究,发现森林土壤多角度偏振高光谱具有4大基本特征,其中与水分相关的是1450nm附近的吸收谷和1940nm附近的吸收谷。对这两个水分吸收谷的光谱值、吸光度、微分形式与不同偏振状态下的含水量进行回归建模,得到不同偏振状态下的模型及其相关系数;并构建1450nm附近和1940nm附近的偏振度与含水量之间的线性模型,得到相关系数,并进行统计检验,发现符合统计规律。利用偏振度与含水量之间的线性模型,确定土壤表现为非朗伯体特性的含水量临界值,并计算得之。
(3)设计正交试验,进行交互作用分析
对不同含水量条件下的森林土壤的多角度偏振高光谱进行正交试验设计与交互作用分析,设计4因素2水平正交试验表格,发现偏振角、探测角、含水量以及方位角均可以对森林土壤的多角度偏振高光谱曲线产生影响,而且这些影响是通过这4个因素之间的交互作用体现的。结果发现,偏振角与含水量的交互作用对森林土壤多角度偏振高光谱曲线影响特别显著,含水量对其曲线也同样影响特别显著,二者均起到了首要作用;探测角与含水量的交互作用对光谱曲线影响显著,而偏振角对其有一定的影响,其它因素对森林土壤多角度偏振高光谱影响不大。
(4)利用MODIS产品数据大范围监测长白山地区土壤水分状况
运用MODIS产品数据中的NDVI和LST产品,对长白山地区土壤水分进行相关研究,拟合干湿边方程,得到参数,建立TVDI模型反演长白山地区2007年3月~11月的土壤水分状况,同时使用野外采点数据进行长白山地区土壤水分验证。通过对TVDI和土壤相对湿度的线性拟合结果和T检验,发现线性回归方程为显著,说明TVDI能够反映长白山地区土壤含水量状况,根据获取的遥感信息展示长白山地区土壤水分状况的空间分布,并与具有时间序列的MODIS产品数据对比分析TVDI和地表植被覆盖类型的关系。
论文的创新之处在于综合微观角度与宏观角度的同时,分析了长白山地区森林土壤水分状况,获得不同含水量条件下的森林土壤光谱曲线与参数,突破了传统研究中仅仅利用单一指标研究和表达土壤水分信息的片面性;同时运用正交试验设计与交互实验分析的方法,展示多角度因素、偏振因素、高光谱因素与含水量因素的交互影响作用,克服传统单一因素分析光谱曲线的弊端,体现了因素间的交互作用对长白山地区森林土壤多角度偏振高光谱特征的影响。
Most of the Changbai Mountains area is the forest ecological system, and also the most important ecological barrier in the northeast China and the northeast Asia. It is one of the best ecological environment in the northeast China. The forest soil is the basis of the ecological forest system of the Changbai Mountains, and the moisture is one of the most important parts in the soil. It has the impact on the heat balance between land and atmospheric, land surface atmospheric circulation and the land surface temperature. At the same time it is one of the most important characteristics to evaluate the soil resource. Many advantages in monitoring the forest soil in the Changbai Mountains are revealed by remote sensing technology, especially using the multi-angle information, hyperspectrum and polarized information to obtain the 3-D characteristics of the soil in 2πspace. It is the important direction to research modern remote sensing technology.
In this paper, the author researched and analyzed the forest soil in the Changbai Mountains from both micro-perspective and macro-perspective point. The basis of remote sensing technology is to describe the characteristics of the electromagnetic waves, including the spectral intensity, polarized properties and the features of the object itself. From the microscopic point, the author had full consideration to the forest soil spectral in Changbai Mountains, for example, the light zenith angle, azimuth angle, the effects of wavelength and the polarized information. The math-models were built about the forest soil moisture through the polarized reflection characteristics and the bidirectional reflectance information, while take the combination of the multi-angle information, hyperspectral and the polarized information. That could be the strong support for the development of the quantitative remote sensing. From the macro-perspective, the author used the NDVI and Land Surface Temperature (LST) in the MODIS product data and establishes the TVDI triangle. The triangle has the wet and the dry side and we could get the inversion of the soil moisture in Changbai Mountains. And we could get the analysis on the results with the measured data. The content and conclusions of the thesis work are as follows:
(1) The characteristics of the multi-angle hyperspectral polarized information of the forest soil in Changbai Mountains with different moisture conditions
It is found that the dry forest soil has no significant on the reflectance characteristics of polarization through comparative analysis; when the incidence angle is fixed, the trend of the polarized reflection is decreased with the viewing zenith angle decreased and the wave shape has little change. The polarized reflection characteristics would not change with the different viewing zenith angle. The polarized reflection curves changed with the different soil moisture conditions. When the incidence angle equals to the viewing zenith angle, the polarized reflection is the biggest under the relative azimuth 180°; the curve of polarizer 45°is between the angle of 0°and 90°; the same circumstances of the incidence angle, viewing zenith angle, the relative azimuth angle and the polarized angle, the curves have different spectral under different soil moisture. With the moisture increased, the multi-angle hyperspectral polarized reflection is decreased, while reaches some certain moisture content, the reflection is increased with the moisture increased.
(2) Regression models between multi-angle hyperspectral polarized information and the soil moisture
After a large number of curves of the forest soil in Changbai Mountains, it is found that the multi-angle hyperspectral polarized information has 4 major basic characteristics and the associated with the moisture is the reflection valley near 1450nm and 1940nm. The water absorption spectrums in the polarization peaks have the correlation relationship with the moisture under different polarized states. We could also get the models and the correlation coefficient. The models build aroud 1450nm and 1940nm with the degree of polarization and the moisture could go through the statistical test, and we could also find the statistical rules.
(3) Making orthogonal design for the interaction analysis
The author made an orthogonal design of the multi-angle hyperspectral polarized information of the forest soil under different moisture, and also had the interaction analysis. It is found that the polarized angle, viewing zenith angle and the relative azimuth angle could have the effects on the multi-angle hyperspectral polarized reflection, and those effects were showed by the interaction. The results showed that the polarized angle and the soil moisture have an especially significant on the curves, the moisture also has the especially significance, and both of the above two played a primary role. The interaction of the viewing zenith angle and the moisture has significant on the curves and polarized angle has particularly significant. All the other factors have little effect on the multi-angle hyperspectral polarized reflection of the forest soil.
(4) Monitoring the soil moisture conditions in Changbai Mountais area by the MODIS product data
The author analyzed the soil moisture in Changbai Mountains using the NDVI and LST information in MODIS product data. We established the TVDI model by the wet and dry functions and the parameters and inverse the soil moisture conditions from March to November in 2007 in Changbai Mountains area. The significant linear regression equation could be found by TVDI and soil relative moisture and T test, which means the TVDI could reflect the soil moisture conditions in Changbai Mountains area. And the author analyzed the surface vegetation types and TVDI by the MODIS product data from the spatial distribution of the soil moisture condition according to remote sensing information.
The innovation of the paper is to analysis the soil moisture conditions under both micro level and the macro level and could obtain the forest soil spectral curves and parameters with different water contents, which breaks the traditional study of expression to use only a single index. At the same time the author used the orthogonal experimental design and interaction analysis. That shows the interaction factors of the multi-angle factor, polarized factor, hyperspectral factor and the moisture factor. The paper reflects the interaction of the factors on the multi-angle hyperspectral polarized characteristics of the forest soil in the Changbai Mountains area, which overcomes the disadvantages of traditional single factor analysis.
本论文从微观角度和宏观角度出发,对长白山森林土壤同时进行研究与分析。遥感技术的基础是物体对电磁波所呈现的固有特性(包括波谱强度和偏振特性以及物体本身的集合特性特征)的描述。从微观角度出发,本文对长白山地区森林土壤进行了多角度偏振高光谱的光谱测量,充分考虑光线天顶角、光线方位角、探测天顶角、探测方位角以及波长的变化对土壤特征的影响,通过偏振反射特征获得与其对应的二向性反射信息、土壤含水量信息等,对长白山森林土壤建立土壤含水量与光谱信息的数学模型,同时把多角度遥感、偏振遥感以及高光谱遥感结合起来,为定量遥感的发展提供有力地支持。从宏观角度出发,本文利用MODIS产品数据中的NDVI与LST,建立TVDI三角形,构成三角形的“干湿边”,反演长白山地区大范围的土壤湿度,与实测数据相对比分析。论文主要工作内容与结论如下:
(1)长白山森林土壤不同含水量条件下的多角度偏振高光谱特征
通过对比分析,发现干燥的森林土壤没有明显的偏振反射特性;入射角度一定时,森林土壤的偏振反射的总体趋势是随着探测角的减小而略有减小,波形变化不大,而且不会因探测角度的改变而产生不同的偏振反射特征。在不同含水量的状况下,森林土壤的偏振反射曲线发生变化,当探测角与入射角度相同时,在180°方位角时的偏振反射比时最大的;不同偏振状态下,偏振角45°(即二向性反射比)时的偏振反射比位于偏振角度为0°和90°的偏振反射曲线中间;在入射角、探测角、方位角和偏振状态一致情况下,不同含水量土壤光谱曲线产生差异,随着含水量的增大,其多角度偏振高光谱反射比减小,达到某含水量后,随着含水量的增大其反射比也随之增大。
(2)建立多角度偏振高光谱与土壤含水量之间的回归模型
经过对长白山地区森林土壤众多曲线的研究,发现森林土壤多角度偏振高光谱具有4大基本特征,其中与水分相关的是1450nm附近的吸收谷和1940nm附近的吸收谷。对这两个水分吸收谷的光谱值、吸光度、微分形式与不同偏振状态下的含水量进行回归建模,得到不同偏振状态下的模型及其相关系数;并构建1450nm附近和1940nm附近的偏振度与含水量之间的线性模型,得到相关系数,并进行统计检验,发现符合统计规律。利用偏振度与含水量之间的线性模型,确定土壤表现为非朗伯体特性的含水量临界值,并计算得之。
(3)设计正交试验,进行交互作用分析
对不同含水量条件下的森林土壤的多角度偏振高光谱进行正交试验设计与交互作用分析,设计4因素2水平正交试验表格,发现偏振角、探测角、含水量以及方位角均可以对森林土壤的多角度偏振高光谱曲线产生影响,而且这些影响是通过这4个因素之间的交互作用体现的。结果发现,偏振角与含水量的交互作用对森林土壤多角度偏振高光谱曲线影响特别显著,含水量对其曲线也同样影响特别显著,二者均起到了首要作用;探测角与含水量的交互作用对光谱曲线影响显著,而偏振角对其有一定的影响,其它因素对森林土壤多角度偏振高光谱影响不大。
(4)利用MODIS产品数据大范围监测长白山地区土壤水分状况
运用MODIS产品数据中的NDVI和LST产品,对长白山地区土壤水分进行相关研究,拟合干湿边方程,得到参数,建立TVDI模型反演长白山地区2007年3月~11月的土壤水分状况,同时使用野外采点数据进行长白山地区土壤水分验证。通过对TVDI和土壤相对湿度的线性拟合结果和T检验,发现线性回归方程为显著,说明TVDI能够反映长白山地区土壤含水量状况,根据获取的遥感信息展示长白山地区土壤水分状况的空间分布,并与具有时间序列的MODIS产品数据对比分析TVDI和地表植被覆盖类型的关系。
论文的创新之处在于综合微观角度与宏观角度的同时,分析了长白山地区森林土壤水分状况,获得不同含水量条件下的森林土壤光谱曲线与参数,突破了传统研究中仅仅利用单一指标研究和表达土壤水分信息的片面性;同时运用正交试验设计与交互实验分析的方法,展示多角度因素、偏振因素、高光谱因素与含水量因素的交互影响作用,克服传统单一因素分析光谱曲线的弊端,体现了因素间的交互作用对长白山地区森林土壤多角度偏振高光谱特征的影响。
Most of the Changbai Mountains area is the forest ecological system, and also the most important ecological barrier in the northeast China and the northeast Asia. It is one of the best ecological environment in the northeast China. The forest soil is the basis of the ecological forest system of the Changbai Mountains, and the moisture is one of the most important parts in the soil. It has the impact on the heat balance between land and atmospheric, land surface atmospheric circulation and the land surface temperature. At the same time it is one of the most important characteristics to evaluate the soil resource. Many advantages in monitoring the forest soil in the Changbai Mountains are revealed by remote sensing technology, especially using the multi-angle information, hyperspectrum and polarized information to obtain the 3-D characteristics of the soil in 2πspace. It is the important direction to research modern remote sensing technology.
In this paper, the author researched and analyzed the forest soil in the Changbai Mountains from both micro-perspective and macro-perspective point. The basis of remote sensing technology is to describe the characteristics of the electromagnetic waves, including the spectral intensity, polarized properties and the features of the object itself. From the microscopic point, the author had full consideration to the forest soil spectral in Changbai Mountains, for example, the light zenith angle, azimuth angle, the effects of wavelength and the polarized information. The math-models were built about the forest soil moisture through the polarized reflection characteristics and the bidirectional reflectance information, while take the combination of the multi-angle information, hyperspectral and the polarized information. That could be the strong support for the development of the quantitative remote sensing. From the macro-perspective, the author used the NDVI and Land Surface Temperature (LST) in the MODIS product data and establishes the TVDI triangle. The triangle has the wet and the dry side and we could get the inversion of the soil moisture in Changbai Mountains. And we could get the analysis on the results with the measured data. The content and conclusions of the thesis work are as follows:
(1) The characteristics of the multi-angle hyperspectral polarized information of the forest soil in Changbai Mountains with different moisture conditions
It is found that the dry forest soil has no significant on the reflectance characteristics of polarization through comparative analysis; when the incidence angle is fixed, the trend of the polarized reflection is decreased with the viewing zenith angle decreased and the wave shape has little change. The polarized reflection characteristics would not change with the different viewing zenith angle. The polarized reflection curves changed with the different soil moisture conditions. When the incidence angle equals to the viewing zenith angle, the polarized reflection is the biggest under the relative azimuth 180°; the curve of polarizer 45°is between the angle of 0°and 90°; the same circumstances of the incidence angle, viewing zenith angle, the relative azimuth angle and the polarized angle, the curves have different spectral under different soil moisture. With the moisture increased, the multi-angle hyperspectral polarized reflection is decreased, while reaches some certain moisture content, the reflection is increased with the moisture increased.
(2) Regression models between multi-angle hyperspectral polarized information and the soil moisture
After a large number of curves of the forest soil in Changbai Mountains, it is found that the multi-angle hyperspectral polarized information has 4 major basic characteristics and the associated with the moisture is the reflection valley near 1450nm and 1940nm. The water absorption spectrums in the polarization peaks have the correlation relationship with the moisture under different polarized states. We could also get the models and the correlation coefficient. The models build aroud 1450nm and 1940nm with the degree of polarization and the moisture could go through the statistical test, and we could also find the statistical rules.
(3) Making orthogonal design for the interaction analysis
The author made an orthogonal design of the multi-angle hyperspectral polarized information of the forest soil under different moisture, and also had the interaction analysis. It is found that the polarized angle, viewing zenith angle and the relative azimuth angle could have the effects on the multi-angle hyperspectral polarized reflection, and those effects were showed by the interaction. The results showed that the polarized angle and the soil moisture have an especially significant on the curves, the moisture also has the especially significance, and both of the above two played a primary role. The interaction of the viewing zenith angle and the moisture has significant on the curves and polarized angle has particularly significant. All the other factors have little effect on the multi-angle hyperspectral polarized reflection of the forest soil.
(4) Monitoring the soil moisture conditions in Changbai Mountais area by the MODIS product data
The author analyzed the soil moisture in Changbai Mountains using the NDVI and LST information in MODIS product data. We established the TVDI model by the wet and dry functions and the parameters and inverse the soil moisture conditions from March to November in 2007 in Changbai Mountains area. The significant linear regression equation could be found by TVDI and soil relative moisture and T test, which means the TVDI could reflect the soil moisture conditions in Changbai Mountains area. And the author analyzed the surface vegetation types and TVDI by the MODIS product data from the spatial distribution of the soil moisture condition according to remote sensing information.
The innovation of the paper is to analysis the soil moisture conditions under both micro level and the macro level and could obtain the forest soil spectral curves and parameters with different water contents, which breaks the traditional study of expression to use only a single index. At the same time the author used the orthogonal experimental design and interaction analysis. That shows the interaction factors of the multi-angle factor, polarized factor, hyperspectral factor and the moisture factor. The paper reflects the interaction of the factors on the multi-angle hyperspectral polarized characteristics of the forest soil in the Changbai Mountains area, which overcomes the disadvantages of traditional single factor analysis.
引文
[1]张清,周可法,赵庆展,等.区域土壤水分遥感反演方法研究[J].新疆地质,2008,26(1):107-116.
[2]刘焕军.松嫩平原主要土壤高光谱定量遥感研究[D]:[博士学位论文].中国科学院东北地理与农业生态研究所,2008.
[3]尹改,王桥,郑炳辉,等.国家环保总局对中国资源卫星的需求与分析[J].卫星应用,1999,7(1):11-20.
[4] Martin P D, Malley D F, Manning G, Fuller L. Determination of soil organic carbon and nitrogen at the field level using nearing-infrared spectroscopy [J]. Can. J. Soil Sci, 2002, 82: 413-422.
[5] He Y, Song H Y, Pereira A G, et al., A New Approach to Predict N, P, K and OM Content in a Loamy Mixed Soil by Using Near Infrared Reflectance Spectroscopy [J]. Lecture Notes in Computer Science, 2005, 3644: 859-867.
[6] Fox G A, Sabbagh G J. Estimation of Soil Organic Matter from Red and Near-infrared Remotely Sensed Data Using a Soil Line Euclidean distance Technique [J]. Soil Sci. Soc. Am. J., 2002, 66: 1922-1929.
[7]张红梅,沙晋明.遥感监测土壤湿度方法综述[J].中国农业通报,2005, 21(2): 307-311.
[8]邓辉,周清波.土壤水分遥感监测方法进展[J].中国农业资源与区划,2004, 25(3):46-49.
[9] Odlare M, Svensson K, Pell M. Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field [J]. Geoderma, 2005, 126:193-202.
[10]尹殿奎,冯君,杨志超.长春市主要土壤类型光谱特性的研究[J].吉林农业大学学报,1998,20(3):57-59.
[11]梅安新,彭望琭,秦其明等,遥感导论[M],2002,北京:高等教育出版社.
[12]李小文,王锦地.植被光学遥感模型和植被结构参数化[M]。1995,北京:科学出版社.
[13] Chen J M, Leblanc S G. A 4-scale bidirectional reflectance model based on canopy architecture [J]. IEEE Transctions on Geoscience and Remote Sensing, 1997, 35:1316-1337.
[14] Liang S. Quantitative Remote Sensing of Land Surfaces. New York: Willey. 2004.
[15]宫鹏,遥感科学与技术中的一些前沿问题,遥感学报,2009,13(1):13-23.
[16] Bachman C G. Laser Radar Systems and Techniques. Norwood(MA): Artech House, 1979.
[17] Nelson R F, Krabill W B, Maclean G A. Determining forest canopy characteristics using airborne laser data[J]. Remote Sensing of Environment, , 1984, 15: 201-212
[18]李树楷,薛永祺.高效三维遥感集成技术系统[M]. 2000,北京:科学出版社.
[19] Szewczyk R, Osterweil E, Polastre J, et al., Habitat monitoring by sensor network [J]. Communications of the ACM, 2004, 47(6):34.
[20]宫鹏.环境监测中无线传感器网络地面遥感新技术[J].遥感学报,2007,11(4):545-551.
[21]宫鹏,黎夏,徐冰.高分辨率遥感影像解译理论和应用方法中的一些研究问题[J],遥感学报,2006,10(1):1-4.
[22] Hui F M, Xu B, Huang H B, et al., Modeling spatial-temporal change of Poyang Lake using multi-temporal Landsat imagery [J]. International Journal of Remote Sensing, 2008, 29(20): 5767-5784;
[23] Weng Y L, Gong P, Zhu Z L, Reflectance spectroscopy for the assessment of salt content in soil of the Yellow River Delta of China [J]. International Journal of Remote Sensing, 2008, 29(19): 5511-5531.
[24] Zhou S Q, Liang X, Chen J X, An assessment of VIC-3L hydrological model for the Yangtze River Basin based on remote sensing- A case study of Baohe watershed [J]. Canadian Journal Remote Sensing, 2004, 32(5): 840-853.
[25] Crosson W L, Laymom C A, Inguva R, et al., Assimilating remote sensing data in a surface flux-soil moisure model [J]. Hydrological Processes, 2002, 16:1645-1662.
[26] Lu L X, Shuttleworth W J, Incorporating NDVI-derived LAI into the climate version of RAMS and its impact on regional climate [J]. Journal of Hydrometeorology, 2002, 3:347-361.
[27] Olioso A, Inoue Y, Orega-Farias S, et al., Future directions for advanced evaptranspiration modeling: assimilation of remote sensing data into crop simulation models and SVAT models [J]. Irrigations and Drainage Systems. 2005, 19:377-412.
[28]宫鹏,徐冰,梁松.用遥感与地理信息系统研究传染病时空分布[J],中国科学C辑:生命科学,2006,36(2):184-192.
[29] Russell E E, Watts L A, Pellicori S F, et al. Orbiter cloud photopolarimeter for the Pioneer Venus mission[C]. 1977.
[30] Banse K, English D C. Geographical differences in seasonality of CZCS-derived phytoplankton pigment in the Arabian Sea for 1978-1986[J]. Deep Sea Research Part II: Topical Studies in Oceanography. 2000, 47(7-8): 1623-1677.
[31] Egan W G, Johnson W R, Whitehead V S. Terrestrial polarization imagery obtained from the Space Shuttle: characterization and interpretation [J]. Applied Optics. 1991, 30(4): 435-442.
[32] Roger J C, Santer R, Herman M, et al. Polarization of the solar light scattered by the Earth-atmosphere system as observed from the US shuttle [J]. Remote Sensing of Environment. 1994, 48(3): 275-290.
[33] Travis L D. Satellite retrieval of aerosol properties over the ocean using polarization as well as intensity of reflected sunlight [J]. Journal of Geophysical Research. 1997, 102(D14): 16, 917-989, 13.
[34] Deschamps P Y, Bréon F M, Leroy M, et al. The POLDER mission: instrument characteristics and scientificobjectives [J]. IEEE Transactions on geoscience and remote sensing. 1994, 32(3): 598-615.
[35] Jensen G L, Peterson J Q. Hyperspectral imaging polarimeter in the infrared[C]. 1998.
[36] Peterson J Q, Jensen G L, Greenman M E, et al. Calibration of the hyperspectral imaging polarimeter[C]. 1999.
[37] Fougnie B, Bracco G, Lafrance B, et al. PARASOL in-flight calibration and performance[J]. Applied optics. 2007, 46(22): 5435-5451.
[38] Winker D M, Pelon J, Mccormick M P. The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds[C]. 2003.
[39] Mishchenko M, Penner J, Anderson D. Global aerosol climatology project[J]. J Atmos Sci. 2002, 59: 249-783.
[40]罗睿智,乔延利,曹汉军.航空型多波段偏振遥感探测及其光学系统的研究与设计[J].量子电子学报, 2002,19(2):143-148
[41]杨伟峰,潘玲,洪津;多波段偏振CCD相机的辐射定标研究;高技术通讯2004,10
[42]罗睿智,乔延利,郝沛明等.偏振CCD相机光学系统的偏振特性分析.量子电子学报,2004,21(3):396-400.
[43]赵慧洁,屈玉福,殷雪冰,等.高光谱全偏振成像遥感系统[P].专利号:200510086926中国,2007年.
[44] Grant L. Diffuse and specular characteristics of leaf reflectance[J]. Remote Sensing of Environment. 1987, 22(2): 309-322.
[45] Woessner P, Hapke B. Polarization of light scattered by clover[J]. Remote Sensing of Environment. 1987, 21(3): 243-261.
[46] Brakke T W, Smith J A, Harnden J M. Bidirectional scattering of light from tree leaves[J]. Remote Sensing of Environment. 1989, 29(2): 175-183.
[47] Painter T H, Paden B, Dozier J. Automated spectro-goniometer: A spherical robot for the field measurement of the directional reflectance of snow[J]. Review of Scientific Instruments. 2003, 74: 5179.
[48] Ben-Dor B, Oppenheim U P, Balfour L S. Polarization properties of targets and backgrounds in the infrared [1971-07][C]. SPIE INTERNATIONAL SOCIETY FOR OPTICAL, 1992.
[49] Thulstrup E W, Michl J. Elementary polarization spectroscopy[M]. Wiley-VCH, 1989.
[50] Tyo J S, Goldstein D L, Chenault D B, et al. Review of passive imaging polarimetry for remote sensing applications [J]. Applied optics. 2006, 45(22): 5453-5469.
[51]曾延安.偏振成像系统关键技术研究[D].华中科技大学, 2007.
[52] Wolff L B. Polarization vision: a new sensory approach to image understanding[J]. Image and Vision Computing. 1997, 15(2): 81-93.
[53] Smith M H, Howe J D, Woodruff J B, et al. Multispectral infrared Stokes imaging polarimeter[C]. 1999.
[54] Egan W G. Optical remote sensing science and technology[M]. New York: M. Dekker, 2004.
[55] Gupta N. Remote sensing using hyperspectral and polarization images[C]. 2002.
[56]金锡峰,乔德林,周素香.地物二向性反射比测量装置[P].中国专利,96239489. 1998.
[57]赵云升,金锡锋等.植物单叶偏振反射特征研究[J].遥感学报. 2000, 4(002): 131-135.
[58]乔延利,杨世植.对地遥感中的光谱偏振探测方法研究[J].高技术通讯. 2001, 11(007):36-39.
[59]偏振遥感图像特性表征及分析[J].量子电子学报. 2002, 19(004): 373-378.
[60] Hongying Z, Hu Z, Lei Y, et al. Model of reflection spectrum of rock surface in 2πspace [J]. ACTA Geologica Sinica. 2004, 78(3): 843-847.
[61]赵虎.岩石的多角度反射光谱与偏振反射光谱特征研究[D].北京大学, 2004.
[62]赵云升,吴太夏,胡新礼, et al.多角度偏振反射与二向性反射定量关系初探[J].红外与毫米波学报. 2005, 24(006): 441-444.
[63]罗杨洁,赵云升,胡新礼, et al.偏振与多角度遥感中的太阳耀光剥离[J].光学技术. 2006, 32(002): 205-208.
[64]刘志刚,周冠华.太阳耀光的偏振分析[J].红外与毫米波学报. 2007, 26(005): 362-365.
[65] Taixia Wu Y Z. The bidirectional polarized reflectance model of soil[J]. Geoscience and Remote Sensing, IEEE Transactions on. 2005, 43(12): 2854-2859.
[66]韩阳,赵云升,张莉莉, et al.丁香叶片叶绿素含量偏振高光谱数学模型反演研究[J].光谱学与光谱分析. 2009, 29(6): 1595-1598.
[67]陈超,赵永强,程咏梅, et al.背景偏振光谱二向反射分布函数建模分析[J].光电子.激光. 2009(003).
[68]吴太夏,晏磊,相云, et al.水体的多角度偏振波谱特性及其在水色遥感中应用[J].光谱学与光谱分析. 2010, 30(2): 448-452.
[69]吴太夏,晏磊,相云, et al.垂直观测时水平粗糙地表偏振反射作用研究[J].红外与毫米波学报. 2009, 28(002): 151-155.
[70]韩阳,李潜,赵云升,等,三因素及其交互作用对植物叶片多角度偏振高光谱特征的影响[J].红外与毫米波学报. 2010, 29(4): 316-320.
[71] Irons J,R Ranson J T,Willams D L,et al. An offnadir pointing imaging spectrometer for terrestrial ecosystem study [J]. IEEE Trans Geosci Remote Sensing,1991,29:66-74.
[72] Deschamps P M,Breon F M,Leroy M,et al. The POLDER mission:instrument characteristics and science objectives [J]. IEEE Trans Geosci Remote Sensing,1994,32(3):598-615.
[73] Lin,Yi-cheng. A fractal-based coherent scattering and propagation model for forest canopies[D]:PHD. USA:UNIVERSITY OF MICHIGAN,1997.
[74] Walker,Joe Alan,Jr. Models and validation measurements of bi-directional reflectance factor for diffuse reflecting materials[D]:PHD.USA :THE UNIVERSITY OF ARIZONA,1998.
[75] Tynes Huey Hatcher. Monte Carlo solutions of the radiative transfer equation for scattering systems[D]:PHD.USA :TEXAS A&M UNIVERSITY,2001.
[76] Lucht W.,Schaaf C. B.. An algorithm for the retrieval of Aledo from space using semi empirical BRDF models[J]. IEEE Transactions on Geosciences and Remote Sensing,2004,38:977–998.
[77] Geol N.S.. Models of vegetation Canopy Reflectance and Their use in Estimation of Biophysical Parameters from Refectance Data [J]. Remote Sense Reviews,1988,4:1-21.
[78] Li Xiaowen , Strahler A.H. . Geometric optical modeling of a coniferous forest canopy [J]. IEEE Trans. Geosci. Remote Sensing,1985,23:207-221.
[79] Li X. and Strahler A H. Geometric-optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy [J].IEEE Trans geosci Remote Sensing,1986,GE-24:906-919.
[80] Wanner W, Li Xiaowen, Strahler A. On the derivation of kernels and kemel-driven models of bi-derectional reflectance [J]. J Geophys Res., 1995,100(D10): 21,077-089.
[81] Strahler A H, Muller J P. MODIS BRDF/Albedo Product Algorithm Theoretical Basis Document Version 5.0[M], MODIS Science Team, 1999(4):1-53.
[82]车大为,陈圣波,吕乐婷,等。多角度遥感中BRDF模型研究的现状与展望,吉林大学学报(地球科学版),2008年增刊,38:229-231.
[83]陈述彭,童庆禧,郭华东等编著,高光谱分辨率遥感信息机理与地物识别[J],北京:科学出版社,1998,139-231。
[84]郑兰芬,童庆禧,王晋年。高光谱分辨率遥感研究进展,遥感科学进展,科学出版社,1995。
[85]浦瑞良,宫鹏。高光谱遥感及其应用[M],北京:高等教育出版社,2000。
[86] Muller E, DE Camps H. Modeling soil moisture-reflectance [J]. Remote Sens. Environ., 2000,76:173-180.
[87] Franacois C. The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status [J]. Remote Sensing of Environment, 2002,80(1): 122-133.
[88] Djepa V, Meneti M, Vaughan R. Relating satellite multiangular thermal infrared ovservations to soil and foliage temperature [J]. Advances in Space Research, 2002,30(11): 2529-2533.
[89] Quesney A, Le He, et al., Estimation of watershed soil moisture index from ERS/SAR data [J]. Remote Sens. Environ., 2000,72:290-303.
[90] Lobell D B, Asner G P. Moisture effects on soil reflectance [J]. Soil Sci. Soc. Am. J., 2002,66:722-727.
[91] Pinty B, Verstraete M, Gobron N. The effect of soil anisotropy on the radiance field emerging from vegetation canopies [J]. Geophys. Res. Lett., 1998, 25: 797-800.
[92] Daniel K W, Tripathi N K, etal., Analysis of VNIR(400-1000nm) spectral signatures for estimation of soil organic matter in tropical soils of Thailand [J]. Int. J. Remote Sensing, 2004,25(3): 643-652.
[93] Torrent J, Schwertmann U, Fetcher H, Alferez F. Quantitative relationships between soil color and hematite content. Soil Sci., 1983, 13: 354-358.
[94] Palacios-Orueta A. and S. L Ustin. Remote sensing of soil properties in the Santa Monica Mountains I. Spectral Analysis. Remote Sens. Environ., 1998,65:170-183.
[95] Zaady E, Karnieli A, Shachak M. Applying a field spectroscopy technique for assessing successional trends of biological soil crusts in a semi-arid environment. Journal of Arid Environments, 2007, 70(3):463-477.
[96] Zimmermann M, Leifeld J, Fuhrer J. Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Boilogy and Biochenistry, 2007, 39(1): 224-231.
[97] Siegal B S. Effect of vegetation on rock and soil type discrimination. Photogrammetric Engineering and Remote Sensing, 197, 43: 191-196.
[98] Dunn B W, Beecher H G, Batten G D, Ciavarella S. The potential of near-infrared reflectance spectroscopy for soil analysis- a case study from the Riverine Plain of South-eastern Australia [J]. Aust. J. Exp. Agric., 2002, 42: 607-614.
[99] Chang C W, Laird D A, Mausbach M J, Hurburgh Jr C R. Near-infrared reflectance spectroscopy-principal components regression analysis of soil properties. Soil Sci. Soc. Am. J., 2001, 65:480-490.
[100] Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N [J]. Soil Sci., 2002, 167(2): 110-116.
[101] Hillel D. Environmental soil physics [M]. Academic Press, San Diego, CA, USA, 1998.
[102] Galvao L S, Vitorello I. Variability of Laboratory Measured Soil Lines of Soil from Southeastern Brazil [J]. Remote Sensing of the Environment, 1998, 63: 166-181.
[103] Grzegorz S, Gregory W M, Tomasz I S, et al. Near-and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content [J]. Journal of Environment Quality, 2004, 33: 2056-2069.
[104] Hovis W A, Infrared spectral reflectance of some common minerals [J]. Applied Optics, 1966, 5: 245-248.
[105] Huete A R. A Soil Adjusted Vegetation Index (SAVI) [J]. Remote Sensing of the Environment, 1988,25:295-309.
[106] Lee K S, Lee G B, Tyler E J. Determination of soil characteristics from thematic mapper data of a cropped organic-inorgani soil landscape [J]. Soil Science Socity of America Journal, 1988,52:1100-1104.
[107] Stoner E R, Baumgardner M F. Characteristic Variations in Reflectance of Surface Soils [J]. Soil Sci. Soc. Am. J., 1981, 45: 1161-1165.
[108] Kumar L, Rietkerk M, Fvan Langevelde, et al. Relationship between vegetation growth rates at the onset of the wet season and soil type in the Sahel of Burkina Faso: implications for resource utilization at large scales [J]. Ecological Modelling, 2002, 149(1-2): 143-152.
[109]何挺,王静,林宗坚等.土壤有机质光谱特征研究,武汉大学学报(信息科学版),2006,11:975-979
[110] Ishida T, Ando H, and Fukkuhara M. Estimation of complex refractive index of soil particles and its dependence on soil chemical properties, Remote Sensing Environ., 1991,38:173-182.
[111]程街亮,土壤高光谱遥感信息提取与二向反射模型研究[D],浙江大学,2008
[112] Baumgardner, M F, Silva, I F, et al.,Reflectance properties of soil. Advances in Agronomy, 1985, 38: 1-44.
[113] Bowers S A, Hanks R J. Reflection of radiant energy from soils [J]. Soil Science, 1965,2: 130-138.
[114] Hoffer R M, Johannsen J. Ecological potentials in spectral signature analysis. In: P. L. Johnson (Ed.), Remote sensing in ecology. Athens University of Georgia Press Athens (Georgia, USA)(Chapter 1). 1969.
[115] Stoner E R, Baumgardner M F. Characteristic Variations in Reflectance of Surface Soils [J]. Soil Sci. Soc. Am. J., 1981, 45: 1161-1165.
[116] Liu W D, F. Baret, Gu X F, Tong Q X, Zheng L F, Zhang B. Relating soil surface moisture to reflectance. Remote Sensing of Environment, 2002, 81: 238-246.
[117] Bedidi A, Cervelle B, Madeira J, Pouget M. Moisture effects on spectral characteristics (visible) of lateritic soils. Soil Science, 1992, 153: 129-141.
[118] Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near-infrared reflectance sepctrophotometry. Soil Sci. Soc. Am. J., 1986,50: 120-123.
[119] Muller E, Decamps H. Modeling Soil moisture-reflectance. Remote Sensing of Environment, 2000, 76: 173-180.
[120] Whiting M L, Li L, Ustin S L. Predicting water content using Gaussian model on soil spectra. Remote Sensing of Environment, 2004, 89: 535-552.
[121]戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究.遥感文选[C],科学出版社. 1981,315-323
[122]童庆禧,中国典型地物波谱及其特征分析,北京:科学出版社,1990
[123]徐彬彬,季耿善,朱永豪.中国陆地背景和土壤光谱反射特性的地理分区的初步研究[J].环境遥感,1991,6(2):142-151.
[124]朱永豪,邓仁达,等,不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义。土壤学报,1984,21(2):194-202
[125]季耿善,徐彬彬。土壤粘土矿物特征及其在土壤学上的应用,土壤学报,1987,24(1):67-76
[126]徐彬彬.我国土壤光谱线之研究[J],环境遥感,1991,6(1):61-71.
[127]张文群,蒋光润。定西遥感试验场土壤光谱特征分析。遥感技术与应用,1992,7(2):25-31
[128] Chen F, D. E. Kissell, L. T. West, and W. Adkins. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Sci. Soc. Am. J., 2000,64: 746-753.
[129] Cierniewski J. A model for soil surface roughness influence on the spectral response of bare soils in the visible and near infrared range, Remote Sensing of Environment, 1987, 23: 98-115.
[130] Cierniewski J. The influence of the viewing geometry of bare soil surface on their spectral response in the visible and near infrared, Remote Sensing of Environment, 1989, 27: 135-142.
[131]徐希孺.遥感物理[M].北京:北京大学出版社, 2005,533.
[132] Escadafal R. Caracterisation de la surface des slos arides par obvervations de terrain et par teledetection [D]. Paris University, Paris, France. 1989.
[133] Hapke B. Bidirectional reflectance spectroscopy. 1. Theory. J. Geophys. Res., 1981, 86: 3039-3054.
[134] Hapke B, Wells E. Bidirectional reflectance spectroscopy. 2. Experiments and ovservations. J. Geophys. Res., 1981, 86: 3055-3060.
[135] Hapke B. Bidirectional reflectance spectroscopy. 3. Correction for macroscopic roughness. Icarus, 1984, 59: 41-59.
[136] Hapke B. Bidirectional reflectance spectroscopy. 4. The extinction coefficient and the opposition effect. Icarus, 1986, 67: 264-280.
[137] Jacquemoud S, Baret E, Hanocq J F. Modeling spectral and bidirectional soil reflectance. Remote Sensing of the Envionment, 1992, 41: 123-132.
[138] Liang S, Townshend J R G. A Modified Hapke Model for Soil Bidirectional Reflectance. Remote Sensing of the Envionment, 1996, 55: 1-10.
[139] Cierniewski J, Gdala T, Karnieli A. A hemispherical-directional reflectance model as a tool for understanding image distinctions between cultivated and uncultivated bare surfaces. Remote Sensing of the Envionment, 2004, 90(4): 505-523.
[140] Cappell A, Zobeck T M, Brunner G. Using bi-directional soil spectral reflectance to model soil surface changes induced by rainfall and wind-tunnel abrasion. Remote Sensing of the Envionment, 2006, 102(3-4): 328-343.
[141] Wassenaar T, Andrieux P, Baret F and Robbez-Masson J M. Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area. CATENA, 2005, 62(2-3): 94-110.
[142] Upadhyay T P, Solberg B, Sankhayan P L. Use of models to analyse land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: A review and analysis. Forest Policy and Economics, 2006, 9(4):349-371.
[143] Chappell A, Strong C, McTainsh G, Leys J. Detecting induced in situ erodibility of a dust-producing playa in Australia using a bi-directional soil spectral reflectance model. RemoteSensing of the Envionment, 2007, 106: 508-524.
[144]赵云升,韩阳.长白山典型地区偏振反射数据库.长白山地理系统研究(第三辑),长春:东北师范大学出版社,2010.
[145] Palacios-Orueta A, Ustin S L. Remote sensing of soil properties in the Santa Monica Mountains I. Spectral Analysis [J]. Remote Sens. Environ., 1998,65:170-183.
[146]韩阳,赵云升,等.森林土壤多角度高光谱偏振反射影响研究初探[J],光谱学与光谱分析,2009,29(3):702-706.
[147]宋开山,赵云升,张柏.土壤偏振反射特性的多角度测量与研究[J],土壤通报,2004,35(4):420-425.
[148]万余庆,谭克龙,周日平,等.高光谱遥感应用研究[M].北京:科学出版社,2006.
[149]张柏,禹秉熙,苏阳.遥感信息系统研究[C] .北京:科学出版社, 2000. 31-34.
[150]赵云升,金伦,张洪波,等.土壤的偏振反射特性研究[J].东北师大学报, 2000, 32(4):93-97.
[151]吕国楷,洪启旺,郝允充,等.遥感概论[M].北京:高等教育出版社, 1995. 21-31.
[152]李民赞.光谱分析技术及其应用[M],北京:科学出版社,2006年8月.
[153] Neema,D I, Shah A, Patel A N. A Statistical Optical Model for Light Reflection and Penetration Through Sand[J]. Intermational Journal of Remote Sensing, 1987, 8(8):1209-1217.
[154] Azzam R M A, Bashara N M. Ellipsometry and polarized light [M]. Amsterdam: North-Holland Publishing Company, 1997: 1-98
[155]张莉莉.利用偏振高光谱反演植被叶绿素含量[D]:[硕士学位论文].东北师范大学,2007.
[156]卜兆宏,唐万龙,刘复新,等,水土流失定量遥感方法新进展及其在太湖流域的应用,土壤学报,2003,40(1):1-9.
[157] Bréon F, Tanre D, Lecomte P, et al. Polarized reflectance of bare soils and vegetation–measurements and models, IEEE Transactions on Geoscience and Remote Sensing, 1995, 3(2): 487-499.
[158] Bicheron, Patrice; Leroy, Marc. A Method of Biophysical Parameter Retrieval at Global scale by Inversion of a Vegetation Reflectance Model [J], Remote Sensing of Environment, 1999, Vol.67, Issue:3: 251-266
[159]杜嘉,赵云升,宋开山,等。偏振遥感测量中土壤偏振度随太阳高度角的变化规律初探[J],地理科学,2007, 27(5):707-710.
[160]张成军.实验设计与数据处理[M],北京:化学工业出版社,2009年10月.
[161]刘文卿.实验设计[M],北京:清华大学出版社,2005年2月
[162] Coulson K L, Whitehead V S, Campbell C. Polarized views of the earth from orbital altitude PPIE 637 [J]. Ocean Opt, 1986, 8: 35-41.
[163]方开泰,马长兴.均匀实验与正交实验[M].北京:科学出版社, 2001. 35-77.
[164]姚春生,使用MODIS数据反演土壤水分研究[D],中科院遥感应用研究所,2003年6月.
[165] Wan Zhengming. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products [J], Remote Sensing of Environment, 2008, 112: 59-74.
[166]赵英时,等.遥感应用分析原理与方法[J].北京:科学出版社,2003.
[167] Wan Z, Wang P, Li X. Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA [J]. Int. J. Remote Sensing, 2004, 25(1):61-72.
[168] Goward S N, Cruickhanks G D, and Hope A S. Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape [J]. Remote Sens. Environ., 1985, 18(2): 137–146.
[169] Nemani R R and Running S W. Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data [J]. J. Appl. Meteorol., 1989, 28(4): 276–284.
[170] Nemani R R, Pierce L, Running S W, and Goward S N. Developing satellite-derived estimates of surface moisture status [J]. J. Appl. Meteorol., 1993, 32(3): 548–557.
[171] Moran M S, Clarke T R, Inoue Y, and Vidal A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index [J]. Remote Sens. Environ., 1994, 49(3): 246–263.
[172] Sandholt I, Rasmussena K, and Andersenb J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status [J]. Remote Sens. Environ., 2002, 79(2): 213–224.
[173] Nishida K, Nemani R R,. Glassy J M and Running S W. Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status [J]. IEEE Trans. Geosci. RemoteSens., 2003, 41(2): 453–501.
[174] Carlson T. An overview of the‘Triangle method’for estimating surface evapotranspiration and soil moisture from satellite imagery [J]. Sensors, 2007, 7(8): 1612–1629.
[175] Goward S N, Cruickhanks G D and Hope A S. Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape [J]. Remote Sens. Environ., 1985, 18(2): 137–146.
[176] Gillies R R, Kustas W P and Humes K S. A verification of the“triangle”method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature [J]. Int. J. Remote Sens., 1997, 18(15): 3145–3166.
[177] Han Y, Wang Y and Zhao Y.. Estimating soil moisture conditions of the Greater Changbai Mountains by Land Surface Temperature and NDVI [J]. IEEE Trans. Geosci. Remote Sens., 2010, 48(6): 2509-2515.
[178]周清,周斌,张杨珠,等。水稻土SOM含量高光谱模型的母质差异性研究[J].科学通报,2004, 20(6):471-475.
[2]刘焕军.松嫩平原主要土壤高光谱定量遥感研究[D]:[博士学位论文].中国科学院东北地理与农业生态研究所,2008.
[3]尹改,王桥,郑炳辉,等.国家环保总局对中国资源卫星的需求与分析[J].卫星应用,1999,7(1):11-20.
[4] Martin P D, Malley D F, Manning G, Fuller L. Determination of soil organic carbon and nitrogen at the field level using nearing-infrared spectroscopy [J]. Can. J. Soil Sci, 2002, 82: 413-422.
[5] He Y, Song H Y, Pereira A G, et al., A New Approach to Predict N, P, K and OM Content in a Loamy Mixed Soil by Using Near Infrared Reflectance Spectroscopy [J]. Lecture Notes in Computer Science, 2005, 3644: 859-867.
[6] Fox G A, Sabbagh G J. Estimation of Soil Organic Matter from Red and Near-infrared Remotely Sensed Data Using a Soil Line Euclidean distance Technique [J]. Soil Sci. Soc. Am. J., 2002, 66: 1922-1929.
[7]张红梅,沙晋明.遥感监测土壤湿度方法综述[J].中国农业通报,2005, 21(2): 307-311.
[8]邓辉,周清波.土壤水分遥感监测方法进展[J].中国农业资源与区划,2004, 25(3):46-49.
[9] Odlare M, Svensson K, Pell M. Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field [J]. Geoderma, 2005, 126:193-202.
[10]尹殿奎,冯君,杨志超.长春市主要土壤类型光谱特性的研究[J].吉林农业大学学报,1998,20(3):57-59.
[11]梅安新,彭望琭,秦其明等,遥感导论[M],2002,北京:高等教育出版社.
[12]李小文,王锦地.植被光学遥感模型和植被结构参数化[M]。1995,北京:科学出版社.
[13] Chen J M, Leblanc S G. A 4-scale bidirectional reflectance model based on canopy architecture [J]. IEEE Transctions on Geoscience and Remote Sensing, 1997, 35:1316-1337.
[14] Liang S. Quantitative Remote Sensing of Land Surfaces. New York: Willey. 2004.
[15]宫鹏,遥感科学与技术中的一些前沿问题,遥感学报,2009,13(1):13-23.
[16] Bachman C G. Laser Radar Systems and Techniques. Norwood(MA): Artech House, 1979.
[17] Nelson R F, Krabill W B, Maclean G A. Determining forest canopy characteristics using airborne laser data[J]. Remote Sensing of Environment, , 1984, 15: 201-212
[18]李树楷,薛永祺.高效三维遥感集成技术系统[M]. 2000,北京:科学出版社.
[19] Szewczyk R, Osterweil E, Polastre J, et al., Habitat monitoring by sensor network [J]. Communications of the ACM, 2004, 47(6):34.
[20]宫鹏.环境监测中无线传感器网络地面遥感新技术[J].遥感学报,2007,11(4):545-551.
[21]宫鹏,黎夏,徐冰.高分辨率遥感影像解译理论和应用方法中的一些研究问题[J],遥感学报,2006,10(1):1-4.
[22] Hui F M, Xu B, Huang H B, et al., Modeling spatial-temporal change of Poyang Lake using multi-temporal Landsat imagery [J]. International Journal of Remote Sensing, 2008, 29(20): 5767-5784;
[23] Weng Y L, Gong P, Zhu Z L, Reflectance spectroscopy for the assessment of salt content in soil of the Yellow River Delta of China [J]. International Journal of Remote Sensing, 2008, 29(19): 5511-5531.
[24] Zhou S Q, Liang X, Chen J X, An assessment of VIC-3L hydrological model for the Yangtze River Basin based on remote sensing- A case study of Baohe watershed [J]. Canadian Journal Remote Sensing, 2004, 32(5): 840-853.
[25] Crosson W L, Laymom C A, Inguva R, et al., Assimilating remote sensing data in a surface flux-soil moisure model [J]. Hydrological Processes, 2002, 16:1645-1662.
[26] Lu L X, Shuttleworth W J, Incorporating NDVI-derived LAI into the climate version of RAMS and its impact on regional climate [J]. Journal of Hydrometeorology, 2002, 3:347-361.
[27] Olioso A, Inoue Y, Orega-Farias S, et al., Future directions for advanced evaptranspiration modeling: assimilation of remote sensing data into crop simulation models and SVAT models [J]. Irrigations and Drainage Systems. 2005, 19:377-412.
[28]宫鹏,徐冰,梁松.用遥感与地理信息系统研究传染病时空分布[J],中国科学C辑:生命科学,2006,36(2):184-192.
[29] Russell E E, Watts L A, Pellicori S F, et al. Orbiter cloud photopolarimeter for the Pioneer Venus mission[C]. 1977.
[30] Banse K, English D C. Geographical differences in seasonality of CZCS-derived phytoplankton pigment in the Arabian Sea for 1978-1986[J]. Deep Sea Research Part II: Topical Studies in Oceanography. 2000, 47(7-8): 1623-1677.
[31] Egan W G, Johnson W R, Whitehead V S. Terrestrial polarization imagery obtained from the Space Shuttle: characterization and interpretation [J]. Applied Optics. 1991, 30(4): 435-442.
[32] Roger J C, Santer R, Herman M, et al. Polarization of the solar light scattered by the Earth-atmosphere system as observed from the US shuttle [J]. Remote Sensing of Environment. 1994, 48(3): 275-290.
[33] Travis L D. Satellite retrieval of aerosol properties over the ocean using polarization as well as intensity of reflected sunlight [J]. Journal of Geophysical Research. 1997, 102(D14): 16, 917-989, 13.
[34] Deschamps P Y, Bréon F M, Leroy M, et al. The POLDER mission: instrument characteristics and scientificobjectives [J]. IEEE Transactions on geoscience and remote sensing. 1994, 32(3): 598-615.
[35] Jensen G L, Peterson J Q. Hyperspectral imaging polarimeter in the infrared[C]. 1998.
[36] Peterson J Q, Jensen G L, Greenman M E, et al. Calibration of the hyperspectral imaging polarimeter[C]. 1999.
[37] Fougnie B, Bracco G, Lafrance B, et al. PARASOL in-flight calibration and performance[J]. Applied optics. 2007, 46(22): 5435-5451.
[38] Winker D M, Pelon J, Mccormick M P. The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds[C]. 2003.
[39] Mishchenko M, Penner J, Anderson D. Global aerosol climatology project[J]. J Atmos Sci. 2002, 59: 249-783.
[40]罗睿智,乔延利,曹汉军.航空型多波段偏振遥感探测及其光学系统的研究与设计[J].量子电子学报, 2002,19(2):143-148
[41]杨伟峰,潘玲,洪津;多波段偏振CCD相机的辐射定标研究;高技术通讯2004,10
[42]罗睿智,乔延利,郝沛明等.偏振CCD相机光学系统的偏振特性分析.量子电子学报,2004,21(3):396-400.
[43]赵慧洁,屈玉福,殷雪冰,等.高光谱全偏振成像遥感系统[P].专利号:200510086926中国,2007年.
[44] Grant L. Diffuse and specular characteristics of leaf reflectance[J]. Remote Sensing of Environment. 1987, 22(2): 309-322.
[45] Woessner P, Hapke B. Polarization of light scattered by clover[J]. Remote Sensing of Environment. 1987, 21(3): 243-261.
[46] Brakke T W, Smith J A, Harnden J M. Bidirectional scattering of light from tree leaves[J]. Remote Sensing of Environment. 1989, 29(2): 175-183.
[47] Painter T H, Paden B, Dozier J. Automated spectro-goniometer: A spherical robot for the field measurement of the directional reflectance of snow[J]. Review of Scientific Instruments. 2003, 74: 5179.
[48] Ben-Dor B, Oppenheim U P, Balfour L S. Polarization properties of targets and backgrounds in the infrared [1971-07][C]. SPIE INTERNATIONAL SOCIETY FOR OPTICAL, 1992.
[49] Thulstrup E W, Michl J. Elementary polarization spectroscopy[M]. Wiley-VCH, 1989.
[50] Tyo J S, Goldstein D L, Chenault D B, et al. Review of passive imaging polarimetry for remote sensing applications [J]. Applied optics. 2006, 45(22): 5453-5469.
[51]曾延安.偏振成像系统关键技术研究[D].华中科技大学, 2007.
[52] Wolff L B. Polarization vision: a new sensory approach to image understanding[J]. Image and Vision Computing. 1997, 15(2): 81-93.
[53] Smith M H, Howe J D, Woodruff J B, et al. Multispectral infrared Stokes imaging polarimeter[C]. 1999.
[54] Egan W G. Optical remote sensing science and technology[M]. New York: M. Dekker, 2004.
[55] Gupta N. Remote sensing using hyperspectral and polarization images[C]. 2002.
[56]金锡峰,乔德林,周素香.地物二向性反射比测量装置[P].中国专利,96239489. 1998.
[57]赵云升,金锡锋等.植物单叶偏振反射特征研究[J].遥感学报. 2000, 4(002): 131-135.
[58]乔延利,杨世植.对地遥感中的光谱偏振探测方法研究[J].高技术通讯. 2001, 11(007):36-39.
[59]偏振遥感图像特性表征及分析[J].量子电子学报. 2002, 19(004): 373-378.
[60] Hongying Z, Hu Z, Lei Y, et al. Model of reflection spectrum of rock surface in 2πspace [J]. ACTA Geologica Sinica. 2004, 78(3): 843-847.
[61]赵虎.岩石的多角度反射光谱与偏振反射光谱特征研究[D].北京大学, 2004.
[62]赵云升,吴太夏,胡新礼, et al.多角度偏振反射与二向性反射定量关系初探[J].红外与毫米波学报. 2005, 24(006): 441-444.
[63]罗杨洁,赵云升,胡新礼, et al.偏振与多角度遥感中的太阳耀光剥离[J].光学技术. 2006, 32(002): 205-208.
[64]刘志刚,周冠华.太阳耀光的偏振分析[J].红外与毫米波学报. 2007, 26(005): 362-365.
[65] Taixia Wu Y Z. The bidirectional polarized reflectance model of soil[J]. Geoscience and Remote Sensing, IEEE Transactions on. 2005, 43(12): 2854-2859.
[66]韩阳,赵云升,张莉莉, et al.丁香叶片叶绿素含量偏振高光谱数学模型反演研究[J].光谱学与光谱分析. 2009, 29(6): 1595-1598.
[67]陈超,赵永强,程咏梅, et al.背景偏振光谱二向反射分布函数建模分析[J].光电子.激光. 2009(003).
[68]吴太夏,晏磊,相云, et al.水体的多角度偏振波谱特性及其在水色遥感中应用[J].光谱学与光谱分析. 2010, 30(2): 448-452.
[69]吴太夏,晏磊,相云, et al.垂直观测时水平粗糙地表偏振反射作用研究[J].红外与毫米波学报. 2009, 28(002): 151-155.
[70]韩阳,李潜,赵云升,等,三因素及其交互作用对植物叶片多角度偏振高光谱特征的影响[J].红外与毫米波学报. 2010, 29(4): 316-320.
[71] Irons J,R Ranson J T,Willams D L,et al. An offnadir pointing imaging spectrometer for terrestrial ecosystem study [J]. IEEE Trans Geosci Remote Sensing,1991,29:66-74.
[72] Deschamps P M,Breon F M,Leroy M,et al. The POLDER mission:instrument characteristics and science objectives [J]. IEEE Trans Geosci Remote Sensing,1994,32(3):598-615.
[73] Lin,Yi-cheng. A fractal-based coherent scattering and propagation model for forest canopies[D]:PHD. USA:UNIVERSITY OF MICHIGAN,1997.
[74] Walker,Joe Alan,Jr. Models and validation measurements of bi-directional reflectance factor for diffuse reflecting materials[D]:PHD.USA :THE UNIVERSITY OF ARIZONA,1998.
[75] Tynes Huey Hatcher. Monte Carlo solutions of the radiative transfer equation for scattering systems[D]:PHD.USA :TEXAS A&M UNIVERSITY,2001.
[76] Lucht W.,Schaaf C. B.. An algorithm for the retrieval of Aledo from space using semi empirical BRDF models[J]. IEEE Transactions on Geosciences and Remote Sensing,2004,38:977–998.
[77] Geol N.S.. Models of vegetation Canopy Reflectance and Their use in Estimation of Biophysical Parameters from Refectance Data [J]. Remote Sense Reviews,1988,4:1-21.
[78] Li Xiaowen , Strahler A.H. . Geometric optical modeling of a coniferous forest canopy [J]. IEEE Trans. Geosci. Remote Sensing,1985,23:207-221.
[79] Li X. and Strahler A H. Geometric-optical Bidirectional Reflectance Modeling of a Conifer Forest Canopy [J].IEEE Trans geosci Remote Sensing,1986,GE-24:906-919.
[80] Wanner W, Li Xiaowen, Strahler A. On the derivation of kernels and kemel-driven models of bi-derectional reflectance [J]. J Geophys Res., 1995,100(D10): 21,077-089.
[81] Strahler A H, Muller J P. MODIS BRDF/Albedo Product Algorithm Theoretical Basis Document Version 5.0[M], MODIS Science Team, 1999(4):1-53.
[82]车大为,陈圣波,吕乐婷,等。多角度遥感中BRDF模型研究的现状与展望,吉林大学学报(地球科学版),2008年增刊,38:229-231.
[83]陈述彭,童庆禧,郭华东等编著,高光谱分辨率遥感信息机理与地物识别[J],北京:科学出版社,1998,139-231。
[84]郑兰芬,童庆禧,王晋年。高光谱分辨率遥感研究进展,遥感科学进展,科学出版社,1995。
[85]浦瑞良,宫鹏。高光谱遥感及其应用[M],北京:高等教育出版社,2000。
[86] Muller E, DE Camps H. Modeling soil moisture-reflectance [J]. Remote Sens. Environ., 2000,76:173-180.
[87] Franacois C. The potential of directional radiometric temperatures for monitoring soil and leaf temperature and soil moisture status [J]. Remote Sensing of Environment, 2002,80(1): 122-133.
[88] Djepa V, Meneti M, Vaughan R. Relating satellite multiangular thermal infrared ovservations to soil and foliage temperature [J]. Advances in Space Research, 2002,30(11): 2529-2533.
[89] Quesney A, Le He, et al., Estimation of watershed soil moisture index from ERS/SAR data [J]. Remote Sens. Environ., 2000,72:290-303.
[90] Lobell D B, Asner G P. Moisture effects on soil reflectance [J]. Soil Sci. Soc. Am. J., 2002,66:722-727.
[91] Pinty B, Verstraete M, Gobron N. The effect of soil anisotropy on the radiance field emerging from vegetation canopies [J]. Geophys. Res. Lett., 1998, 25: 797-800.
[92] Daniel K W, Tripathi N K, etal., Analysis of VNIR(400-1000nm) spectral signatures for estimation of soil organic matter in tropical soils of Thailand [J]. Int. J. Remote Sensing, 2004,25(3): 643-652.
[93] Torrent J, Schwertmann U, Fetcher H, Alferez F. Quantitative relationships between soil color and hematite content. Soil Sci., 1983, 13: 354-358.
[94] Palacios-Orueta A. and S. L Ustin. Remote sensing of soil properties in the Santa Monica Mountains I. Spectral Analysis. Remote Sens. Environ., 1998,65:170-183.
[95] Zaady E, Karnieli A, Shachak M. Applying a field spectroscopy technique for assessing successional trends of biological soil crusts in a semi-arid environment. Journal of Arid Environments, 2007, 70(3):463-477.
[96] Zimmermann M, Leifeld J, Fuhrer J. Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Boilogy and Biochenistry, 2007, 39(1): 224-231.
[97] Siegal B S. Effect of vegetation on rock and soil type discrimination. Photogrammetric Engineering and Remote Sensing, 197, 43: 191-196.
[98] Dunn B W, Beecher H G, Batten G D, Ciavarella S. The potential of near-infrared reflectance spectroscopy for soil analysis- a case study from the Riverine Plain of South-eastern Australia [J]. Aust. J. Exp. Agric., 2002, 42: 607-614.
[99] Chang C W, Laird D A, Mausbach M J, Hurburgh Jr C R. Near-infrared reflectance spectroscopy-principal components regression analysis of soil properties. Soil Sci. Soc. Am. J., 2001, 65:480-490.
[100] Chang C W, Laird D A. Near-infrared reflectance spectroscopic analysis of soil C and N [J]. Soil Sci., 2002, 167(2): 110-116.
[101] Hillel D. Environmental soil physics [M]. Academic Press, San Diego, CA, USA, 1998.
[102] Galvao L S, Vitorello I. Variability of Laboratory Measured Soil Lines of Soil from Southeastern Brazil [J]. Remote Sensing of the Environment, 1998, 63: 166-181.
[103] Grzegorz S, Gregory W M, Tomasz I S, et al. Near-and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content [J]. Journal of Environment Quality, 2004, 33: 2056-2069.
[104] Hovis W A, Infrared spectral reflectance of some common minerals [J]. Applied Optics, 1966, 5: 245-248.
[105] Huete A R. A Soil Adjusted Vegetation Index (SAVI) [J]. Remote Sensing of the Environment, 1988,25:295-309.
[106] Lee K S, Lee G B, Tyler E J. Determination of soil characteristics from thematic mapper data of a cropped organic-inorgani soil landscape [J]. Soil Science Socity of America Journal, 1988,52:1100-1104.
[107] Stoner E R, Baumgardner M F. Characteristic Variations in Reflectance of Surface Soils [J]. Soil Sci. Soc. Am. J., 1981, 45: 1161-1165.
[108] Kumar L, Rietkerk M, Fvan Langevelde, et al. Relationship between vegetation growth rates at the onset of the wet season and soil type in the Sahel of Burkina Faso: implications for resource utilization at large scales [J]. Ecological Modelling, 2002, 149(1-2): 143-152.
[109]何挺,王静,林宗坚等.土壤有机质光谱特征研究,武汉大学学报(信息科学版),2006,11:975-979
[110] Ishida T, Ando H, and Fukkuhara M. Estimation of complex refractive index of soil particles and its dependence on soil chemical properties, Remote Sensing Environ., 1991,38:173-182.
[111]程街亮,土壤高光谱遥感信息提取与二向反射模型研究[D],浙江大学,2008
[112] Baumgardner, M F, Silva, I F, et al.,Reflectance properties of soil. Advances in Agronomy, 1985, 38: 1-44.
[113] Bowers S A, Hanks R J. Reflection of radiant energy from soils [J]. Soil Science, 1965,2: 130-138.
[114] Hoffer R M, Johannsen J. Ecological potentials in spectral signature analysis. In: P. L. Johnson (Ed.), Remote sensing in ecology. Athens University of Georgia Press Athens (Georgia, USA)(Chapter 1). 1969.
[115] Stoner E R, Baumgardner M F. Characteristic Variations in Reflectance of Surface Soils [J]. Soil Sci. Soc. Am. J., 1981, 45: 1161-1165.
[116] Liu W D, F. Baret, Gu X F, Tong Q X, Zheng L F, Zhang B. Relating soil surface moisture to reflectance. Remote Sensing of Environment, 2002, 81: 238-246.
[117] Bedidi A, Cervelle B, Madeira J, Pouget M. Moisture effects on spectral characteristics (visible) of lateritic soils. Soil Science, 1992, 153: 129-141.
[118] Dalal R C, Henry R J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near-infrared reflectance sepctrophotometry. Soil Sci. Soc. Am. J., 1986,50: 120-123.
[119] Muller E, Decamps H. Modeling Soil moisture-reflectance. Remote Sensing of Environment, 2000, 76: 173-180.
[120] Whiting M L, Li L, Ustin S L. Predicting water content using Gaussian model on soil spectra. Remote Sensing of Environment, 2004, 89: 535-552.
[121]戴昌达.中国主要土壤光谱反射特性分类与数据处理的初步研究.遥感文选[C],科学出版社. 1981,315-323
[122]童庆禧,中国典型地物波谱及其特征分析,北京:科学出版社,1990
[123]徐彬彬,季耿善,朱永豪.中国陆地背景和土壤光谱反射特性的地理分区的初步研究[J].环境遥感,1991,6(2):142-151.
[124]朱永豪,邓仁达,等,不同湿度条件下黄棕壤光谱反射率的变化特征及其遥感意义。土壤学报,1984,21(2):194-202
[125]季耿善,徐彬彬。土壤粘土矿物特征及其在土壤学上的应用,土壤学报,1987,24(1):67-76
[126]徐彬彬.我国土壤光谱线之研究[J],环境遥感,1991,6(1):61-71.
[127]张文群,蒋光润。定西遥感试验场土壤光谱特征分析。遥感技术与应用,1992,7(2):25-31
[128] Chen F, D. E. Kissell, L. T. West, and W. Adkins. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. Soil Sci. Soc. Am. J., 2000,64: 746-753.
[129] Cierniewski J. A model for soil surface roughness influence on the spectral response of bare soils in the visible and near infrared range, Remote Sensing of Environment, 1987, 23: 98-115.
[130] Cierniewski J. The influence of the viewing geometry of bare soil surface on their spectral response in the visible and near infrared, Remote Sensing of Environment, 1989, 27: 135-142.
[131]徐希孺.遥感物理[M].北京:北京大学出版社, 2005,533.
[132] Escadafal R. Caracterisation de la surface des slos arides par obvervations de terrain et par teledetection [D]. Paris University, Paris, France. 1989.
[133] Hapke B. Bidirectional reflectance spectroscopy. 1. Theory. J. Geophys. Res., 1981, 86: 3039-3054.
[134] Hapke B, Wells E. Bidirectional reflectance spectroscopy. 2. Experiments and ovservations. J. Geophys. Res., 1981, 86: 3055-3060.
[135] Hapke B. Bidirectional reflectance spectroscopy. 3. Correction for macroscopic roughness. Icarus, 1984, 59: 41-59.
[136] Hapke B. Bidirectional reflectance spectroscopy. 4. The extinction coefficient and the opposition effect. Icarus, 1986, 67: 264-280.
[137] Jacquemoud S, Baret E, Hanocq J F. Modeling spectral and bidirectional soil reflectance. Remote Sensing of the Envionment, 1992, 41: 123-132.
[138] Liang S, Townshend J R G. A Modified Hapke Model for Soil Bidirectional Reflectance. Remote Sensing of the Envionment, 1996, 55: 1-10.
[139] Cierniewski J, Gdala T, Karnieli A. A hemispherical-directional reflectance model as a tool for understanding image distinctions between cultivated and uncultivated bare surfaces. Remote Sensing of the Envionment, 2004, 90(4): 505-523.
[140] Cappell A, Zobeck T M, Brunner G. Using bi-directional soil spectral reflectance to model soil surface changes induced by rainfall and wind-tunnel abrasion. Remote Sensing of the Envionment, 2006, 102(3-4): 328-343.
[141] Wassenaar T, Andrieux P, Baret F and Robbez-Masson J M. Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area. CATENA, 2005, 62(2-3): 94-110.
[142] Upadhyay T P, Solberg B, Sankhayan P L. Use of models to analyse land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: A review and analysis. Forest Policy and Economics, 2006, 9(4):349-371.
[143] Chappell A, Strong C, McTainsh G, Leys J. Detecting induced in situ erodibility of a dust-producing playa in Australia using a bi-directional soil spectral reflectance model. RemoteSensing of the Envionment, 2007, 106: 508-524.
[144]赵云升,韩阳.长白山典型地区偏振反射数据库.长白山地理系统研究(第三辑),长春:东北师范大学出版社,2010.
[145] Palacios-Orueta A, Ustin S L. Remote sensing of soil properties in the Santa Monica Mountains I. Spectral Analysis [J]. Remote Sens. Environ., 1998,65:170-183.
[146]韩阳,赵云升,等.森林土壤多角度高光谱偏振反射影响研究初探[J],光谱学与光谱分析,2009,29(3):702-706.
[147]宋开山,赵云升,张柏.土壤偏振反射特性的多角度测量与研究[J],土壤通报,2004,35(4):420-425.
[148]万余庆,谭克龙,周日平,等.高光谱遥感应用研究[M].北京:科学出版社,2006.
[149]张柏,禹秉熙,苏阳.遥感信息系统研究[C] .北京:科学出版社, 2000. 31-34.
[150]赵云升,金伦,张洪波,等.土壤的偏振反射特性研究[J].东北师大学报, 2000, 32(4):93-97.
[151]吕国楷,洪启旺,郝允充,等.遥感概论[M].北京:高等教育出版社, 1995. 21-31.
[152]李民赞.光谱分析技术及其应用[M],北京:科学出版社,2006年8月.
[153] Neema,D I, Shah A, Patel A N. A Statistical Optical Model for Light Reflection and Penetration Through Sand[J]. Intermational Journal of Remote Sensing, 1987, 8(8):1209-1217.
[154] Azzam R M A, Bashara N M. Ellipsometry and polarized light [M]. Amsterdam: North-Holland Publishing Company, 1997: 1-98
[155]张莉莉.利用偏振高光谱反演植被叶绿素含量[D]:[硕士学位论文].东北师范大学,2007.
[156]卜兆宏,唐万龙,刘复新,等,水土流失定量遥感方法新进展及其在太湖流域的应用,土壤学报,2003,40(1):1-9.
[157] Bréon F, Tanre D, Lecomte P, et al. Polarized reflectance of bare soils and vegetation–measurements and models, IEEE Transactions on Geoscience and Remote Sensing, 1995, 3(2): 487-499.
[158] Bicheron, Patrice; Leroy, Marc. A Method of Biophysical Parameter Retrieval at Global scale by Inversion of a Vegetation Reflectance Model [J], Remote Sensing of Environment, 1999, Vol.67, Issue:3: 251-266
[159]杜嘉,赵云升,宋开山,等。偏振遥感测量中土壤偏振度随太阳高度角的变化规律初探[J],地理科学,2007, 27(5):707-710.
[160]张成军.实验设计与数据处理[M],北京:化学工业出版社,2009年10月.
[161]刘文卿.实验设计[M],北京:清华大学出版社,2005年2月
[162] Coulson K L, Whitehead V S, Campbell C. Polarized views of the earth from orbital altitude PPIE 637 [J]. Ocean Opt, 1986, 8: 35-41.
[163]方开泰,马长兴.均匀实验与正交实验[M].北京:科学出版社, 2001. 35-77.
[164]姚春生,使用MODIS数据反演土壤水分研究[D],中科院遥感应用研究所,2003年6月.
[165] Wan Zhengming. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products [J], Remote Sensing of Environment, 2008, 112: 59-74.
[166]赵英时,等.遥感应用分析原理与方法[J].北京:科学出版社,2003.
[167] Wan Z, Wang P, Li X. Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA [J]. Int. J. Remote Sensing, 2004, 25(1):61-72.
[168] Goward S N, Cruickhanks G D, and Hope A S. Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape [J]. Remote Sens. Environ., 1985, 18(2): 137–146.
[169] Nemani R R and Running S W. Estimation of regional surface resistance to evapotranspiration from NDVI and thermal IR AVHRR data [J]. J. Appl. Meteorol., 1989, 28(4): 276–284.
[170] Nemani R R, Pierce L, Running S W, and Goward S N. Developing satellite-derived estimates of surface moisture status [J]. J. Appl. Meteorol., 1993, 32(3): 548–557.
[171] Moran M S, Clarke T R, Inoue Y, and Vidal A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index [J]. Remote Sens. Environ., 1994, 49(3): 246–263.
[172] Sandholt I, Rasmussena K, and Andersenb J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status [J]. Remote Sens. Environ., 2002, 79(2): 213–224.
[173] Nishida K, Nemani R R,. Glassy J M and Running S W. Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status [J]. IEEE Trans. Geosci. RemoteSens., 2003, 41(2): 453–501.
[174] Carlson T. An overview of the‘Triangle method’for estimating surface evapotranspiration and soil moisture from satellite imagery [J]. Sensors, 2007, 7(8): 1612–1629.
[175] Goward S N, Cruickhanks G D and Hope A S. Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape [J]. Remote Sens. Environ., 1985, 18(2): 137–146.
[176] Gillies R R, Kustas W P and Humes K S. A verification of the“triangle”method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature [J]. Int. J. Remote Sens., 1997, 18(15): 3145–3166.
[177] Han Y, Wang Y and Zhao Y.. Estimating soil moisture conditions of the Greater Changbai Mountains by Land Surface Temperature and NDVI [J]. IEEE Trans. Geosci. Remote Sens., 2010, 48(6): 2509-2515.
[178]周清,周斌,张杨珠,等。水稻土SOM含量高光谱模型的母质差异性研究[J].科学通报,2004, 20(6):471-475.