光谱学与光谱分析 |
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Modeling Polarimetric BRDF of Leaves Surfaces |
XIE Dong-hui1, WANG Pei-juan2, ZHU Qi-jiang1, ZHOU Hong-min1 |
1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, School of Geography and Remote Sensing Science, Beijing Normal University, Beijing 100875, China 2. Chinese Academy of Meteorological Sciences 100081, China |
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Abstract The purpose of the present paper is to model a physical polarimetric bidirectional reflectance distribution function (pBRDF), which can character not only the non-Lambertian but also the polarized features in order that the pBRDF can be applied to analyze the relationship between the degree of polarization and the physiological and biochemical parameters of leaves quantitatively later. Firstly, the bidirectional polarized reflectance distributions from several leaves surfaces were measured by the polarized goniometer developed by Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. The samples of leaves include two pieces of zea mays L. leaves (young leaf and mature leaf) and a piece of E. palcherrima wild leaf. Non-Lambertian characteristics of directional reflectance from the surfaces of these three leaves are obvious. A Cook-Torrance model was modified by coupling the polarized Fresnel equations to simulate the bidirectional polarized reflectance properties of leaves surfaces. The three parameters in the modified pBRDF model, such as diffuse reflectivity, refractive index and roughness of leaf surface were inversed with genetic algorithm (GA). It was found that the pBRDF model can fit with the measured data well. In addition, these parameters in the model are related with both the physiological and biochemical properties and the polarized characteristics of leaves, therefore it is possible to build the relationships between them later.
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Received: 2010-01-22
Accepted: 2010-04-26
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Corresponding Authors:
XIE Dong-hui
E-mail: xiedonghui@bnu.edu.cn
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[1] ZHANG Hong, ZHU Qi-jiang(张 红, 朱启疆). Journal of Remote Sensing(遥感学报), 1997, 1(Supp.1): 36. [2] Jacquemoud S, Ustin S L. Proc. 8th International Symposium Physical Measurements & Signatures in Remote Sensing, Aussois(France), 2001. 223. [3] Ustin S L, Jacquemoud S, Govaerts Y. Plant Cell Environment, 2001, 24: 1095. [4] Jacquemoud S, Baret F. Remote Sens. Environ., 1990, 34: 75. [5] Jacquemoud S, Ustin S L, Verdebout J, et al. Remote Sens. Environ., 1996, 56: 194. [6] Dawson T P, Curran P J, Plummer S E. Remote Sens. Environ., 1998, 65: 50. [7] Bousquet L, Lachérade S, Jacquemoud S, et al. Remote Sens. Environ., 2005, 98: 201. [8] Brakke T W, Smith J A, Harnden J M. Remote Sens. Environ., 1989, 29: 185. [9] Walter-Shea E A, Norman J M, Blad B L. Remote Sens. Environ., 1989, 29: 161. [10] SONG Kai-shan, ZHAO Yun-sheng, JIN Lun(宋开山, 赵云升, 金 伦). Journal of Northest Normal University(Natrural Science)(东北师范大学学报·自然科学版), 2000, 32(4): 112. [11] Grant L, Daughtry C S T, Vanderbilt V C. Purdue Univ., LARS Tech. Rep. 081583, 1983. [12] Govaerts Y M, Jacquemoud S, Verstraete M M, et al. Appl. Optics, 1996, 35(33): 6585. [13] Grant L. Remote Sens. Environ., 1987, 22: 309. [14] ZHAO Yun-sheng, HUANG Fang, JIN Lun, et al(赵云升, 黄 方, 金 伦, 等). Journal of Remote Sensing(遥感学报) , 2000, 4(2):131. [15] XU Xi-ru(徐希孺). Physics of Remote Sensing(遥感物理). Beijing: Peking University Press(北京:北京大学出版社),2005. [16] JIN Xi-feng, QIAO De-lin, ZHOU Su-xiang(金锡峰, 乔德林, 周素香). The Measuring Device for Bidirectional Polarized Reflectance, Patent No.96239489.0(地物偏振二向性反射比测量装置, 专利号: 96239489.0), 1998. [17] Torrance K E, Sparrow E M. J. Heat Transfer C, 1966, 88: 223.
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