Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging

Zhang Dongyan; Liang Dong; Zhao Jinling; Coburn Craig; Wang Zhijie; Wang Xiu

Volume 42 Issue 3

Feb. 2014

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Article Navigation > Infrared and Laser Engineering > 2013 > 42(3): 787-797

Zhang Dongyan, Liang Dong, Zhao Jinling, Coburn Craig, Wang Zhijie, Wang Xiu. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3): 787-797.

Citation:

Zhang Dongyan, Liang Dong, Zhao Jinling, Coburn Craig, Wang Zhijie, Wang Xiu. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3): 787-797.

Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging

1.
Key Laboratory of Intelligent Computing & Signal Processing,Ministry of Education,Anhui University,Hefei 230039,China;
2.
Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;
3.
Department of Geography,University of Lethbridge,Alberta T1K3M4,Canada

Received Date: 2012-07-22
Rev Recd Date: 2012-08-19
Publish Date: 2013-03-25

Abstract

Research on bidirectional reflectance characteristics of vegetation canopy is an important direction for quantitative remote sensing. The self-developed multi-angle observation system in this paper was used to collect imaging data of soybean in different sowing density from branch period to flowering period. Characteristics of changes in Bidirectional Reflectance (BR) for pure vegetation and mixed canopy, including vegetation and soil, were analyzed by hyperspectral images from segmenting vegetation, background soil, and shadow leaves. Studies have shown that observation position is the principal plane; canopy reflectance of pure vegetation after soil spectra is removed, gradually increased along with the decrease of zenith angle. When forward observation was conducted in principal plane, it differed from the canopy composed of vegetation and soil. When observation direction changed from backward to forward in the principal plane, canopy reflectance of pure vegetation in visible and near-infrared region showed a growing tendency gradually and the soil spectra was removed, this was different from before. In addition, when the observation position was in a perpendicular plane, there was a similar change for reflectance of soybean canopy before and after soil spectra was removed; but the symmetry of canopy reflectance of the former was better than the latter. The results had similar trends with BR change of soybean canopy in different sowing density. The paper provides a basis for the development of multi-angle remote sensing.
- analysis of image and spectrum,
- BR,
- zenith angle,
- azimuth angle,
- soybea

References

[1]	Feng Xiaoming, Zhao Yingshi. A spectral -directional reflectance remote sensing model of the semiarid landscape[J]. Journal of Remote Sensing, 2005, 9 (4): 337-342. (in Chinese)
[2]
[3]
[4]	Wu Chaoyang, Niu Zheng, Wang Jindi, et al. Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements [J]. Canadian Journal of Remote Sensing, 2010, 36(4): 301-312.
[5]	Qin Wenhan, Xiang Yueqin. An analytica1 model for bidirectional reflectance factor of multi -component vegetation canopies [J]. Science China (Series C), l996, 26: 542-551. (in Chinese)
[6]
[7]	Sandmeier St, Muller Ch, Hosgood B, et al. Physical mechanisms in hyperspectral BRDF data o f grass and watercress [J]. Remote Sensing of Environment, 1998, 66: 222-233.
[8]
[9]	Sandmeier S, Deering D W. Structure analysis and classification of Boreal forests using airborne hyperspectral BRDF data from ASAS [J]. Remote Sensing of Environment, 1999, 69: 281-295.
[10]
[11]
[12]	D'Entremont, Schaaf R P, Lucht C B, et al. Retrieval of red spectral albedo and bidirectional reflectance from 1km2 satellite observations for the New England region [J]. Journal of Geophysical Research, 1999, 104: 6229-6339.
[13]
[14]	Gao Feng, Schaaf C B, Strahler A H, et al. Detecting vegetation structure using a Kernel-Based BRDF model [J]. Remote Sensing of Environment, 2003, 86:1 98-205.
[15]	Shen Guangrong, Wang Renchao. A study on multicomponent bidirectional reflectance model for rice [J]. Chinese Journal of Applied Ecology, 2003, 14(3): 394-398. (in Chinese)
[16]
[17]	Li Yunmei. Theroy and Application of Vegetation Radiation Transfor [M]. Nanjing: Nanjing Normal Unversity, 2005: 11. (in Chinese)
[18]
[19]	Schneider Th, Manakos I. BRDF approximation of maize and canopy parameter retrieval by ProSail inversion [C]//The 3rd EARSEL Workshop on Imaging Spectroscopy, 2003, 5: 13-16.
[20]
[21]
[22]	Huang Wenjiang, Wang Jihua, Liu Liangyun, et al. Remote sensing identification of plant structural types based on multi -temporal and bidirectional canopy spectrum [J]. Transactions of The Chinese Society of Agricultural Engineering, 2005, 21(6) :1-5. (in Chinese)
[23]
[24]	Schut. Imaging spectroscopy for characterization of grass swards [D]. Netherlands: Wageningen University, 2003.
[25]	Casa R, Jones H G. Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification [J]. International Journal of Remote Sensing, 2004, 25( 6): 1119-1130.
[26]
[27]	Zhang Dongyan. Diagnosis mechanism and methods of crop chlorophyll information based on hypersepctral imaging technology [D]. Hangzhou: Zhejiang University, 2012. (in Chinese)
[28]
[29]	Wang Zhijie, Coburn C A, Ren Xuemin, et al. Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor [J]. Canadian Journal of Soil Science, 2012, 92(2): 297-313.
[30]
[31]	Liu Qinhuo, Xin Xiaozhou, Tang Pin, et al. Research Model,Application and Uncertainty of Quantitative Remote Sensing [M]. Beijing: Science Press, 2010: 1. (in Chinese)
[32]
[33]	Wang Zhijie, Coburn C A, Ren Xuemin, et al. Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor [J]. Canadian Journal of Soil Science, 2012, 92(2): 297-313.
[34]
[35]	Li Xiaowen, Strahler A, Zhu Qijiang. Geometric -optical bidirectional reflectance modelling of ground objects and its progress in measurement [J]. Remote Sensing For Land Resources, 1991, 7(1): 9-19. (in Chinese)
[36]
[37]	Kimes D S, Newcomb W W, Tucker C J, et al. Directional reflectance factor distribution for cover types of Northern Afirica [J]. Remote Sensing of Enviroment, 1985, 18:1-19.
[38]
[39]
[40]	Kimes D S. Dynamics of directional reflectance factor distributions for vegetation canopies [J]. Application Optics, 1993, 22(9): 1364-1372.
[41]
[42]	Kuusk A. The angular distribution of reflectance and vegetation indices in barley and clover canopies [J]. Remote Sensing of Enviroment, 1991, 37: 143-151.
[43]
[44]	Li Yunmei. Studying on rice BRDF model integration and its application [D]. Hangzhou: Zhejiang Unversity, 2001. (in Chinese)
[45]
[46]	Chen Jieliang. Hyperspectral Remote Sensing Information Extraction and BRDF Model of Soil [M]. Hangzhou: Zhejiang University, 2008. (in Chinese)
[47]	Li Yunmei, Wang Renchao, Wang Xiuzhen, et al. Effect of rice canopy structural changes on bidirectional reflectance[J]. Chinese Journal of Applied Ecology, 2001, 32 (3):47-52.(in Chinese)
[48]
[49]
[50]	Haboudane D, Miller J, Tremblay N, et al. Integrated narrow -band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensing of Environment, 2002, 81: 416-426.
[51]
[52]	Tan Changwei, Wang Jihua, Lu Jianfei, et al. Summer maize growth supervision and nutrition diagnosis with red edge parameters [J]. Chinese Journal of Eco-Agriculture, 2007, 15(1): 82-86. (in Chinese)
[53]
[54]	Niu Zheng. Recent advance in studies on vegetative bidirectional reflecting property [J]. Remote Sensing Technology and Appication, 1997, 12(3): 49-57. (in Chinese)
[55]	Li Yunmei, Wang Renchao, Wang Xiuzhen, et al. Simulation of bidirectional reflectance on rice canopy and its inversion[J]. Chinese Journal of Rice Science, 2002, 16 (3): 291 -294. (in Chinese)
[56]	Fan Wenjie, Yan Binyan, Xu Xiru, et al. Crop area and leaf area index simultaneous retrieval based on spatial scaling transformation [J]. Science China Earth Science, 2010, 40 (12): 1735-1732. (in Chinese)
[57]
[58]	Yang Guijun, Xing Zhurong, Huang Wenjiang, et al. Analysis of winter wheat canopy structure for different plant types of growth period [J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(7): 227-234.(in Chinese)
[59]
[60]	Zhang Zhengyi. Effect on Yield and Quality of Soybean in Different Sowing Density and Relay-cropping [M]. Chengdu: Sichuan Agricultural University, 2008. (in Chinese)
[61]
[62]	Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages [J]. Remote Sensing of Environment, 2002, 81: 337-354.
[63]
[64]	Haboudane D, Miller J R, Pattey E. Hyperspectral vegetation in dices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture [J]. Remote Sensing of Environment, 2004, 90: 337-352.
[65]

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging

1. Key Laboratory of Intelligent Computing & Signal Processing,Ministry of Education,Anhui University,Hefei 230039,China;

2. Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China;

3. Department of Geography,University of Lethbridge,Alberta T1K3M4,Canada

Received Date: 2012-07-22

Rev Recd Date: 2012-08-19

Publish Date: 2013-03-25

Key words:

Abstract: Research on bidirectional reflectance characteristics of vegetation canopy is an important direction for quantitative remote sensing. The self-developed multi-angle observation system in this paper was used to collect imaging data of soybean in different sowing density from branch period to flowering period. Characteristics of changes in Bidirectional Reflectance (BR) for pure vegetation and mixed canopy, including vegetation and soil, were analyzed by hyperspectral images from segmenting vegetation, background soil, and shadow leaves. Studies have shown that observation position is the principal plane; canopy reflectance of pure vegetation after soil spectra is removed, gradually increased along with the decrease of zenith angle. When forward observation was conducted in principal plane, it differed from the canopy composed of vegetation and soil. When observation direction changed from backward to forward in the principal plane, canopy reflectance of pure vegetation in visible and near-infrared region showed a growing tendency gradually and the soil spectra was removed, this was different from before. In addition, when the observation position was in a perpendicular plane, there was a similar change for reflectance of soybean canopy before and after soil spectra was removed; but the symmetry of canopy reflectance of the former was better than the latter. The results had similar trends with BR change of soybean canopy in different sowing density. The paper provides a basis for the development of multi-angle remote sensing.

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Reference (65)

[1]	Feng Xiaoming, Zhao Yingshi. A spectral -directional reflectance remote sensing model of the semiarid landscape[J]. Journal of Remote Sensing, 2005, 9 (4): 337-342. (in Chinese)
[2]
[3]
[4]	Wu Chaoyang, Niu Zheng, Wang Jindi, et al. Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements [J]. Canadian Journal of Remote Sensing, 2010, 36(4): 301-312.
[5]	Qin Wenhan, Xiang Yueqin. An analytica1 model for bidirectional reflectance factor of multi -component vegetation canopies [J]. Science China (Series C), l996, 26: 542-551. (in Chinese)
[6]
[7]	Sandmeier St, Muller Ch, Hosgood B, et al. Physical mechanisms in hyperspectral BRDF data o f grass and watercress [J]. Remote Sensing of Environment, 1998, 66: 222-233.
[8]
[9]	Sandmeier S, Deering D W. Structure analysis and classification of Boreal forests using airborne hyperspectral BRDF data from ASAS [J]. Remote Sensing of Environment, 1999, 69: 281-295.
[10]
[11]
[12]	D'Entremont, Schaaf R P, Lucht C B, et al. Retrieval of red spectral albedo and bidirectional reflectance from 1km2 satellite observations for the New England region [J]. Journal of Geophysical Research, 1999, 104: 6229-6339.
[13]
[14]	Gao Feng, Schaaf C B, Strahler A H, et al. Detecting vegetation structure using a Kernel-Based BRDF model [J]. Remote Sensing of Environment, 2003, 86:1 98-205.
[15]	Shen Guangrong, Wang Renchao. A study on multicomponent bidirectional reflectance model for rice [J]. Chinese Journal of Applied Ecology, 2003, 14(3): 394-398. (in Chinese)
[16]
[17]	Li Yunmei. Theroy and Application of Vegetation Radiation Transfor [M]. Nanjing: Nanjing Normal Unversity, 2005: 11. (in Chinese)
[18]
[19]	Schneider Th, Manakos I. BRDF approximation of maize and canopy parameter retrieval by ProSail inversion [C]//The 3rd EARSEL Workshop on Imaging Spectroscopy, 2003, 5: 13-16.
[20]
[21]
[22]	Huang Wenjiang, Wang Jihua, Liu Liangyun, et al. Remote sensing identification of plant structural types based on multi -temporal and bidirectional canopy spectrum [J]. Transactions of The Chinese Society of Agricultural Engineering, 2005, 21(6) :1-5. (in Chinese)
[23]
[24]	Schut. Imaging spectroscopy for characterization of grass swards [D]. Netherlands: Wageningen University, 2003.
[25]	Casa R, Jones H G. Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification [J]. International Journal of Remote Sensing, 2004, 25( 6): 1119-1130.
[26]
[27]	Zhang Dongyan. Diagnosis mechanism and methods of crop chlorophyll information based on hypersepctral imaging technology [D]. Hangzhou: Zhejiang University, 2012. (in Chinese)
[28]
[29]	Wang Zhijie, Coburn C A, Ren Xuemin, et al. Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor [J]. Canadian Journal of Soil Science, 2012, 92(2): 297-313.
[30]
[31]	Liu Qinhuo, Xin Xiaozhou, Tang Pin, et al. Research Model,Application and Uncertainty of Quantitative Remote Sensing [M]. Beijing: Science Press, 2010: 1. (in Chinese)
[32]
[33]	Wang Zhijie, Coburn C A, Ren Xuemin, et al. Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor [J]. Canadian Journal of Soil Science, 2012, 92(2): 297-313.
[34]
[35]	Li Xiaowen, Strahler A, Zhu Qijiang. Geometric -optical bidirectional reflectance modelling of ground objects and its progress in measurement [J]. Remote Sensing For Land Resources, 1991, 7(1): 9-19. (in Chinese)
[36]
[37]	Kimes D S, Newcomb W W, Tucker C J, et al. Directional reflectance factor distribution for cover types of Northern Afirica [J]. Remote Sensing of Enviroment, 1985, 18:1-19.
[38]
[39]
[40]	Kimes D S. Dynamics of directional reflectance factor distributions for vegetation canopies [J]. Application Optics, 1993, 22(9): 1364-1372.
[41]
[42]	Kuusk A. The angular distribution of reflectance and vegetation indices in barley and clover canopies [J]. Remote Sensing of Enviroment, 1991, 37: 143-151.
[43]
[44]	Li Yunmei. Studying on rice BRDF model integration and its application [D]. Hangzhou: Zhejiang Unversity, 2001. (in Chinese)
[45]
[46]	Chen Jieliang. Hyperspectral Remote Sensing Information Extraction and BRDF Model of Soil [M]. Hangzhou: Zhejiang University, 2008. (in Chinese)
[47]	Li Yunmei, Wang Renchao, Wang Xiuzhen, et al. Effect of rice canopy structural changes on bidirectional reflectance[J]. Chinese Journal of Applied Ecology, 2001, 32 (3):47-52.(in Chinese)
[48]
[49]
[50]	Haboudane D, Miller J, Tremblay N, et al. Integrated narrow -band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensing of Environment, 2002, 81: 416-426.
[51]
[52]	Tan Changwei, Wang Jihua, Lu Jianfei, et al. Summer maize growth supervision and nutrition diagnosis with red edge parameters [J]. Chinese Journal of Eco-Agriculture, 2007, 15(1): 82-86. (in Chinese)
[53]
[54]	Niu Zheng. Recent advance in studies on vegetative bidirectional reflecting property [J]. Remote Sensing Technology and Appication, 1997, 12(3): 49-57. (in Chinese)
[55]	Li Yunmei, Wang Renchao, Wang Xiuzhen, et al. Simulation of bidirectional reflectance on rice canopy and its inversion[J]. Chinese Journal of Rice Science, 2002, 16 (3): 291 -294. (in Chinese)
[56]	Fan Wenjie, Yan Binyan, Xu Xiru, et al. Crop area and leaf area index simultaneous retrieval based on spatial scaling transformation [J]. Science China Earth Science, 2010, 40 (12): 1735-1732. (in Chinese)
[57]
[58]	Yang Guijun, Xing Zhurong, Huang Wenjiang, et al. Analysis of winter wheat canopy structure for different plant types of growth period [J]. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(7): 227-234.(in Chinese)
[59]
[60]	Zhang Zhengyi. Effect on Yield and Quality of Soybean in Different Sowing Density and Relay-cropping [M]. Chengdu: Sichuan Agricultural University, 2008. (in Chinese)
[61]
[62]	Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages [J]. Remote Sensing of Environment, 2002, 81: 337-354.
[63]
[64]	Haboudane D, Miller J R, Pattey E. Hyperspectral vegetation in dices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture [J]. Remote Sensing of Environment, 2004, 90: 337-352.
[65]

Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging

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通讯作者: 陈斌, bchen63@163.com

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