光谱学与光谱分析 |
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Wheat Leaf Area Index Inversion Using Hyperspectral Remote Sensing Technology |
LIANG Liang1, 2, YANG Min-hua2, ZHANG Lian-peng1, LIN Hui1 |
1. School of Geodesy and Geomatics of Xuzhou Normal University, Xuzhou 221116, China 2. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China |
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Abstract The wheat leaf area index (LAI) was inverted using hyperspectral remote sensing technology in the present paper. Eighteen kinds of hyperspectral indices were comparatively analyzed, and the index OSAVI, which could reflect wheat LAI most sensitively, was screened out. The models for wheat LAI inversion were built using the field spectra as the training samples. The study showed that the calibration R-square and prediction R-square of the inversion model which were built by hyperspectral index OSAVI were 0.823 and 0.818, respectively, higher than that of other indices, indicating that the accuracy was highest. The inversion model was spatially quantitatively expressed in OMIS image, and then the inversion value and measured value was compared by the method of regression fitting. The R-square and RMSE of the fitting model were 0.756 and 0.500, respectively, indicating that the similarity between the inversion value and measured value was high. The result showed that it was feasible to invert the wheat LAI by hyperspectral indices, and OSVAI was an optimal one.
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Received: 2010-07-14
Accepted: 2010-10-20
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Corresponding Authors:
LIANG Liang
E-mail: liangliang198119@163.com
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[1] Inoue Y. Plant Production Science, 2003, 6(1): 3. [2] Wiegand C L, Gausman H W, Cuellar J A, et al. Proceedings of third ETRS Symposium, 1974, 1: 93. [3] Bunnik N J. The Multispectral Reflectance of Shortwave Radiation by Agricultural Crops in Relation with Their Morphological and Optical Properties. Wageningen: Veenman, 1978. 6. [4] Darvishzadeh R, Skidmore A, Atzberger C. et al. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(3): 358. [5] Müller K, Bttcher U, Meyer-Schatz F, et al. Biosystems Engineering, 2008, 101(2): 172. [6] Chen P F, Haboudane D, Tremblay N, et al. Remote Sensing of Environment, 2010, 114(9): 1987. [7] TONG Qing-xi, ZHENG Lan-fen(童庆禧, 郑兰芬). Journal of Remote Sensing(遥感学报), 1997, 1(1): 50. [8] Gitelson A A, Merzlyak M N. Journal of Plant Physiology, 1994, 143: 286. [9] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81(2-3): 337. [10] Datt B. Journal of Plant Physiology, 1999, 154: 30. [11] Curran P J, Windham W R, Gholz H L. Tree Physiology, 1995, 15: 203. [12] Gamon J A, Penuelas J, Field, C B. Remote Sensing of Environment, 1992, 41(1): 35. [13] Gamon J A, Serrano L, Surfus J S. Oecologia, 1997, 112(4): 492. [14] Rondeaux G, Steven M D, Baret F. Remote Sensing Environment, 1996, 55(2): 95. [15] Gupta R K, Vijayan D, Prasad T S. Advances in Space Research, 2001, 28(1): 201. [16] Marshak A, Knyazikhin Y, Davis A, et al. Geophysical Research Letters, 2000, 27(12): 1695. [17] Gupta R K, Vijayan D, Prasad T S. Advance Space Research, 2003, 32(11): 2217. [18] Haboudane D, Miller J R, Pattey E, et al. Remote Sensing of Environment, 2004, 90(3): 337. [19] Richardson A J, Wiegand C L. Photogrammetric Engineering and Remote Sensing, 1997, 43: 1541. [20] Broge N H,Leblanc E. Remote Sensing of Environment, 2001, 76(2): 156. [21] Gitelson A A, Merzlyak M N. Journal of Plant Physiology, 1996, 148(3-4): 494. [22] LIANG Liang, YANG Min-hua, LI Ying-fang(梁 亮, 杨敏华, 李英芳). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(10): 2724. [23] Qi J, Cabot F, Moran M S, et al. Remote sensing of Environment, 1995, 54(1): 71. [24] Qi J, Chehbouni A, Huete A R, et al. Remote Sensing of Environment, 1994, 48(2): 119. [25] LIANG Liang, YANG Min-hua, ZANG Zhuo(梁 亮, 杨敏华, 臧 卓). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(12): 248.
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