光谱学与光谱分析 |
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Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum |
YANG Shao-yuan1, 2, 3, HUANG Wen-jiang1*, LIANG Dong2, 3, HUANG Lin-sheng2, 3, YANG Gui-jun4, ZHANG Dong-yan2, 3, CAI Shu-hong5 |
1. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. Key Laboratory of Intelligent Computer & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China 3. School of Electronic and Information Engineering, Anhui University, Hefei 230039, China 4. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China 5. Hebei Agricultural Technique Extension Station, Shijiazhuang 050000, China |
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Abstract The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing enviro nmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, ±50°and ±60°; at nadir, ±30°and ±40°; and at nadir, ±20°and ±30°were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were established. The impact of soil reflectance and the canopy non-photosynthetic materials was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution
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Received: 2014-04-14
Accepted: 2014-07-20
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Corresponding Authors:
HUANG Wen-jiang
E-mail: huangwenjiang@gmail.com
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[1] Johnson L F. Remote Sensing of Environment, 2001, 78(3): 314. [2] Eitel J U H, Vierling L A, Long D S, et al. Agricultural and Forest Meteorology, 2011, 151(10): 1338. [3] Bausch W C, Duke H R, Iremonger C J. Precision Agriculture, 1996(precisionagricu3): 23. [4] Ranjan R, Chopra U K, Sahoo R N, et al. International Journal of Remote Sensing, 2012, 33(20): 6342. [5] Herrmann I, Karnieli A, Bonfil D J, et al. International Journal of Remote Sensing, 2010, 31(19): 5127. [6] Daughtry C, Walthall C, Kim M, et al. Remote Sensing of Environment, 2000, 74(2): 229. [7] Eitel J U H, Long D S, Gessler P E, et al. International Journal of Remote Sensing, 2007, 28(18): 4183. [8] Connor D J, Sadras V O, Hall A J. Oecologia, 1995, 101(3): 274. [9] Serrano L, Filella I, Penuelas J. Crop Science, 2000, 40(3): 723. [10] Huang W, Wang Z, Huang L, et al. Precision agriculture, 2011, 12(2): 165. [11] WANG Hui-wen(王惠文). Partial Least Squares Regression Method and Application(偏最小二乘回归方法及其应用). Beijing: National Defence Industry Press(北京:国防工业出版社), 1999. [12] Gitelson A A, Kaufman Y J, Merzlyak M N. Remote Sensing of Environment, 1996, 58(3): 289. [13] Penuelas J, Filella I, Gamon J A. New Phytologist, 1995, 131(3): 291. [14] Penuelas J, Baret F, Filella I. Photosynthetica, 1995, 31(2): 221. [15] Penuelas J, Gamon J, Fredeen A, et al. Remote Sensing of Environment, 1994, 48(2): 135. [16] ZHAO Chun-jiang, HUANG Wen-jiang, WANG Ji-hua, et al(赵春江, 黄文江, 王纪华). Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2006, 22(6): 104. |
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