| 光谱学与光谱分析 |
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| Impact of Dust-Fall on Spectral Features of Plant Leaves |
| LUO Na-na1, 2, ZHAO Wen-ji1, 2*, YAN Xing3 |
1. Key Laboratory of 3D Information Acquisition and Application of Ministry of Education, Capital Normal University, Beijing 100048, China
2. Resources, Environment and Geographic Information System Key Laboratory of Beijing, Capital Normal University, Beijing 100048, China
3. The Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China |
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Abstract In order to build inversion model of dust-fall weight by hyperspectral data, 30 samples were collected in Beijing. Through electronic balance and Analytical Spectral Devices FieldSpec Pro (ASD) analysis, the “dust leaves” and the “clean leaves” weight and spectral reflectance were determined respectively, which also obtained information of dust weight and spectral features. Then, based on tradition and partial least squares (PLS) model’s analysis, the relationship between dust weight and spectral reflectance was explored. The results showed that 350～700, 780～1 300 and 1 900～2 500 nm bands had apparently variations when they response to the different dust weights. In general, there was a negative relationship between dust weight and spectral reflectance, the maximum negative value -0.8 occurred at 737 band which belonged to near-infrared bands. In the analysis of dust weight with multi-band, it was indicated that NDVI index which was formed by 948 and 945 bands had a significant correlation (r=0.76) to dust. Finally, through accuracy assessment of regression model, the PLS could obtain a more accurate result than the traditional model.
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Received: 2013-06-09
Accepted: 2013-08-10
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Corresponding Authors:
ZHAO Wen-ji
E-mail: zhwenji1215@163.com
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[1] Wang Z F, Chen L F, Tao J H, et al. Remote Sensing of Environment, 2010, 114(1): 50.
[2] Fan S B, Tian G, Li G, et al. Atmospheric Environment, 2009, 43(38): 6003.
[3] Wang T, Xie S D, et al. Atmospheric Environment, 2009, 43(35): 5682.
[4] Sun Y L, Zhuang G S, Zhang W J, et al. Atmospheric Environment, 2006, 40(16): 2973.
[5] He K B, Yang F M, Ma Y L, et al. Atmospheric Environment, 2001, 35(29): 4959.
[6] Marc O, Hein D V B, Alex L A F. Ecological Engineering, 2010, 36(2): 154.
[7] Chudnovsky A, Ben-Dor E. Science of the Total Environment, 2008, 393(2-3): 198.
[8] Horler D H N, Dockray M, Barber J. International Journal of Remote Sensing, 1983, 4(2): 273.
[9] Rock B N, Ho shizaki T, Miller J R. Remote Sensing of Environment, 1988, 24(1): 10.
[10] LIU Ai-jun, WANG Bao-lin, HUANG Ping-ping, et al(刘爱军, 王保林, 黄平平, 等). Acta Agreatia Sinica(草地学报), 2012, 20(6): 1005.
[11] XIAO Shen-liang, CHEN Zhong-xin(肖伸亮, 陈仲新). Chinese Agricultural Science Bulletin(中国农学通报)，2007，23(4)：410.
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