光谱学与光谱分析 |
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Effect of Foliar Dustfall Content (FDC) on High Spectral Characteristics of Pear Leaves and Remote Sensing Quantitative Inversion of FDC |
PENG Jie, WANG Jia-qiang, XIANG Hong-ying, NIU Jian-long, CHI Chun-ming, LIU Wei-yang |
College of Plant Science, Tarium University, Alar 843300, China |
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Abstract The precipitation of floating and sinking dust on leaves of plants is called as foliar dustfall. To monitor foliar dustfallIt, it will provide fundamental basis for environmental assessment and agricultural disaster evaluation of dust area. Therefore, the aim of this work to (1) study the effect of foliar dustfall content (FDC) on high spectral characteristics of pear leaves, (2) analyze the relationship between reflectances and FDC, and (3) establish high spectral remote sensing quantitative inversion model of FDC. The results showed that FDC increased reflectances of visible band (400~700 nm) with maximum band of 666 nm. Absolute and relative rates of change were -10.50% and -62.89%, respectively. The FDC decreased reflectances of near infrared band (701~1 050 nm) with maximum band of 758 nm. Absolute and relative rates of change were 12.04% and 41.75%, respectively. After dustfall was removed, reflection peak of green light and absorption valley of red and blue light became prominent, and slope of 500~750 nm wave band increased when FDC was more than 20 g·m-2. While FDC just slightly affected shape and area of reflection peak of green light when FDC was less than 20 g·m-2. FDC were positive and negative correlated with reflectances of visible band and near infrared band, respectively. Maximum correlation coefficient (0.61) showed at 663 nm. All of 7 inversion models, the model based on the first-order differential of logarithm of the reciprocal had better stability and predictive ability. The coefficient of determination(R2), root mean square error (RMSE) and relative percent deviation (RPD) of this model were 0.78, 3.37 and 2.09, respectively. The results of this study can provide a certain reference basis for hyperspectral remote sensing of FDC.
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Received: 2014-06-07
Accepted: 2014-10-10
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Corresponding Authors:
PENG Jie
E-mail: pjzky@163.com
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