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Alar, China

Peng J.,Tarium University | Xiang H.-Y.,Tarium University | Wang J.-Q.,Tarium University | Ji W.-J.,Zhejiang University | And 3 more authors.
Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | Year: 2013

By analyzing hyperspectral features of elm foliar dustfall content (FDC), a models of hyperspectral monitoring was built. Relationship between hyperspectral parameters and FDC was investigated by using regression analysis method. The results showed that FDC increased spectral reflectance in the visible band while decreased it in the near infrared band. Foliar dust didn't affect the "three edge" position but significantly affected its amplitudes and areas. FDC of elm was badly predicted with the models based on spectrum index or "three edge" parameter. Models based on multivariate linear regression, principal component regression and partial least squares regression can predict FDC primely. The model with 1st derivative value as variables was the best one for estimating FDC by the hyperspectral. Predictive correlation coefficient, predictive root mean square error, and the ratio of sample standard deviation to predictive root mean square error of this model were 0.92, 1.06, and 8.2, respectively.

Peng J.,Tarium University | Wang J.-Q.,Tarium University | Xiang H.-Y.,Tarium University | Niu J.-L.,Tarium University | And 2 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2015

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. ©, 2015, Science Press. All right reserved.

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