[Effect of Foliar Dustfall Content (FDC) on High Spectral Characteristics of Pear Leaves and Remote Sensing Quantitative Inversion of FDC].

The precipitation of floating and sinking dust on leaves of plants is called as foliar dustfall. To monitor foliar dustfall, 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 nrn) 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 wake 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.

[Comparative study on hyperspectral inversion accuracy of soil salt content and electrical conductivity].

The objective of the present article is to ascertain the mechanism of hyperspectral remote sensing monitoring for soil salinization, which is of great importance for improving the accuracy of hyperspectral remote sensing monitoring. Paddy soils in Wensu, Hetian and Baicheng counties of the southern Xinjiang were selected. Hyperspectral data of soils were obtained. Soil salt content (S(t)) an electrical conductivity of 1:5 soil-to-water extracts (EC(1:5)) were determined. Relationships between S(t) and EC(1:5) were studied. Correlations between hyperspectral indices and S(t), and EC(1:5) were analyzed. The inversion accuracy of S(t) using hyperspectral technique was compared with that of EC(1:5). Results showed that: significant (p<0.01) relationships were found between S(t) and EC(1:5) for soils in Wensu and Hetian counties, and correlation coefficients were 0.86 and 0.45, respectively; there was no significant relationship between S(t) and EC(1:5) for soils in Baicheng county. Therefore, the correlations between S(t) and EC(1:5) varied with studied sites. S(t) and EC(1:5) were significantly related with spectral reflectance, first derivative reflectance and continuum-removed reflectance, respectively; but correlation coefficients between S(t) and spectral indices were higher than those between EC(1:5) and spectral indices, which was obvious in some sensitive bands for soil salinization such as 660, 35, 1229, 1414, 1721, 1738, 1772, 2309 nm, and so on. Prediction equations of St and EC(1:5) were established using multivariate linear regression, principal component regression and partial least-squares regression methods, respectively. Coefficients of determination, determination coefficients of prediction, and relative analytical errors of these equations were analyzed. Coefficients of determination and relative analytical errors of equations between S(t) and spectral indices were higher than those of equations between EC(1:5) and spectral indices. Therefore, the responses of high spectral information to St were more sensitive than those of high spectral information to EC(1:5). Accuracy of St predicted from high spectral data was higher than that of EC(1:5) estimated from high spectral data. The results of this study can provide a theoretical basis to improve hyperspectral remote sensing monitoring accuracy of soil salinization.