[The inversion of soil alkaline hydrolysis nutrient content with hyperspectral reflectance based on wavelet analysis].

One hundred thirty for soil samples of Qitai in Xinjiang were selected, and the first derivative spectrum of the soil sample logarithmic reflectance was decomposed to many layers by using 4 wavelet functions respectively, and PLSR was used to establish the prediction models respectively, and precision values were tested. The results show that: 1-3 layers low-frequency coefficients of wavelet decomposition were better, while the rest were worse. In 6 layers of all function decomposition, the highest accuracy of inversion models constructed by low-frequency coefficients were all ca2, while with increasing the decomposition layers, the precision and significance decreased significantly.

[Comparative analysis of soil organic matter content based on different hyperspectral inversion models].

The present paper, based on the Qitai county of Xinjiang, selected 40 soil samples, and used two methods respectively, i.e. multiple linear stepwise regression (MLSR) and artificial neural network (ANNs), to establish the inversion and predieting model of soil organic matter (SOM) content and the model test from measured reflectance spectra and relative test were carried through to the models.

[Response characteristics of the field-measured spectrum for the four general types of halophyte and species recognition in the northern slope area of Tianshan Mountain in Xinjiang].

Based on the field-measured Vis-NIR reflectance of four common types of halophyte (Achnatherum splendens(Trin.) Nevski, Sophora alopecuroides L., Camphorosma monspeliaca L.