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Nitrogen cycle is an important process in the circle of soil ecosystem elements, and nitrification has significant effect on soil nitrogen cycling. The main completer of nitrification is nitrification microbial communities. Soil microorganisms are vital components of wetland ecosystem. They can indicate the variations of wetland ecological environment, and this helps us to have the correct understanding of nitrogen cycle and pollution purification function in wetland ecosystem. This paper tries to study nitrification microbial communities in wetland soils from the perspective of hyperspectral remote sensing technology, based on the monitoring mechanisms of soil nitrogen spectrum. The study explores hyperspectral estimation techniques for nitrification microbial communities in wetland soils, and it can provide a new technical approach to estimate the temporal and spatial distribution of nitrification microbial communities. The study adopted most probable number method (MPN) to count the numbers of ammonia oxidizing bacteria and nitrite oxidizing bacteria respectively, which were main completers of two independent stages in nitrification. And the total results of both count measurements were used as the values of soil nitrification microorganisms for each sampling area. The estimation models of nitrification microorganism and total nitrogen in wetland soils were developed respectively using spectral transformation techniques, such as log-transformed spectra (LR), first derivative (FD), second derivative (SD), continuum removal (CR) and band depth (BD), and modeling methods, such as stepwise multiple linear regression (SMLR) and partial least-squares regression (PLSR) based on the bootstrap technology. The results indicated that the selected estimation bands of nitrification microorganism and total nitrogen were close (especially for original spectral data (R) and SD spectra) when the modeling method of bootstrap SMLR was used. Compared to the bootstrap SMLR, the bootstrap PLSR achieved higher accuracies for estimating nitrification microorganism and total nitrogen in wetland soils. The spectral transformation technique of SD combined with the modeling method of bootstrap PLSR yielded the highest estimation accuracy to predict nitrification microorganism in wetland soils. The CR spectral data combined with bootstrap PLSR produced the highest estimation accuracy to predict total nitrogen content in wetland soils.

Because of their special structural characteristics, straw burning residues (biochar) have important impacts on the soil carbon sequestration and the transport and transformation behavior of pollutants. In this paper, a series of qualitative and quantitative analysis methods such as scanning electron microscopy (SEM) , X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) have been used to study the basic physical and chemical properties and structural features of rice straw burning residues generating at different incineration intensity in field. The results show that: the basic physical and chemical properties of straw burning residues from field were closely associated with the burning intensity. The higher the burning intensity, the lower the TOC content. Meanwhile, the order degree of carbon atoms in the resulting residue increased. Wherein the fatty component of rice straw burning residues is gradually reduced with the burning intensity while the aromaticity of rice straw burning residues is gradually increased. In addition, the organic components in the straw burning residues from field have more significant contribution to the surface area.

Hydrocarbon generation and structural evolution would be occurred in the process of from coal-forming material (i. e. peat sample) transforming to the coal. While Fourier Transform Infrared Spectroscopy (FTIR) have a special advantages in analyzing molecular structure of samples. For understanding the characteristics of hydrocarbon generation and structural evolution of coal-forming material during the process of pyrolysis and microbial degradation, based on the physical simulation experiments of closed pyrolysis and anaerobic microbial degradation, the generation potential of thermogenic gas and biogenic gas were studied in this paper, and characteristics of molecular structure evolution and its mechanism was analyzed by FTIR technology. Results show that cumulative gas yields of hydrocarbon gases (mainly for methane) increased with experiment temperature. The gas yield of non-hydrocarbon gas (mainly for CO2) exhibited two peaks at 250 and 375 degrees C. The degradation ability of anaerobe on coal samples weakened with the maturity increasing and there was no gas generation on the pyrolysis samples with maturity from 1.6% to 1.8%. After pyrolysis, the content of hydroxyl in peat sample decreased first and then increased with the pyrolysis temperature increasing. The content of aldehyde carbonyl, methylene and phosphate reduced. The content of aromatic esters decreased with nonlinear. The bone of S-O in stretching vibration appeared after 350 degrees C and its content increased with temperature. This shows that the sulfocompound restrains the activity of methanogenic bacteria. After degradation by anaerobe, the relative content of hydroxyl, aldehyde carbonyl, aromatic esters, methylene and phosphate in peat sample dropped significantly. It is shown that the intermolecular force between these groups weakened.

Soil phosphorus provides nutrient elements for plants, is one of important parameters for evaluating soil quality. The traditional method for soil total phosphorus content (STPC) measurement is not effective and time-consuming. However, remote sensing (RS) enables us to determine STPC in a fast and efficient way. Studies on the estimation of STPC in near-infrared spectroscopy have been developed by scholars, but model accuracy is still poor due to the low absorption coefficient and unclear absorption peak of soil phosphorus in near-infrared. In order to solve the deficiency which thermal-infrared emissivity estimate desert soil total phosphorus content, and could improve precision of estimation deserts soil total phosphorus. In this paper, characteristics of soil thermal-infrared emissivity are analyzed on the basis of laboratory processing and spectral measurement of deserts soil samples from the eastern Junggar Basin. Furthermore, thermal-infrared emissivity based RS models for STPC estimation are established and accuracy assessed. Results show that: when STPC is higher than 0.200 g x kg(-1), the thermal-infrared emissivity increases with the increase of STPC on the wavelength between 8.00 microm and 13 microm, and the emissivity is more sensitive to STPC on the wavelength between 9.00 and 9.6 microm; the estimate mode based on multiple stepwise regression was could not to estimate deserts soil total phosphorus content from thermal-infrared emissivity because the estimation effects of them were poor. The estimation accuracy of model based on partial least squares regression is higher than the model based on multiple stepwise regression. However, the accuracy of second-order differential estimation model based on partial least square regression is higher than based on multiple stepwise regression; The first differential of continuous remove estimation model based on partial least squares regression is the best model with R2 of correction and verification are up to 0.97 and 0.82 respectively, and RMSE of correction and verification are only 0.0106 and 0.015 7 respectively, RPD is 2.62. Research results provide optimized models for remotely sensed analysis on deserts soil total phosphorus content and could realize timeliness and effective monitoring on the space-time dynamic of deserts soil total phosphorus content for future regional ecological restoration.

Soil is a much complicated substance, because animals, plants and microbes live together, organic and inorganic exist together. So soil contains a large amount of information. The traditional method in laboratory is a time-consuming effort. But the technology of near infrared reflectance spectroscopy (NIRS) has been widely used in many areas, owing to its rapidness, high efficiency, no pollution and low cost, NIRS has become the most important method to detect the composition of soil. This paper mainly introduce some traditional methods in laboratory, the basic processes of soil detection by NIRS, some algorithms for data preprocessing and modeling. Besides, the present paper illustrates the latest research progress and the development of portable near infrared instruments of the soil. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.

In recent years, the area of saline soil in the west of Jilin Province expands increasingly, and soil quality is becoming more and more worsening, which not only caused great damage to the land resources, but also posed a huge threat to agricultural production and ecological environment. We combined with polarized and hyperspectral information to establish the general model and scientifically validated it. The results show that there is a strong relationship between the saline soil hyperspectral polarized information and its physicochemical property parameters, and with regularity. This paper has important theoretical significance for the mechanism of saline soil surface reflection, recognition and classification of saline soil and background, the utilization of soil polarization sensor and the development of quantitative remote sensing.