[A Quantitative Analysis of the Reflectance of the Saline Soil under Different Disturbance Extent].

The reflectance of saline soil in the downstream of No.500 reservoir in Fukang, Xinjiang province was investigated. Through filed sampling and spectral test, using the method of spectral transform, correlation analysis and a quantitative analysis were conducted on the salt and water content of the soil under different disturbance degree. A multiple linear regression model was established between the soil reflectance and soil salinity content. The results show that: first, the human disturbance has a significantly positive correlation with the soil content while it has an extremely negative correlation with the water content. The correlation coefficients are 0.961 and -0.929 respectively. Secondly, it shows that those most heavily disturbed soil reflectance is about 10%higher than the slightly disturbed, while the slightly disturbed soil reflectance is about 17% higher than the undisturbed soil. The reason is that the soil surface of barren land with a small amount of vegetation, the biological creature and soil surface crust have been destroyed. The more the disturbance is, the greater chance the surface layer would be destroyed. Meanwhile, the surface layer of soil will be lack of the crust protective; the soil salinity of the bottom rises to the surface associated with the soil moisture will quickly evaporate. The salt is concentrated to the surface layer due to both little precipitation and a lack of protection of soil crust. Thirdly, the peak wavelength location of the spectrum is increased (999, 876~979, 1 182~1 370, 1 900 nm) while the soil is taken from undisturbed to heavily disturbed conditions, which means that with the increase of disturbance, the soil becomes more sensitive in the near infrared region. What's more, the three different prediction models are established though the reflectance R, the reflectivity of the first derivative R', the reflectance R+water. According to the R(2) and the RMSE to comprehensive judge the accuracy of the model. It is found that among those established prediction models of the same soil salinity in the different levels of disturbance, the smaller the degree of human disturbance is, the higher the accuracy of model is. It is found that among all of those established prediction models, the one based on the derivative of R works the best, of which R(2) is larger than 0.983, model accuracy is improved by 5%~10% ,which means that through a derivative transformation, the linear noises in the original spectrum can be removed.

[Response of Human Respiratory Height PM2.5 Variation Characteristics to Meteorological Factors During Winter Haze Days in Beijing].

In order to investigate the influence of meteorological factors on the variation characteristics of PM2.5 in Beijing. According to the survey of PM2.5 mass concentration in height of human respiration, humidity, the direction of the wind, wind speed and temperature. Using the methods of correlation analysis and nonlinear regression analysis, the effects of meteorological factors on the formation and variation of PM2.5 mass concentration in light and moderate air pollution days and heavy pollution were discussed respectively. The results showed that:① On mild to moderate pollution days, if the temperature was low, the daily average wind speed was low, the humidity was high, then the humidity was the decisive influencing factor of PM2.5 mass concentration; if the temperature, wind speed and humidity were all high, then the variation of PM2.5 mass concentration was influenced by the combined action of these three; when the temperature, humidity and wind speed were all low, then the PM2.5 mass concentration was mainly affected by the first two factors. This suggested that changes in the height of the human respiration PM2.5 mass concentrations were extremely sensitive to small changes in meteorological factors; ② During the process of air quality turning from good to heavily polluted, the accumulation of PM2.5 mass concentration was mainly due to the weak air turbulence, coupled with the high humidity, in addition, the northwest wind and northeast wind were larger during the daytime but the duration was shorter, while the southeast and southwest wind speed at night was lower with longer duration, which was conducive to pollutant accumulation;③ Short-term low amount of snow decreased the temperature and increased the air humidity, which not only could not reduce the PM2.5 mass concentration, but rather increased it by 72%, resulting in the jump phenomenon of particle concentration; ④ When the wind speed reached up to 2.0 m·s-1 and lasted for two hours, the local PM2.5 mass concentrations could be reduced to some extent, but it could not completely change the air quality situation. Only when the wind speed was greater than 3.5 m·s-1 and lasted for more than 4 hours, the fine particulate matter in the air could be quickly diffused and the air quality was changed from heavy pollution to excellent.

[Environmental magnetic properties and their spatial variability of topsoil in Shihezi City].

The 80 topsoil samples (0-10 cm) are collected from different land use types in built-up areas, northern new town (including chemical concentration area and concentrated residential areas) and suburban agricultural land at Shihezi oasis city in arid zone of Xinxiang, China. The aim of this study is to analysis the magnetic parameters of concentration, composition and particle size of magnetic properties for the urban topsoils, and to describe the spatial distribution under different circumstances of land use. The magnetic grain parameters show that the soils are dominated with coarser multi domain (MD) ferrimagnetic grain. The magnetic mineralogy parameters suggest that samples are dominated by ferrimagnetic minerals corresponding to magnetite-like minerals, but contain a small amount of anti-ferromagnetic material. From the spatial distribution, the concentration of magnetic minerals are ranked in the order of northern new town > built-up areas > suburban agricultural land. Particle size of magnetic minerals are ranked in the order of northern new town > suburban agricultural land > built-up areas. The high concentration of magnetic parameters areas is coincident with factories' area. However, the magnetic concentration in heavy chemical industry region (N1-N7) are low, and particle size of the magnetic particles is larger. XLF, SIRM and SOFT are effective magnetic parameter indexes indicating the light industrial zone of the study area. While, the discrimination in the heavy chemical industry area needs to combine with a magnetic particle parameters (XFD%) .

[Study of using digital photography to measure soil pH].

Soil saline-alkalization is one of the most important problems of land degradation and the basic environmental problem in arid and semi-arid regions. The digital photography technology can rapidly and timely provide the information about properties, geographical distribution and extent of soil saline-alkalization. For verifying use digital photography assess degrees of sodality promptly and accurately, based on the monitored data of soil pH and measured VIS-NIR reflectance and photographs on given spots, The correlation were analyzed between soil pH and color space model parameters, Partial least squares Regression (PISR) was employed to build predicting model of pH value and the different between two Kinds of data were compared. The results showed that most of parameters with significant correlation While the CIEL * a * b * color model was the best, and it is the best model to assess soil pH (R2 = 0.795, RMSEcv = 0. 084). Prediction set has also seen it was accurate and stability (R2 =0.781, RMSE p = 0.158). The prediction had no significant difference between the digital photography and VIS-NIR reflectance data. The digital image color analysis method showed the potential of being used in soil pH value assessing in the future.

[Study on the difference in canopy spectral reflectance and chlorophyll content of spring wheat at jointing stage in different land].

Taking chlorophyll content, seedling height, blade width and canopy spectral reflectance of spring wheat at jointing stage in different lands as data source, by analyzing the correlations between canopy spectral reflectance and chlorophyll content, making regression analysis for red edge inflection points of canopy spectral reflectance and chlorophyll content of spring wheat, the chlorophyll content monitor models of irrigated and dry land were established respectively. The results showed that there is a significant difference in chlorophyll content of spring wheat, with chlorophyll content of irrigated land much higher. Although there is a good correlation between wheat canopy spectral reflectance and chlorophyll content in the two lands, the correlation of dry spring wheat is lower than irrigated land in visible light and near infrared band. In the visible region, dry spring wheat canopy spectral reflectance is higher, inverse in near-infrared region. Due to high soil moisture, the dry-land spring wheat grows well and there is little difference from irrigated land. The monitor model of red-edge inflection points of canopy spectral reflectance and chlorophyll content of spring wheat at different lands showed that irrigated land wheat is available for linear model, The estimated precision is 94.06%, but dry land is suitable for binomial model, The estimated precision is 97.15%, 10.48% higher than linear model.

[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. Through quantitative analysis, the conclusions can be drawn as follows that the precision values of the different models vary from one to another, the model fitting effects order from high to low is that the integrated model for artificial neural networks (ANNs) is best, single artificial neural networks (ANNs) model is better, while stepwise multiple regression (MLSR) models are worse. Artificial neural networks (ANNs) has the strong abilities of linear and nonlinear approximation, while its integrated model for artificial neural networks (ANNs) is an important way to improve the inversion accuracy of soil organic matter (SOM) content, with the correlation coefficient up to 0.938, root mean square error and total root mean square error are minimum, being 2.13 and 1.404 respectively, and the predictive ability of the soil organic matter (SOM) content are very close to the measured spectrum, so the analysis results can achieve a more practical prediction accuracy for the best fitting model.

[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. In the same scale, there was little accuracy difference between inversion models constructed by 4 wavelet functions low-frequency coefficients, while Bior1.3 was optimal. The best inversion model was ca2 that built by Bior 1.3, with R2 and RMSE being 0.977 and 7.51 mg x kg(-1) respectively, reaching to significant level. Upon testing, it can be used to estimate the alkaline hydrolysis nitrogen content quickly and accurately.

[Contrastive analysis on soil alkalinization predicting models based on measured reflectance and TM image reflectance].

Based on the monitored data of soil pH and measured Vis-NIR reflectance on spot in Qitai oasis alkalinized area in Xinjiang, as well as comparison of the relationship between measured reflectance and soil pH and the relationship between TM reflectance and soil pH, both of the reflectance multivariate linear regression models were built to evaluate soil alkalinization level, and the model accuracy of pH fitting was discussed with error inspection of post-sample. The results showed that there is a significant positive correlation between soil pH and reflectance. With pH rising the reflectance increased concurrently. So the alkalinization soil characterized by hardening had good spectral response characteristics. Both measured reflectance and TM image reflectance had good potential ability for change detection of the alkalinization soil. The pH predicting model of measured reflectance had higher accuracy and the major error was from different hardening state. If building model by TM reflectance directly, the accuracy of fitting was lower because of the vegetation information in image spectrum. With the vegetation factor removed with NDVI, the accuracy of TM predicting model was near the accuracy of measured reflectance predicting model, and both of the model levels were good.

[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. subsp. lessingii(L.)Aellen, Alhagi sparsifolia shap) within given spots in the Northern Slope Area of Tianshan Mountain in Xinjiang, the spectral response characteristics and species recognition of these types of halophyte were analyzed. The results showed that (Alhagi sparsifolia shap) had higher chlorophyll and carotenoid by CARI and SIPI index. (Sophora alopecuroides L. was at a vigorously growing state and had a higher NDVI compared with the other three types of halophyte because of its greater canopy density. But its CARI and SIPI values were lower due to the influence of its flowers. (Sophora alopecuroides L.) and (Camphorosma monspeliaca L. subsp. lessingii(L.)) had stable REPs and BEPs, but REPs and BEPs of (Achnatherum splendens(Trin.)Nevski, Aellen, Alhagi sparsifolia shap) whose spectra red shift and spectra blue shift occurred concurrently obviously changed. There was little difference in spectral curves among the four types of halophyte, so the spectrum mixing phenomenon was severe. (Camphorosma monspeliaca L. subsp. lessingii (L.)Aellen) and (Alhagi sparsifolia shap) could not be separated exactly in a usual R/NIR feature space in remote sensing. Using the stepwise discriminant analysis, five indices were selected to establish the discriminant model, and the model accuracy was discussed using the validated sample group. The total accuracy of the discriminant model was above 92% and (Achnatherum splendens(Trin.)Nevski) and (Camphorosma monspeliaca L. subsp. lessingii(L.)Aellen) could be respectively recognized 100% correctly.