[Temporal and Spatial Distribution Characteristics of Aerosol Optical Properties in Urban Agglomerations on the North Slope of the Tianshan Mountains].

In order to explore the temporal and spatial distribution characteristics of atmospheric aerosol optical depth (AOD) in the urban agglomeration on the northern slope of the Tianshan Mountains, the temporal and spatial distribution characteristics and trends of changes in the AOD in the study area from 2000 to 2019 were analyzed by MODIS aerosol products(MCD19-A2). For 2016-2019, when the AOD was relatively stable, the parameters such as the AOD and Ångström wavelength index (α) were analyzed using multi-band sun photometer ground-based remote sensing technology. The results showed that ① the spatial distribution of AOD in the study area was consistent with the topography, and high values were mainly distributed in the low altitude area. The spatial distribution of AOD in the four seasons showed a strong seasonal change from spring (0.15±0.03) > autumn (0.14±0.03) > summer (0.14±0.02). ② In terms of time, the annual average AOD value of the study area was 0.12 from 2000 to 2019 with an annual growth rate of 1.03%, thereby showing an overall increasing trend. The annual variation in the monthly mean value of AOD was bimodal; the first and second peaks were in May and November. The main reason for the increase in AOD was the release and transmission of dust from natural sources and heating. ③ Under the influence of dust weather, the AOD changed sharply in spring, and the size and change range of aerosol particles were larger than those in summer. The high value of AOD in the study area was mainly affected by coarse mode particles. The moisture absorption growth of fine mode particles caused a fluctuation in the AOD, but it was not the cause of the high value of AOD.

[Aerosol Optical Properties over the Ebinur Region].

The key to understanding the transport and deposition process of salt dust to Ebinur Lake involves the quantitative evaluation of the aerosol concentration and characteristics in Jinghe County. Based on the data of the CE-318 sun photometer station in Jinghe County during 2019, the characteristics of the aerosol optical depth (AOD) and Angström exponent (α) were analysed. The results showed that the daily variation of the AOD in Jinghe County was a single peak curve that increased or decreased monotonously in the early/late peak period and peaked at 12:00-14:00, which was opposite to the trend of the α. There were obvious seasonal differences in the aerosol concentration and dominant mode. The seasonal AOD was ranked as:spring (0.403±0.282) > summer (0.222±0.135) > autumn (0.218±0.112), whereas α was ranked as:summer (1.339±0.446) > autumn (1.116±0.278) > spring (0.914±0.269). During the spring, the range of the change in the AOD was more intense, the aerosol particle size was larger than that during the summer and autumn, and the range of the variation in the particle size was larger. There was a negative correlation between the AOD and α. During the spring and summer, the aerosol particle size varied over a wide range, and the composition was more complex. With the decrease of the α, the AOD tended to increase; during the autumn, the dominant aerosol mode (mainly fine particles) stabilized, and the AOD exhibited no obvious change with the α. From spring to autumn, aerosol gradually transited from a coarse to fine mode. Compared with the summer, local aerosols were more sensitive to the changes of the wind speed, wind direction, and relative humidity during the spring. The primary reason for the increase of the AOD during the spring was the main wind direction and the dust input brought by gale weather. Influenced by the soluble salt ions in the dust, the aerosol particles were able to undergo hygroscopic growth, but this was not the main reason for the high AOD. Temperature was not the internal factor for the change in the local aerosols; however, it was directly proportional to the diffusion ability of aerosol particles. Overall, the AOD of Jinghe County was primarily affected by dust aerosols. The increases in the amounts of small particles and aerosol moisture absorption were not the main reasons for the increase of the AOD in this area.

[Spatiotemporal Evolution and Driving Mechanism of Aerosol Optical Depth in the Ebinur Lake Basin].

Aerosol optical depth (AOD) describes the attenuation of light by aerosols and reflects the degree of regional air pollution to some extent. This study was based on the data from the long-term sequence MOD09A1 from 2000 to 2015 and the generation of a lookup table using the deep blue algorithm (DB) to perform AOD remote sensing estimation on the Landsat TM/ETM+/OLI data from the Ebinur Lake Basin to analyze the temporal and spatial variation characteristics of AOD in the Ebinur Lake Basin and to perform an AOD prediction and factor contribution ranking using the random forest model (RF) combined with environmental variables. The results showed that:① AOD of Ebinur Lake Basin has significant seasonal variation characteristics, and the AOD values were spring (0.414) > summer (0.390) > autumn (0.287), with the largest variation in spring. ② The AOD average of the Ebinur Lake Basin was 0.374, and the interannual variation as a whole showed an upward trend. However, the AOD increased rapidly during 2010-2015, with an interannual increase of 32.32%, which indicated increasing air pollution in the basin over the past 15 years, especially the past five years. ③ The spatial distribution of AOD in the Ebinur Lake Basin was stepped up from the north to the south of Lake Ebinur. In this area, the pollution in Jinghe County was the most prominent, and the AOD value reached 0.483. ④ The RF model had a good predictive effect on AOD, R2=0.866, RMSE=0.042, and evapotranspiration had the most significant effect on AOD in the Ebinur Lake basin.

[Study on Estimation of Deserts Soil Total Phosphorus Content by Vis-NIR Spectra with Variable Selection].

In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filte- ring with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500-700, 1 101-1 300, 1 501-1 700, and 1 901-2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1621, 546, 1259, 573, 1572, 1527, 564, 1 186, 1 988, 1541, 2024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg x g(-1), and the related coefficient for cross validation (R(c)) and the related coefficient for prediction (R(p)) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of AGO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.

[Soil Salinity Modelling Study with Salinity Inference Model Based on the Integration of Soil and Vegetation Spectrum in Arid Land].

Only using soil spectrum to model soil salinity is not enough to meet the actual demands because of the complicated soil context. As a remotely sensed indicator, the vegetation type and its growing condition can provide a spatial overview of salinity distribution. Based on the synergistic relationship between soil salinity and vegetation in arid land, this paper tries to combine the spectrum of soil and vegetation to quantitatively estimate the salt content with the help of the concept of two-dimensional feature space. After the analysis of scatter diagram, the soil salinity detecting model was constructed to improve reasoning precision. However, because the impact of soil reflectance on the quantification of vegetation parameters under the individual pixel, the Normalized Difference Vegetation Index (NDVI) was difficult to accurately obtain sparse vegetation cover in arid areas. Therefore, in order to avoid the limitations of NDVI, the Combined Vegetation Indicative Factor(CVIF)was created and supported by Linear Spectral Unmixing Model (LSUM). Then, the study constructed the feature space based on the CVIF and salinity index (SI) and analyzed the response relationship between soil salinity and the trend of scattered points. Finally, a new and operational model termed Salinity Inference Model (SID) was developed. The results showed that the CVIF (R2>0.84, RMSE=3.92) performed better than NDVI(R2>0.66, RMSE=13.77), which means the CVIF was more appropriate for analyzing variations in vegetation cover (particularly halophytes) than NDVI in the study area. The SID was then compared to the Combined Cpectral Response Index (COSRI)(NDVI-based) from field measurements with respect to the soil salt content. The results indicated that the SID values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SID (R2>0.86, RMSE<6.86) compared to COSRI (R2=0.71, RMSE=16.21). These results suggested that the feature space related to biophysical properties combined with CVIF and SI can effectively provide information on soil salinity.

[EEMD de-noising of reflecting spectrum in soil profiles].

In the present paper, based on the multi-resolution attribute of EEMD (ensemble empirical mode decomposition) method, we presented a new de-noising method for analyzing spectrum, and applied it to process the reflecting spectrum data of 33 soil profiles in the typical oasis located in the middle reaches of the Tarim River. To explore the de-noising effect of EEMD threshold method for reflecting spectrum in soil profiles; we compared EEMD threshold method with wavelet transform method. The results showed that compared with traditional wavelet transform method, the signal to noise ratio (SNR) was improved from 14. 8366 to 34. 2757 dB, and the root mean square error (RMSE) was reduced to 7. 2406 X 10(-6) from 6. 7861 X 10(-5) and the correlation coefficient (r) increased from 0. 9825 to 0. 9998. Therefore, three de-noising effect indicators of EEMD threshold method are better than those of wavelet transform method. This proved that the EEMD threshold method can effectively eliminate the noise of soil-profile spectrum and also preserve the detailed information of the original spectra well. Thus, the analysis precision of the spectrum will be improved. In addition, by contrast with the wavelet threshold method, the EEMD threshold method is adaptive and is fairly reliable. As a new method for spectral pretreatment, the EEMD threshold method will have a good application prospect in spectra de-noising.

The influence of natural and human factors in the shrinking of the Ebinur Lake, Xinjiang, China, during the 1972-2013 period.

The Ebinur Lake is a closed inland lake located within the arid region of the Xinjiang Autonomous Region in the northwestern part of China, near the Kazakhstan border. The shrinkage of the lake area is believed to be caused by ecological environmental deterioration and has become an important restraining factor for the social development of the local population. Of all the lakes in the Xinjiang Autonomous Region, the Ebinur Lake is the most severely impacted water body. The lake has undergone change in size naturally for over thousands of years due to natural causes. However, the authors observed the dramatic changes in the freshwater resources of this region from the aerial images from 1972 to 2013. Thus, this paper traces and analyzes the change in the Ebinur Lake surface area in the past 41 years. A set of six satellite images acquired between 1972 and 2013 was employed to map the change in the surface area of the Ebinur Lake using the water index approach. The authors applied the traditional normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) to quantify the change in the water body area of the Ebinur Lake during the study period. The results indicate that the lake area has experienced a dramatic decrease of 31.4% from 1972 to 2013. The paper also examines the natural processes and human activities that may have contributed to the decrease in the lake area. The results show that the decrease in total lake area appears to coincide with periods of rapid land reclamation in the study area. Moreover, the uncontrolled land reclamation activities, such as irrigation, can increase the sedimentation in the Ebinur Lake thereby reducing the lake size. Reduction of the lake area has a negative ecological impact on the environment and on human life and property. The lake area is the most important factor to ensure the environment of the watershed and the key index to measure the environment balance.

[Study on the soil salinization monitoring based on measured hyperspectral and HSI data].

The present paper selects the Kuqa Oasis as the study area, studies spectrum characteristics of soil salinity, and establishes soil spectrum library. Through transforming and analyzing varying degrees of soil salinization reflectance spectra data in the typical study area, and selecting the most sensitive spectral bands in response to salinization, we established the measured hyperspectral soil salinity monitoring model, and by correcting the soil salinity monitoring model established by HIS image through scale effect conversion improved the model accuracy under the conditions of a regional-scale monitoring of soil salinization. The results show that both measured hyperspectral soil salinity monitoring model and HSI image soil salinity inversion model have good accuracy, model determination coefficient (R2) is higher than 0.57 and the model stability is better. Compared with the corrected HSI image soil salinity inversion model and uncorrected HSI image soil salinity inversion model, the coefficient of determination has been greatly improved, which increased from 0.571 to 0.681, and through the 0.01 significance level, the root mean square error (RMSE) value is 0.277. The correction HIS image soil salinization monitoring model can better improve the model accuracy under the condition of regional scale soil salinization monitoring, and using this method to carry out the soil salinization quantitative remote sensing monitoring is feasible, and also can provide scientific reference for future research.

[Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data].

In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization.

[Monitoring of soil salinization in Northern Tarim Basin, Xinjiang of China in dry and wet seasons based on remote sensing].

Soil salinization is one of the most important eco-environment problems in arid area, which can not only induce land degradation, inhibit vegetation growth, but also impede regional agricultural production. To accurately and quickly obtain the information of regional saline soils by using remote sensing data is critical to monitor soil salinization and prevent its further development. Taking the Weigan-Kuqa River Delta Oasis in the northern Tarim River Basin of Xinjiang as test object, and based on the remote sensing data from Landsat-TM images of April 15, 2011 and September 22, 2011, in combining with the measured data from field survey, this paper extracted the characteristic variables modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), and the third principal component from K-L transformation (K-L-3). The decision tree method was adopted to establish the extraction models of soil salinization in the two key seasons (dry and wet seasons) of the study area, and the classification maps of soil salinization in the two seasons were drawn. The results showed that the decision tree method had a higher discrimination precision, being 87.2% in dry season and 85.3% in wet season, which was able to be used for effectively monitoring the dynamics of soil salinization and its spatial distribution, and to provide scientific basis for the comprehensive management of saline soils in arid area and the rational utilization of oasis land resources.

[Study on the soil salinization monitoring based on synthetical hyperspectral index].

The present paper selected the spectral reflectivity of saline soil and vegetation of Weigan-Kuqa River Delta Oasis in the northern margin of the Tarim Basin in Xinjiang as objects, and used various spectral transforms to process the data with continum removed methods, derivate spectra, reciprocal, first order differential and root mean square etc, then analyzed the spectrum features and decided the most sensitive band ranges most relevant to salinization, and used field hyperspectral vegetation index, soil salinity index and measured synthetical spectral index to respectively establish hyperspectral quantitative models which could evaluate the soil salinization degrees. By comparing various spectral transformations of hyperspectral data the result showed that the first derivative of measured soil and vegetation hyperspectral were most sensitive to soil salinization degrees. The hyperspectral quantitative model based on measured synthetical spectral index could monitor soil salinization accurately and was better than the models simply based on vegetation index or soil salinity index. The research provided some scientific basis with soil salinization detection.

[Simulation of image multi-spectrum using field measured endmember spectrum].

The characteristic of landscape spectrum is the basic of application of remote sensing and plays an important role in quantitative analysis of remote sensing. However, in spectrum-based application of remote sensing, because the difference of measuring scale and instrument resolution yield serious error in spectral curve and reflectance for the same landscape, there exists difficulty in quantitative retrieval of special information extraction of remote sensing. Firstly, the imaging simulation principles of the optics image was described and proposed A method using field measured endmember spectrum with higher spectrum resolutions to simulate spectrum of Multi-spectrum images with lower spectrum resolution was proposed. In the present paper, the authors take the delta oasis of Weigan and Kuqa rivers ocated in the North of Tarim Basin as study area, and choose vegetation and soil as study object. At first, we accomplished the simulation from field measured endmember for multi-spectrum by using the spectral response function of AVNIR-2, and found the large correlation between simulated multi-spectrum and pixel spectrum of AVNIR-2 by using the statistical analyse. Finally, the authors set up the linear model to accomplish the quantitative transformation from edmember scale to pixel scale. The result of this study has the realistic meaning for the quantitative application of remote sensing.

[Strategic environmental assessment on land use programming of Shaya County, Xinjiang Uygur Automous Region].

With the establishment and implement of national and regional land use programming, to approach the technology and methodology of environmental assessment appropriate for the overall land use programming is of great significance. By using the theories of strategic environmental assessment and taking ecosystem services value as an evaluation index, a comprehensive assessment on the potential eco-environmental effects of 1996-2010 land use programming of Shaya County in Xinjiang Uygur Automous Region was made. The results showed that from 1996 to 2010, the total ecosystem services value of the County increased from 69.33 x 10(8) Yuan to 70.81 x 10(8) Yuan, with an annual increment of 0.15%, which suggested that this programming was basically reasonable. However, the common land expansion should be controlled strictly. The increase rate of eco-value was higher than that of GDP, indicating that Shaya County was of eco-value gaining. There were still some shortages in the programming; e.g., the area ratio of unutilized land (desert) would be 83.95% in 2010, and thus, the programming should be emended to increase the eco-benefit of land use.

[Spectral data analysis of salinity soils with ground objects in the delta oasis of Weigan and Kuqa Rivers].

The characteristic of landmark spectrum is not only the physical base of remote sensing technical application but also the base of the quantificational analysis of remote sensing, and the study of landmark spectrum is the main content in the basic research of remote sensing. In the present paper, the authors adopted CI700 portable spectrum apparatus made in American CID Company, and investigated or examined some spots in the delta oasis of Weigan and Kuqa rivers located in the north of Tarim Basin considered as the typical area, based on a great deal of spectral data for different kinds of geo-targets, and the spectral features and changing law of saline-alkaline ground, silver sand ground, dune, cotton ground etc. Alhagi sparsifolia., Phragmites australis, Tamarix, Halostachys caspica etc. were analyzed. According to the actual conditions, we analyzed the data noise characteristic of the spectrum and got rid of the noise. Meanwhile, derivative spectrum technology was used to remove the environmental background influence. Finally, in order to take full advantage of multi-spectrum data, ground information is absolutely necessary, and it is important to build a representative spectral library. The ENVI software was used to build the spectral library of surface features by field survey of the delta oasis of Weigan and Kuqa Rivers, Xinjiang Uygur Autonomous Region. This library can be used for features investigation, vegetation surveys, vegetation classification and environmental monitoring in the delta oasis of Weigan and Kuqa Rivers by remote sensing. The result of this research will be significant to the research on the saline-alkali soil in the arid area.