Search

Knowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas. [ABSTRACT FROM AUTHOR]

Detecting and assessing changes in the hydrologic cycle and its response to a changing environment is essential for maintaining regional ecological security and restoring degraded ecosystems. There is no clear scientific evidence on the effects of human activities and climate variability on runoff and its components in typical arid areas. Therefore, in this study, a heuristic segmentation algorithm, a variable infiltration capacity model (VIC), and remote sensing data to quantify the effects of human activities and climate variability on runoff in the catchment of Lake Ebinur, Xinjiang, China. The results found: (1) The heuristic segmentation algorithm divided the study period into reference period (1964–1985) and two impact periods: I (1986–2000) and II (2001–2017). (2) Cropland and forest land showed an increasing trend, with grassland and barren land accounting for most of the increase. At the same time, the leaf area index (LAI) increased by 0.002 per year during the growing season. (3) Compared with the reference period, runoff depth decreased by 108.80 mm in impact period I due to human activities, but increased by 110.5 mm due to climate variability, resulting in an overall increase in runoff depth of 1.72 mm. Runoff depth increased by 11.10 mm in the impact period II compared to the reference period, with climate variability resulting in an increase of 154.40 mm, but human activities resulted in a decrease of 143.30 mm. Our results shed light on decision-making related to water stress in changing circumstances in arid regions. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective. [ABSTRACT FROM AUTHOR]

Accurate assessment of the spatial distribution and severity of soil salinization has long plagued local governments and researchers in the arid parts of Xinjiang Uygur Autonomous Region (XJUAR). The emergence of machine learning has brought hope to this research field, such as Random Forest (RF) and Stochastic Gradient Treeboost (SGT),however, which are few applications to the quantitative assessment of soil salinization. Therefore, in order to evaluate the accuracy level of the two algorithms for predicting soil salinity, twenty-seven environmental subsets were designed. Each data set is calculated using both RF and SGT to produce an optimal set of variables. The simulation results from 70.37% (19/27) of the subsets showed that the predicted value of soil salinity from SGT is closer to the observed value than is that from RF. The statistics of all datasets showed that the average values of R2 value for RF and SGT were 0.38 and 0.40, the average Root Mean Squared Error (RMSE) value were 28.59 and 27.46, and the Ratio of Prediction to Deviation (RPD) averages were 1.20 and 1.24, respectively. The important dominant factor were topographic variables with coarse resolution, temperature and vegetation indices, land use and landform. [ABSTRACT FROM AUTHOR]

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions. [ABSTRACT FROM AUTHOR]

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas. [ABSTRACT FROM AUTHOR]

Aiming at the remote sensing application has been increasingly relying on ground object spectral characteristics. In order to further research the spectral reflectance characteristics in arid area, this study was performed in the typical delta oasis of Weigan and Kuqa rivers located north of Tarim Basin. Data were collected from geo-targets at multiple sites in various field conditions. The spectra data were collected for different soil types including saline-alkaline soil, silt sandy soil, cotton field, and others; vegetations of Alhagi sparsifolia, Phragmites australis, Tamarix, Halostachys caspica, etc., and water bodies. Next, the data were processed to remove high-frequency noise, and the spectral curves were smoothed with the moving average method. The derivative spectrum was generated after eliminating environmental background noise so that to distinguish the original overlap spectra. After continuum removal of the undesirable absorbance, the spectrum curves were able to highlight features for both optical absorbance and reflectance. The spectrum information of each ground object is essential for fully utilizing the multispectrum data generated by remote sensing, which will need a representative spectral library. In this study using ENVI 4.5 software, a preliminary spectral library of surface features was constructed using the data surveyed in the study area. This library can support remote sensing activities such as feature investigation, vegetation classification, and environmental monitoring in the delta oasis region. Future plan will focus on sharing and standardizing the criteria of professional spectral library and to expand and promote the utilization of the spectral databases. [ABSTRACT FROM AUTHOR]

LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth's resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where data is lacking. The objective of this research was to use the VIC-3L (Variable Infiltration Capacity) based on multi-source remote sensing data to simulate and explore spatio-temporal changes in the LST, to analyze the relationship between the LST and meteorological elements by using cross-wavelet transform (XWT) and wavelet coherence (WTC), the relationship between the LST and LUCC by using three-phase remote sensing images of LUCC. The following results were obtained. The annual average LST of the study area is increasing at a rate of 0.027 °C per year. The annual average LST level is relatively high in the central and eastern regions. The average temperature has an important influence on LST, which is mainly reflected in the period scale of 1~4a in 1963–1972, 1980–1996, and 2004–2010. The sharp decline in open shrubs may have exacerbated the increase in LST in the study area. This study provides a scientific reference for studying LST in arid areas. [ABSTRACT FROM AUTHOR]

Changes in atmospheric particulate matter content directly affect the Earth-atmosphere system balance and climate change and indirectly affect the environment and ecology. Therefore, this matter is receiving increasing attention. A typical arid area with a fragile ecological environment and frequent dust activities, Xinjiang, China, was investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) daily aerosol optical thickness and Ångström wavelength exponent data to obtain the temporal and spatial distribution characteristics of long-term dust aerosols over 20 years. The frequency of occurrence was calculated to accurately identify anthropogenic and natural sources, and data, such as wind speed, were superimposed to quantitatively estimate dust aerosol emissions. The factors that influenced dust aerosol emissions are discussed. The results show that the distribution of dust aerosols in South and North Xinjiang is obviously different, and the average dust aerosol optical depth (DOD) in spring is higher than that in other seasons. The sources of natural dust have little variation and are primarily located in Lop Nur, on the edges of the Taklamakan and Gurbantunggut deserts, in the central Tuha Basin, and in dried-up lakes. The sources of anthropogenic dust are mainly distributed in the farmland areas of the oasis plain and the impact fan plain. Natural dust aerosols are dominant in Xinjiang, with high natural dust emissions in spring. Changes in anthropogenic dust aerosols are caused by production activities, and the annual average anthropogenic dust emissions account for 8% of the total dust emissions. Precipitation can promote a reduction in natural dust emissions. Topography has an obvious effect on the formation and transportation of dust aerosols, which manifests as the frequent occurrence of dust in the basin. The population density data and Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data reveal, that the intensity of human activities has a great impact on anthropogenic dust aerosols. This study provides support for research on how aerosols affect climate and environmental changes in Xinjiang. · Daily dust aerosol optical depth (DOD) data was obtained from 2003 to 2019 · Anthropogenic and natural dust sources of Xinjiang, China, were identified · Dust aerosols emissions in dust sources were quantitatively estimated · The influences of precipitation, topography, and human on dust aerosols were analyzed [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data. [ABSTRACT FROM AUTHOR]

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors. [ABSTRACT FROM AUTHOR]

• The dominant scales of soil salinity variation were 21.77 km and >32 km. • GDVI was the major predictor of soil salinity on salt-affected land. • Accuracy of the predictions with wavelet reconstruction was largely improved. • The predicted performance of WRM was outperform than SMLR. The variation in soil salinity is affected by environmental factors that occur at different scales with varying intensities. It is critical to adequately consider environmental variables under scale effects for digital soil mapping which has been minimally discussed in previous studies. The objectives of this research are to analyze the scale-dependent variability in soil salinity distribution under environmental controlling factors using discrete wavelet transform (DWT) techniques and to compare the differences between the accuracy of soil salinity predictions with and without multiple scale-specific relationships. Thirteen environmental factors related to soil salinity that included influencing environmental factors and indicative environmental factors involving climate, soil, terrain, and vegetation were extracted at 500 m intervals along four transects through farmland and salt-affected land situated at the oasis and oasis-desert ecotones of Xinjiang, China. Each spatial series of soil salinity and environmental variables along the four transects was separated into seven scale components (six details components, namely D1 through D6, and one approximation component, namely A6). A Hilbert transform was used to identify the specific spatial scales of each scale component in the DWT procedure. The results indicate that 21.77 km and >32 km were the dominant scales, which explained approximately 60–80% of the spatial variation of soil salinity throughout the oases. The prediction accuracy with wavelet reconstruction that depended on all the scale components of environmental variables is significantly improved compared with the accuracy of those with the stepwise multiple linear regression method at a single sampling scale. The generalized difference vegetation index (GDVI) was the major predictor of soil salinity on salt-affected land, while evapotranspiration and the terrain ruggedness index (TRI) were the major contributing factors in farmland inside the oases. This study demonstrated that specific scale-dependent relationships can reveal the scale control of soil salinity variation and had the potential to improve the prediction accuracy of soil properties. [ABSTRACT FROM AUTHOR]

Black carbon (BC), which consists of the strongest light-absorbing particles (LAPs) in snow, has been regarded as a potential factor accelerating regional climate change and the melting of snow cover globally. In this study, we used remote sensing (Moderate-resolution Imaging Spectroradiometer, MODIS) observations combined with a snow albedo model (Snow, Ice, and Aerosol Radiation, SNICAR) and a radiative transfer model (Santa Barbara DISORT Atmospheric Radiative Transfer, SBDART) to retrieve the radiative forcing (RF) by BC in snow (R MODIS BC) across Xinjiang, China, for the first time. The observations in January–February show that the concentrations of BC (equivalent BC) in snow ranged from 44.08 to 1949.9 ng g−1, with an average of 536.71 ng g−1. The lowest concentrations of BC were on the border of the Altay region (AR), with a median concentration in snow of 98.5 ng g−1. South of this area in the industrial region (Tianshan Mountain North Slope Economic Development Belt, TMNSEDB), the median concentration of BC in snow was 913.2 ng g−1 R MODIS BC presents distinct spatial variability, with the minimum (3.01W m−2) in the AR and the maximum (40.2W m−2) near industrial areas in TMNSEDB. The regional mean R MODIS BC was 20.43 ± 7.3 W m−2 in Xinjiang, and the average values of the impurity index (I LAPs) and SGS in the region were 0.273 and 241.38 μm, respectively. Moreover, based on the multiple linear regressions, the BC emission intensity values were significantly correlated with I LAPs and RF, and the correlation reached 0.681 and 0.661, respectively; thus, the BC emission could explain above 75% of the spatial variance of BC contents in TMNSEDB, confirming the reasonability of the spatial patterns of retrieved RF MODIS BC in Xinjiang. Additionally, we found that the distribution of R MODIS BC in northern Xinjiang is dominated by I LAPs and BC emissions. We validated R MODIS BC using in situ RF estimates (R site estimate), and the error was 24.05 W m−2; furthermore, the biases in R MODIS BC were negatively correlated with the BC concentrations and ranged from 24.3% to 326% in Xinjiang. • MODIS, observations data and SNICAR is proposed to retrieve RF by BC across Xinjiang, first time. • MODIS derived albedo data is an effective method to RF by BC. • I LAPs and BC emission were very similar to RF in distribution of Xinjiang. [ABSTRACT FROM AUTHOR]