Soil moisture (SM) plays a vital role in regulating the water and energy exchange between land surfaces and the atmosphere and is declared an essential climate variable by the Global Climate Observing System (GCOS). Vegetation optical depth (VOD) is a crucial parameter describing vegetation attenuation properties in microwave radiative transfer equation, and it has been proven to be a promising ecological indicator for studying plant hydraulics, carbon stocks, and vegetation phenology. A long-term SM and polarization-, frequency-dependent VODs (C/X/Ku) product was derived from the inter-calibrated AMSR-E/2 multi-frequency brightness temperature, using the multi-channel collaborative algorithm (MCCA). The MCCA comprehensively considers the physical relationship between multiple microwave channels and could simultaneously retrieve frequency- and polarization-dependent VODs and SM. The new MCCA AMSR-E/2 SM dataset was validated over 25 dense soil moisture networks from the International Soil Moisture Network (ISMN) and United States Department of Agriculture (USDA) watersheds. The results showed that MCCA performs best in terms of ubRMSE among the current publicly available SM datasets related to AMSR-E/2. In addition, polarization-, frequency-dependent VODs from MCCA may provide new insights for better understanding the water fluxes in plant physiology.
HU Lu, ZHAO Tianjie, JU Weimin , PENG Zhiqing , YAO Panpan, SHI Jiancheng
This dataset is global respiration data, including autotrophic respiration (ra) and heterotrophic respiration (rh). It is simulated by TaiESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under historical scenarios. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 0.9 ° x1.25 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.AS -RCEC.TaiESM1.historical。
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
The three pole soil microbial post-treatment products in typical years collected the distribution and analysis results of soil samples from the north and south polar regions from 2005 to 2006 and the distribution and analysis results of soil samples from the Qinghai Tibet Plateau in 2015. Through sorting and summarizing, the post-processing data products of soil microorganisms in the three pole region are obtained. The data format is excel, which is convenient for users to view. Among them, the collection time of samples from the north and south polar regions was from December 13, 2005 to December 8, 2006, including 52 samples from three regions in the Arctic (Spitsbergen slijeringa, Spitsbergen vestpynten, and Alexandra fjord Highlands), and 171 samples from five regions in the Antarctic (Mitchell Peninsula, Casey station main power house, Robinson ridge, herring Island, browning Peninsula); The Qinghai Tibet Plateau was collected from July 1 to July 15, 2015, including meadow, grassland and desert ecosystems. There were 18 sampling points in total, and the number of samples at each sampling point was 3-5. The precipitation, air temperature and drought degree of the sampling point are estimated from the meteorological information for reference. The soil surface samples were collected and stored in liquid nitrogen, then transported back to the Sydney Laboratory for extraction by fastprep DNA kit. The extracted DNA samples were amplified with the 16S rRNA gene fragment using 27F (5'-gagttttgatcntggctca-3') and 519r (5'-gtnttacngcgckctg-3'). The amplified fragments were sequenced by 454 method, and the original data were analyzed by mothur software. The sequences with poor sequencing quality were first removed, and then the chimeric sequences were sequenced and removed. After that, the similarity between sequences is calculated. Sequences with a similarity of more than 97% are clustered into one OTU, and OTU representative sequences are defined. The OTU representative sequences were aligned with the Silva database, and were identified to belong to the first level when the reliability was greater than 80%.
YE Aizhong
The survey area covers Luding, Kangding, Yajiang, Litang, Batang and other areas in Sichuan Province. The crops involved include highland barley, wheat, corn, potato and tomato and other open vegetables. The dry Leak Bucket method was used to extract 171 small and medium-sized soil animal samples, and more than 800 soil animals were captured. The samples were stored in the Chengdu Institute of biology, Chinese Academy of Sciences. After collection, the samples were identified by means of a body microscope. Among them, the 0-15cm soil layer in Batang area, Sichuan Province was the largest, and 208 small and medium-sized soil animals were identified; The second is the 0-15 cm soil layer in Kangding, Sichuan Province, where 130 small and medium-sized soil animals were observed.
SUN Xiaoming
Monthly data of 7cm soil moisture in the surface layer of China. The time range includes the historical period 1850-2014 and the future period 2015-2100 (the future period includes four different shared socio-economic paths: ssp1-2.6, ssp2-4.5, ssp3-7.0 and ssp5-8.5). The spatial resolution is 0.25 °. This data is based on the deep learning method, taking the 7cm surface soil moisture data of era5 land as a reference, and integrating the surface soil moisture data of 25 scaled down cmip6 models. In the context of climate change, data can be used for drought and vegetation correlation analysis.
FENG Donghan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from January 1 to October 9 in 2021. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from January 1 to October 13, 2021. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to October 13 in 2021. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018/8/31 10:30.
Li Xiaoyan
This dataset provide the daily observations of soil water contents and soil temperature in the Qihuli catchment in the upper reach of Qihai Lake basin. Daily soil water content and soil temperature were measured in the shady slope, sunny slope and the outfall of this catchment in the period of 2019-2021. The Qihuli catchment is located at 37°25′N and 100°15′E, with the elevation ranging from 3565-3716. The soil water content and soil temperature were continuously monitored using the ECH2O and 5TE sensors at both shady and sunny slopes. The monitoring depths are 10 cm, 30 cm, 50 cm, 80 cm, 110 cm, and 10 cm, 30 cm, 60 cm, 90 cm, and 120 cm at shady and sunny slope sites, respectively. The soil water content and soil temperature were monitored continuously using the Trime and PICO32 sensors, which were installed at ten soil depths, including 5 cm, 10 cm, 20 cm, 40 cm, 80 cm, 100 cm, 120 cm, 140 cm, 160 cm and 180 cm. This dataset can support the long-term investigation of ecohydrological processes in typical catchment and also support the validation of hydrology models.
Li Xiaoyan
1) Data content: CT scan dataset of vegetation-soil-rock three-dimensional spatial structure of typical watersheds in Qilian Mountains, the data includes the volume density of moss layers at different depths, soil macroporosity and soil gravel volume density data in typical watersheds of Qilian Mountains; 2) Data Source and processing method: The moss layer and the undisturbed soil column with a depth of 30 cm under the moss cover were collected in a typical small watershed of the Qilian Mountains, and the moss layer and the undisturbed soil column were scanned with an industrial X-ray three-dimensional microscope; 3) Data quality description: The resolution of moss layer is 40 μm, and the resolution of undisturbed soil column is 68 μm; 4) Data application results and prospects: CT scan data set of vegetation-soil-rock three-dimensional spatial structure of typical small watersheds in Qilian Mountains is suitable for ecological restoration, water resources management and utilization in Qilian Mountains. It is of great significance and can provide basic data and theoretical support for elaborating the water conservation function and mechanism of the Qilian Mountains.
HU Xia
This dataset is a raster dataset of annual rainfall erosivity on the Qinghai-Tibet Plateau from 1960 to 2019. The rainfall erosivity was calculated using the daily rainfall data of 129 stations in the Qinghai-Tibet Plateau and its surrounding 150km range, of which 74 stations were located inside the Qinghai-Tibet Plateau and 55 stations were located outside. The calculation method is consistent with the algorithm of the first national Water Resources Inventory, using WGS_ 1984 coordinate system and Albers projection (central meridian 105°E, standard parallels 25°N and 47°N), and then Kriging interpolation is carried out year by year to generate grid map with spatial resolution of 250m. Rainfall erosivity is the main dynamic factor of soil erosion, and it is also the basic factor calculated by models such as CSLE and RUSLE. The integrated daily rainfall data of long-time series has high data accuracy, which improves the accuracy of rainfall erosivity estimation, and also helpful to further accurately estimate the amount of soil erosion on the Qinghai Tibet Plateau.
ZHANG Wenbo
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2021. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include the MUltiscale Satellite remotE Sensing (MUSES) LAI/FVC product, the daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST-TP;) and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
Based on the "second Qinghai Tibet Plateau comprehensive scientific investigation" and "China's soil series investigation and compilation of China's soil series" "The obtained soil survey profile data, using predictive Digital Soil Mapping paradigm, using geographic information and remote sensing technology for fine description and spatial analysis of the soil forming environment, developed adaptive depth function fitting methods, and integrated advanced ensemble machine learning methods to generate a series of soil attributes (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, gravel content (>2mm) in the Qinghai Tibet plateau region." , sand, silt, clay, soil texture type, unit weight, soil thickness, etc.) and quantify the spatial distribution of uncertainty. Compared with the existing soil maps, it better represents the spatial variation characteristics of soil properties in the Qinghai Tibet Plateau. The data set can provide soil information support for the study of soil, ecology, hydrology, environment, climate, biology, etc. in the Qinghai Tibet Plateau.
LIU Feng, ZHANG Ganlin
The dataset includes the measured soil thickness data at 148 points in the Yarlung Zangbo River Basin, as well as the physical properties and hydraulic characteristics (such as particle size, saturated water content, organic matter content, saturated hydraulic conductivity, etc.) of soil samples at 40 points. The sampling points are distributed from Zhongba County in the upper reaches of the Yarlung Zangbo River basin to Nyingchi city in the lower reaches. The soil thickness data is obtained through the excavation profile measurement, and other soil data are obtained from the collected ring knife samples according to the standardized experimental process, so the data accuracy is high. The soil data of the Yarlung Zangbo River basin provided by this dataset can provide a reference for large-scale soil mapping on the Qinghai Tibet Plateau and improve the prediction accuracy of relevant studies.
LIU Jintao
This dataset provides global soil texture data optimized by remote sensing estimation of wilting coefficient, with a spatial resolution of 0.25 degree. The dataset incorporates remote sensing-based (e.g., SMAP satellite) estimation of soil wilting point and uses the SCE-UA algorithm to optimize two prevalently used soil texture datasets (i.e., GSDE (Shangguan et al. 2014) and HWSD (Fischer et al., 2008)). Comparison results with in-situ observations (44 stations in North America) show that, the soil moisture and evaporative fraction simulation from the Noah-MP land surface model by using the optimized soil texture have been significantly improved.
HE Qing , LU Hui, ZHOU Jianhong , YANG Kun, YANG Kun, 阳坤, YANG Kun, SHI Jiancheng
1) Soil environmental quality data of typical industrial parks in Huangshui basin of Qinghai Province provide basic support for soil pollution control caused by regional industrial activities; 2) The data source is the soil samples of typical areas in Huangshui River Basin. After collection, the samples are quickly stored in the refrigerator at - 4 ℃ and sent to the laboratory as soon as possible. After pretreatment, the relevant parameters are tested; 3) The process of sample collection and transportation meets the specifications, and the experimental detection process strictly follows the relevant standards. Due to the changes of various factors of soil environment, the results are only aimed at the investigation results; 4) The data can be used to analyze regional soil pollution and heavy metal risk assessment;
WANG Lingqing
We developed a 1-km resolution long-term soil moisture dataset of China derived through machine learning trained with in-situ measurements of 1,648 stations, named as SMCI1.0 (Soil moisture of China based on In-situ data, Li et al, 2022). SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000-2020. Random Forest is used to predict soil moisture using ERA5-land time series, leaf area index, land cover type, topography and soil properties as covariates. Using in-situ soil moisture as the benchmark (The data comes from China Meteorological Administration), two independent experiments are conducted to investigate the estimation accuracy of the SMCI1.0: year-to-year experiment (ubRMSE ranges from 0.041-0.052 and R ranges from 0.883-0.919) and station-to-station experiment (ubRMSE ranges from 0.045-0.051 and R ranges from 0.866-0.893). As SMCI1.0 is based on in-situ data, it can be useful complements of existing model-based and satellite-based datasets for various hydrological, meteorological, and ecological analyses and modeling, especially for those applications requiring high resolution SM maps. Please read the readme file for more details. We provided two versions with different resolution, i.e., 30 arc seconds (~1km) and 0.1 degree (~9km).
SHANGGUAN Wei, LI Qingliang , SHI Gaosong
The soil temperature and moisture observation network is located south of Tibetan Plateau, with an average elevation of 4,486 meters, providing soil moisture, soil temperature and freeze-thaw measured datasets. Data content (data file, table name, and observation indicators included) : (1) Number of sites: 25 observation sites (2) observation variables: (soil moisture and soil temperature) (3) Observation depths: (0-5, 10, 20 and 40 cm) (4) Geographic coverage: 27.7°-28.1°N； 89.1°-89.4°E (5) Spatial resolution: passive microwave satellite pixel (0.3°) (6) Temporal resolution: 30 min resolution (7) Soil moisture measurement accuracy and resolution: ± 2% VWC and 0.1% VWC. Data content field description: (1) Variable 1-6: Date (Integer: yyyy-mm-dd-hh-mm-ss; UTC+8) (2) Variable 7-34: Observational data values at each site (real, missing value: -99.00) (3) Soil moisture(SM): %vol（m³/m³) (4) Soil temperature(ST): ℃ Data correction and quality control: The 30 min resolution temperature data are the direct sampling data after quality control, and the soil moisture volume content is the correction value based on the soil moisture measurement by the drying method.
YANG Kun, YANG Kun, 阳坤, YANG Kun, CHEN Yingying, ZHAO Long , QIN Jun , LA Zhu , ZHOU Xu, JIANG Yaozhi , TIAN Jiaxin
This data includes the soil carbon and nitrogen content at 0-10cm, 10-20cm and 20-30cm soil depths of 52 sample points in the west of Qinghai Tibet Plateau. The soil samples were obtained by the research team through soil drilling from 2019 to 2020. After the soil was screened with 2mm aperture, it was air dried and fine roots were removed, and then measured by carbon and nitrogen analyzer in the laboratory. This data can provide a theoretical basis for the study of soil carbon and nitrogen processes at different depths in the western Qinghai Tibet Plateau under the scenario of global climate change in the future, and provide data support for the model to simulate the process of soil carbon and nitrogen cycle, which is conducive to a deeper understanding of the process of soil carbon and nitrogen cycle in the western Qinghai Tibet Plateau.
DING Jinzhi
The erosivity calculation of rainfall and snow melt runoff based on the revised universal soil loss equation (RUSLE) is improved by comprehensively using the observed sediment transport, meteorological and remote sensing data. Based on the improved RUSLE model, the soil erosion rate of the Mid-Yarlung Tsangpo River Region is calculated, and the spatial distribution of multi-year average rainfall runoff erosivity factor, soil erodibility factor, slope length and slope steepness factor, vcover management factor, ssupport practice factor and soil erosion rate are obtained. The data set analyzes the phenomena of "less water and more sediment" in Nianchu River Basin and "more water and less sediment" in Lhasa River Basin, which can provide theoretical support for regional soil and water conservation.
WANG Li , ZHANG Fan
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
