Timely and correct observation of the spatial and temporal patterns and dynamics of oases is important for the property socioeconomic development of arid zones. During this study, a complete of 9 periods of Landsat image knowledge in 1986, 1990, 1995, 2000, 2005, 2010, 2015, 2018, and 2020 were accustomed get oasis distribution knowledge within the Hexi region from 1986 to 2020 employing a combination of OSTU threshold methodology and manual visual interpretation, and combined with high-resolution Google Earth pictures and field validation knowledge were combined to ascertain random sample points supported confusion matrix to verify the accuracy of oasis extraction results. The overall accuracy of oasis data in Hexi Corridor is over 94%, and the Kappa coefficient is over 0.88. This dataset can provide data support for the ecological environment protection of Hexi oasis.
XIE Yaowen, ZHANG Xueyuan, LIU Yiyang, HUANG Xiaojun, LI Ruyan, ZONG Leli, XIAO Min, QIN Mengyao
PML-V2(China) terrestrial evapotranspiration and total primary productivity dataset, including five elements: gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall (Ei) and water body, ice and snow evaporation (ET_water). The dataset has a spatial-temporal resolution of daily and 500-meter from Feb.26, 2000 to Dec.31, 2020. Compared with the global version, the simulation accuracy of the PML-V2(China) product has been greatly improved, which has the following improvements and innovations: I. Compared with the 8-day temporal resolution of the global version, the new product is daily; II. The observation data are from 26 eddy flux stations in China, which are with 9 underlying surface types including deserts with sparse vegetation, and used for the parameter calibration of the model (there are only eight stations located in China for calibrating the global PML-V2, covering only five vegetation types); III. China meteorological forcing dataset for 2000-2018 and the global land surface data assimilation system GLDAS-2.1 meteorological dataset with bias correction for 2019-2020 are used to replace the original 0.25 ° GLDAS meteorological inputs; IV. The ERA5-Land as the input surface temperature instead of air temperature to calculate the outgoing longwave radiation; and V. Taking the MODIS leaf area index by the improved Whittaker filter as the model input, the new product provides new insights into monitoring crop water consumption and reveals the characteristics of the planting system. Please note that the dataset does not include the South China Sea.
ZHANG Yongqiang, HE Shaoyang
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
The data source of this data set is the European Space Agency (ESA) multispectral satellite Sentinel-2. It includes the annual mean data of CDOM and DOC of Qinghai Tibet Plateau lakes in 2017. Method of use: Based on the CDOM data of the measured sample points, the image reflectance information is extracted, the best prediction variable is selected through Pearson correlation analysis, and a multiple stepwise regression CDOM prediction model is constructed to obtain the CDOM results of the Qinghai Tibet Plateau water body. Because CDOM has a good correlation with DOC, DOC prediction results are calculated by CDOM. Adjustment R of the CDOM model of the final Qinghai Tibet Plateau ² Up to 0.81.
SONG Kaishan
Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
China cloud-removal snow albedo product data set is raster data with a geospatial extent of 72 - 142E, 16 - 56N, using an equal latitude and longitude projection and a spatial resolution of 0.005°. The dataset covers the period from 1 January 2000 to 31 December 2020 with a temporal resolution of 1 day. The data contains six elements: black sky albedo (Black_Sky_Albedo), white sky albedo (White_Sky_Albedo), solar zenith angle (Solar_Zenith_Angle), pixel-level cloud label (Cloud_Mask), pixel-level forest pixel (Forest_Mask) and pixel-level retrieval label (Abnormal_Mask). Black_Sky_Albedo records the black sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. White_Sky_Albedo records the white sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. Cloud_Mask records whether the pixel is cloud type, with a value of 0 indicating non-cloud and 1 indicating cloud. Forest_Mask records whether the pixel has been corrected as a forest type, with a value of 0 indicating that it has not been corrected and 1 indicating that it has been corrected. Abnormal_Mask records whether the retrieval of the black sky albedo and white sky albedo of the pixel is an anomaly of less than 0 or greater than 10000, with a value of 0 indicating a non-anomaly and 1 indicating an anomaly. ChinaSA was retrieved based on the MODIS land surface reflectance product MOD09GA, the snow cover product MOD10A1/MYD10A1 and the global digital elevation model SRTM. The snow albedo retrieval model was developed based on the ART model and produced using the GEE and local side interactions.
XIAO Pengfeng , HU Rui , ZHANG Zheng , QIN Shen
This phenological data is based on the MOD13A2 data of the Qinghai Tibet Plateau from 2000 to 2015 (with a temporal resolution of 16 days and a spatial resolution of 1km). The NDVI curve is fitted using the segmented Gaussian function in the TIMESAT software. The spring phenology, autumn phenology and the length of the growth season are extracted using the dynamic threshold method. The thresholds of spring phenology and autumn phenology are set to 0.2 and 0.7 respectively. The phenological data were masked. Among them, the mask rules are: 1) The maximum value of NDVI must be met between June and September; 2) The average value of NDVI from June to September shall not be less than 0.2; 3) The average NDVI in winter shall not exceed 0.3.
ZU Jiaxing , ZHANG Yangjian
This dataset is the growing season NDVI and vegetation phenology dataset of the Tibetan Plateau during during the past 20 years (2001-2020). The data source is MODIS (MOD13A2, Collection 6) products, and the spatial resolution is 1km. The dataset includes: the average NDVI during the growing season (May-September), the start date of the growing season (SOS), the end date of the growing season (EOS) and the duration of the growing season (DOS) for each year from 2001 to 2020. Two methods were used to extract vegetation phenology: dynamic threshold approach and double logistic function method. The data format is TIFF and the projection is Sphere_ ARC_ INFO_ Lambert_ Azimuthal_ Equal_ Area.
WANG Taihua, YANG Dawen
NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters to reflect the crop growth and nutrient information. According to this parameter, the N demand of crops in different seasons can be known, which is an important guide to the reasonable application of N fertilizer. Correct NDVI (C-NDVI) is the value of NDVI after excluding the influence of climate elements (temperature, precipitation, etc.) on NDVI. Taking precipitation as an example, studies on the lag effect of precipitation on vegetation growth show that the lag time of precipitation effects varies in different regions due to differences in vegetation composition and soil types. In this study, we post-processed the MODIS NDVI data and firstly correlated the NDVI value of the current month with the precipitation of the current month, the average value of the precipitation of the current month with that of the previous month, and the average value of the precipitation of the current month with that of the previous two months to determine the optimal lag time. The NDVI was regressed on precipitation and air temperature to obtain the correlation coefficients, and then the corrected NDVI values were calculated by the difference between the MODIS NDVI and the NDVI regressed on climate factors. We corrected NDVI using climate data to give reliable vegetation correction indices for the circum-Arctic Circle (range north of 66°N) and the Tibetan Plateau (range 26°N to 39.85°N and 73.45°E to 104.65°E) for 2013 and 2018. The spatial resolution of the data is 0.5 degrees and the temporal resolution is monthly values.
YE Aizhong
The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation at various scales. Evi is similar to the normalized difference vegetation index (NDVI) and can be used to quantify vegetation greenness. However, evi corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. It contains an "L" value to adjust the canopy background, a "C" value as the atmospheric drag coefficient, and a value from the blue band (b). These enhancements allow the ratio between R and NIR values to be calculated exponentially while reducing background noise, atmospheric noise and saturation in most cases. This research work mainly focuses on post-processing NDVI and evi data, and gives a more reliable vegetation situation of the Qinghai Tibet Plateau in 2013 and 2018 through transformation of projection coordinate system, data fusion, maximum value synthesis method, elimination of outliers and clipping. The spatial resolution of the data is 0.05 °, and the temporal resolution is month.
YE Aizhong
Terrestrial actual evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. This global ET dataset is obtained based on ETMonitor model, which combines parameterizations for different processes and land cover types, with multi-source satellite data as input. Several open accessed remote sensing variables, e.g., LAI, FVC, albedo, surface soil moisture, dynamic surface water cover and snow/ice cover, were used as input to estimate daily ET. The meteorological variables from ERA5 reanalysis dataset were also adopted. The ETMonitor model is applied at daily scale to estimate the ET components at 1-km resolution, including vegetation transpiration, soil evaporation, canopy precipitation interception loss, water surface evaporation and snow/ice sublimation on daily step, and the total actual ET is estimated as the sum of these components. Overall, the actual ET estimated by ETMonitor agreed well with ground measurements from 251 flux towers across various ecosystems and climate zones globally, with high correlation (0.75), low bias (0.08mm/d), and low root mean square error (0.93 mm/d). The estimated ET showed reasonable spatial patterns, and superior in presenting the spatial variation of ET especially in the mountain regions and in the arid irrigated cropland regions. The ET estimation is conducted at daily temporal step and 1km spatial resolution. For easier publication, the daily/1-km ET from ETMonitor (https://doi.org//10.12237/casearth.6253cddc819aec49731a4bc2) was summed to obtain monthly ET in this dataset. The data type is 16-bit signed integer, the scale factor is 0.1, and the unit is mm/month. The missing values were filled by -1.
ZHENG Chaolei , JIA Li , HU Guangcheng
This datasets include the spatial distribution multi-year means of the SOS and EOS from 2000 to 2018, and the temporal trends of the SOS and EOS from 1982 to 1999 and 2000 to 2020 across the Tibetan Plateau. Based on AVHRR NDVI, MODIS NDVI, and EVI, four steps were used to minimize bias and noise in SOS and EOS extracted from time series of vegetation indexes. First, pixels with multiple-year average vegetation indexes lower than a threshold are regarded as areas of low or no vegetation coverage and are excluded. The pixels with weak seasonality of greenness are also excluded. Second, values of vegetation indexes contaminated by snow cover, ice, or both in winter (December–early March) are substituted with the mean of non-contaminated, high-quality vegetation indexes values during winter. Third, remaining negative vegetation indexes bias caused by clouds and aerosols in other seasons is calibrated by a Savitzky–Golay filtering technique. Finally the improved annual time series of vegetation indexes is fitted to double logistic or modified double logistic functions. Based on thresholds and inflection-point, the SOS and EOS across the Tibetan Plateau were extracted. The spatial resolution of the datasets were 250m and 1/12°. The data quality is reliable.
SHEN Miaogen
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2020. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2020. The land cover classification products of 30m in 2020 areproduced using change detection method based on the land cover classification product of 2019 in Google Earth engine platform with the Landsat series data . The overall accuracy of the product is better than 85%. This product is a continuation of the human activity parameter product from 1985 to 2019，which also can be downloaded from this website.
YANG Aixia, ZHONG Bo, WU Junjun
Data content: this data set is the historical archived satellite data of the domestic high score series (GF1 / 2 / 3 / 4) in the key river and lake research areas of the Qinghai Tibet Plateau from 2015 to 2020, which can cover the typical river and lake areas for effective monitoring. The time range of the data is from 2015 to 2020. Data source and processing method: the data are level 1 products. After equalizing radiation correction, the changes affecting the sensors are corrected by the equalizing functions of different detectors. Some data are based on the Landsat 8 images in the same period as the base map, and control points are selected for geometric correction of the images. Then, orthophoto correction is carried out based on DEM data, and band fusion processing is carried out for the corresponding data. Data quality description: the Gaofen series satellites are processed by the China Resources Satellite Application Center. There are raw data received by the satellite ground receiving station of the Chinese Academy of Sciences and processed products at all levels. Among them, level 1a (pre-processing level radiometric correction image product): image data processed by data analysis, uniform radiometric correction, noise removal, MTFC, CCD splicing, band registration, etc; And provide RPC files for satellite direct attitude orbit data production. Refer to the data website of China Resources Satellite Application Center for details. Data application achievements and prospects: the data are domestic high-resolution data with high resolution, which can be used to monitor the changes of the Qinghai Tibet Plateau as a water tower in Asia and the generated images, and test the accuracy of other data in the region
QIU Yubao
The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
NIU Fujun
Based on the Sentinel-2 and Landsat 5/7/8 multispectral instrument imageries combined with in-situ measured hydrological data, bankfull river geometry of six major exorheic river basins of the Qinghai-Tibet Plateau (the upper Yellow River, upper Jinsha River, Yalong River, Lantsang River, Nu River and Yalung Zangbo River) are presented. River surface of six mainstreams and major tributaries are included. For each river basin, two types of rivers are included: connected and disconnected rivers. Format of the dataset is .shp exported from the ArcGIS 10.5. Three products are included in the dataset: one original product (bankfull river surface dataset) and two derived products (bankfull river width dataset and bankfull river surface area dataset with a 1 km river length interval). These three products are in three folders. The first folder, “1-Bankfull River Surface”, contains river surface vectors for six river basins in the .shp file. The second folder, “2-Bankfull River Width”, contains bankfull river widths and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. The river width vectors in the .shp files are also provided in the second folder. The third folder, “3-Bankfull River Surface Area”, contains bankfull river surface areas and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. Three Supplementary Files are included: Supplementary File 1, tables and figures related to the dataset; Supplementary File 2, used for river surface extraction based on GEE platform; Supplementary File 3, used for river width extraction based on Matlab. The provided planform river hydromorphology data can supplement global hydrography datasets and effectively represent the combined fluvial geomorphology and geological background in the study area.
LI Dan , XUE Yuan , QIN Chao , WU Baosheng , CHEN Bowei , WANG Ge
(1) Data content: The evolution product of the global climate-ecological pattern. The time range includes the historical period 1981-2020 with a spatial resolution of 0.5°, and the future period 2021-2100 (the future period contains four different shared socioeconomic pathways: SSP126, SSP245, SSP370, SSP585), with a spatial resolution of 1°, every 20 years 1 issue. (2) Data source and processing method: The leaf area index data of GLOBMAP was selected as the basis in the historical period, and the leaf area index data of three CMIP6 models (ACCESS-ESM1-5, CanESM5, UKESM1-0-LL) were integrated in the future period. The relationship between temperature, precipitation and radiation and the leaf area index was constructed through multiple linear regression, and the corresponding coefficients were extracted to characterize the influence of each climate variable on the leaf area index. Finally, the RGB map was used to characterize the climatic factors of the leaf area index. Influence coefficient. (3) Data quality description: Global 20-year period 1, historical period 2 period (1981-2000; 2001-2020), the future period includes four shared socio-economic paths (SSP126, SSP245, SSP370, SSP585), each path The next 4 issues (2021-2040; 2041-2060; 2061-2080; 2081-2100). (4) Data application achievements and prospects: This data can be used for studies related to the evolution of vegetation and ecosystems in the context of climate change.
HE Bin
Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
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
Project based on Landsat_ Through manual interpretation and machine learning algorithm, tm30m remote sensing data has completed the extraction of spatial pattern distribution information of six types of ecosystems in Qilian Mountains from 1990 to 2015, including forest, farmland, grassland, wetland, settlement city and desert. This set of data can be used to study the evolution law of regional ecosystem macro pattern, ecosystem service function evaluation, major ecological restoration project planning and effect evaluation. The evolution of ecosystem macro pattern is a macro response to the evolution of natural processes driven by climate socio-economic coupling. It is also a direct reflection of land use and land cover changes. It is also an important data basis for the evaluation of the effectiveness of regional sustainable development. The research can provide data basis for the evaluation of green development index in Qilian mountain area.
WU Feng
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