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
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
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
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
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
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
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
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 data set product contains the aboveground biomass and vegetation coverage data products of the Qinghai-Tibet Plateau every five years from 1990 to 2020 (1990, 1995, 2000, 2005, 2010, 2015 and 2020).The aboveground biomass of the Qinghai-Tibet Plateau is the remote sensing inversion product of above-ground biomass inversion models based on different land cover types including grassland, forest, etc. Vegetation coverage data of the Qinghai-Tibet Plateau is inversed using remote sensing by the dimidiate pixel model. Among them, the aboveground biomass and vegetation coverage data from 2000 to 2020 were estimated based on MODIS data, the spatial resolution was 250 m; the aboveground biomass and vegetation coverage data of 1990 and 1995 were estimated based on NOAA AVHRR data, the spatial resolution after resampling process is 250 m. This dataset can provide basic data for revealing the temporal and spatial pattern of land cover areas and quality on the Qinghai-Tibet Plateau and supporting the assessment of ecosystems, ecological assets and ecological security.
WU Bingfang
The Normalized Difference Vegetation Index (LST) dataset is original from MODIS products and preprocessed by format conversion, projection and resampling. The existing format is TIFF and projection is Krasovsky_1940_Albers. The data set has a spatial resolution of 1000 meters and provides one image per year during the period from 2001 to 2020. NDVI products are calculated by reflectance of red and near-infrared bands, which can be used to detect vegetation growth state and vegetation coverage. NDVI is ranged from -1 to 1, and the negative value means the land is covered by snow, water, etc. By contrast, positive value means vegetation coverage, and the coverage increases with the increase of NDVI.
ZHU Juntao
As the basis of ecosystem material and energy cycle, net primary productivity (NPP) of vegetation can reflect the carbon sequestration capacity of vegetation at regional and global scales. It is an important indicator to evaluate the quality of terrestrial ecosystem. Aiming at the production of net primary productivity products of vegetation, based on the principle of light energy utilization model and coupling remote sensing, meteorological, vegetation and soil type data, the modeling of ecosystem productivity in national barrier area was studied. In terms of parameter selection, the photosynthetic effective radiation (APAR) is calculated from the spot/veg etation NDVI satellite remote sensing data, China's vegetation map, total solar radiation and temperature data; Compared with the soil water molecular model, the regional evapotranspiration model can simplify the parameters and enhance the operability of the model. Taking photosynthetic effective radiation and actual light energy utilization as input variables of CASA (Carnegie Ames Stanford approach) model, the net primary productivity of land vegetation on the Qinghai Tibet Plateau with a resolution of 1km from 2000 to 2018 was estimated based on the parametric model.
WANG Xiaofeng
The dataset is the salinity index (SI) products of 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the SI equation which is based on the method that the red band and blue band can well reflect the soil salinity.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.SI is usually used to quantitatively evaluate the salinized soil .
PENG Yan
The dataset is the land surface temperature (LST) product of 2020 over the Tibetan Plateau. The dataset is retrieved based on Landsat images and a practical single-channel (PSC) algorithm. When validated with the simulation data set, the root-mean-square error (RMSE) of the PSC algorithm was 1.23 K. The corresponding quality assessment (QA) product is also generated to identify cloud, cloud shadow, ice and snow. LST is a commonly used land surface parameter, which can provide data product support for the research and applications in resources survey, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
The dataset includes FPAR, GPP, NPP, evapotranspiration product and LAI product. FPAR product is and LAI product are obtained from the MODIS Terra MOD15A2H dataset, GPP and NPP product are obtained from the MOD15A2H dataset and evapotranspiration product is obtained from MODIS Terra MOD16A2 dataset of 2020 over the Tibetan Plateau,which is downloaded from USGS, and their formats are converted from .hdf to .tif by GDAL.The quality assessment files are also included for aboved products,and they are stored in an efficient bit-encoded manner.The MODIS products play an important role in forest, agriculture, ecology.
GONG Chengjuan
The disaster catalogue of the Qinghai Tibet Plateau contains the spatial distribution and type information of various historical disasters, ranging from Pakistan and Kashmir in the west, Qinghai Province in the East, the foothills of the Himalayas in the South and Arkin mountain in the north. The production of data is completed by a large number of manual remote sensing interpretation, field investigation, collection of geological survey data and open source data. The data is stored in the form of vector points, mainly including attribute table, indicating disaster type, coordinates and other information. This data can be used to study the spatial distribution law of disasters and disaster evaluation. This data contains a total of 23536 pieces of data. Due to the reference of geological survey data, most of the debris flow data are distributed along the road, and there are few data in no man's land.
TANG Chenxiao
This diurnal (hourly) land surface albedo product is with a spatial resolution of 0.02 ° x 0.02 ° over the Tibet Plateau from 2016-2019. Multi-sensory data is used to retrieve the Extended Multi-Sensor Combined BRDF Inversion model (EMCBI) developed from a kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF / albedo of good spatial-temporal continuity is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himiwarri-8 AHI land surface reflectance (a geostationary satellite ). MODIS land surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days, and then the albedo is estimated. The black sky albedo is calculated hourly from 9:00 to 18:00 at Beijing Time (UTM zone 8). The validation results show that it meets the accuracy requirements of albedo application, and agreed well with the in situ albedo inner-daily variation.Tt has more advantages in capturing rapidly changing surface features, especially the inner-daily variations, and has better temporal and spatial continuity. It can effectively support the study of radiation energy balance and environmental change in the Tibet Plateau.
YOU Dongqin, YOU Dongqin, TANG Yong, TANG Yong, TANG Yong, HAN Yuan HAN Yuan
This data set is daily surface albedo product over Tibet plateau region from 2002 to 2020 with a spatial resolution of 0.00425°. The MODIS reflectance data product was used to retrieve the Extended Multi-Sensor Combined BRDF Inversion (EMCBI) Model which has coupled with topographic effects with assistance of a BRDF priori-knowledge. The daily BRDF was retrieved in a 5-day period to collect multi-angular information from MODIS observations. And then the daily albedo is estimated, where the black sky albedo was calculated at local noon. MODIS surface reflectance data (MOD09GA and MYD09GA) are downloaded from the official website. The albedo product is quality-controlled with better temporal and spatial continuity in Tibet plateau area. The validation results show that it meets the accuracy requirements of albedo application with higher precisions comparing to the other similar products. And thus, this product is useful for the long-term environmental monitoring and radiation energy budget research study.
YOU Dongqin, YOU Dongqin, TANG Yong, TANG Yong, TANG Yong, HAN Yuan HAN Yuan
1) Data content: the modified universal soil and water loss equation (RUSLE) is used to estimate the soil water erosion modulus at the plot scale. The soil conservation is used to measure the ability of the ecosystem to reduce soil erosion caused by precipitation, and to characterize the amount of water erosion reduction caused by vegetation, that is, the difference between the amount of soil water erosion under actual surface coverage and extreme degradation. Based on the above process, a 30-year (every five years from 1990 to 2020) ecological function map of the Qinghai Tibet Plateau is made, including two parts of water conservation and soil conservation data sets. 2) Data source and processing method: Based on ecosystem type data, MODIS NDVI products, 1:1 million soil attribute data, meteorological interpolation and elevation data, the atlas uses the precipitation storage method to estimate the water conservation of forest and grassland ecosystems, and measures its water conservation capacity by the hydrological regulation effect of ecosystem, that is, the increment of water conservation compared with bare land. 3) Data quality: the data has a temporal resolution of 5 years and a spatial resolution of 1000m, which can meet the needs of high-precision Ecosystem Assessment on the Qinghai Tibet Plateau. 4) Data application achievements and prospects: the statistical results show that in recent 30 years, the spatial distribution of water conservation work energy on the Qinghai Tibet Plateau has shown an overall distribution pattern of high in the southeast and low in the northwest, and gradually decreasing from the southeast to the northwest. The overall amount of soil conservation showed an increasing trend in fluctuation, and the amount of soil conservation function showed a decreasing trend in most areas of the West and south, of which the decreasing trend was obvious in the South and the increasing trend in the East.
CAO Wei, HUANG Lin
Muli coal mine is a typical industrial and mining area in the Qinghai Tibet Plateau. Taking Muli coal mine as an example, in the area delimitation, we take the coordinate boundaries of East, West, North and south to cut it out, and get a rectangular area, and take it as the mining area of Muli coal mine. We use the national 1km land use remote sensing monitoring data provided by the resource, environment and data center of the Institute of geography, Chinese Academy of Sciences. The data production of the three phases in 2000, 2005 and 2010 is based on Landsat TM / ETM Remote Sensing Image of each phase as the main data source, and the two phases in 2015 and 2020 are based on Landsat 8 oli / tirs remote sensing image as the main data source, which are generated by manual visual interpretation. The data format is grid TIF, and the resolution is 1km.
LIU Zhenwei, CHEN Shaohui
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