SRTM DEM data on the Tibetan Plateau (2012)

This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture (CIAT)with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.

0 2020-05-29

The monthly mean ground surface temperature in the Qilian Mountains on the Qinghai-Tibet Plateau (1980-2013)

This dataset includes the ground surface temperature in the Qilian Mountains on the Qinghai-Tibet Plateau during 1980-2013. This dataset was obtained from the ERA-interim reanalysis product. The ERA-interim system includes a 4-dimensional variational analysis (4D-Var). The quality of the data has been improved using the bias correction of satellite data. The spatial resolution of the dataset is 0.125°. The dataset includes the grid data of the ground surface temperature in the Qilian Mountains during the past 30 years, and may provide a basic data for relevant studies such as climatic change, ecosystem succession, and earth system models.

0 2020-05-28

The map of fractional vegetation cover in the Yellow River source region of Tibet Plateau (2015)

This dataset is a pixel-based maximum fractional vegetation cover map within the Yellow River source region on the Qinghai-Tibet Plateau, with an area of about 44,000 square kilometers. Based on the time series images acquired from MODIS with a resolution of 250 m and Landsat-8 with a resolution of 30 m in 2015 during the vegetation growing season, the data are derived using dimidiate pixel model and time interpolation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data is in the format of grid.

0 2020-05-28

Snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018)

Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.

0 2020-05-28

Landsat multi-spectral remote sensing images dataset of pan-third pole key points region (2000-2016)

The data sources of this dataset are the first to seventh bands of the top-of-atmosphere (TOA) reflectance data of Landsat-5 and landsat-8 satellites. Landsat satellites are sun synchronous satellite with a repetition period of 16 days. Based on the data of Landsat-5 and landsat-8 TOA reflectance from 2000 to 2016, this dataset mainly covers the pan third polar key points region in Southeast Asia and the Middle East. It uses Google Earth engine cloud computing platform to clip the data of the study area, and finally gets the 30-meter resolution multi spectral remote sensing image data of the pan third polar region 2000-2016 in TIFF format.

0 2020-05-27

30-meter Global land cover data (2010, 2015 and 2017) for key nodes of pan-third pole region

Global land cover data are key sources of information for understanding the complex interactions between human activities and global change. FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover) from Tsinghua is the 30 m resolution global land cover maps produced. The Global land cover data of all 34 key nodes of pan-third pole region are produced through analyse by argis. The classfication system is crop(10), forest(20), grass(30), shrbu(40), wetland(50), water(60), tundra(70), impervious(80), Bareland(90), snow/ice(100), cloud(120). Finally, This data set serves as the research basis for all remote sensing data and provides baseline data for the project.

0 2020-05-27

30-meter and 16-day Landsat NDVI dataset of key nodes in pan-third pole (2000-2016)

The vegetation index mainly reflects the differences between the visible light, near-infrared reflection and soil background. The vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. At present, normalized vegetation index (NDVI) is an important data source for detecting vegetation growth status, vegetation coverage and eliminating some radiation errors. NDVI can reflect the background influence of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and it is related to vegetation coverage. Landsat satellite data product is an important data source for NDVI estimation. Taking 31 key nodes and 3 major projects in the third pole as the research area, based on the data of Landsat-5 and landsat-8 from 2000 to 2016, the NDVI of different areas was cut and estimated, and finally the 16 day time series ten meter (30M) high-resolution NDVI data of key node areas in the third pole from 2000 to 2016 was obtained.

0 2020-05-27

Landsat-derived tree cover dataset of key node of pan-third pole region (2000-2016)

The data includes the county-level data of characteristic agriculture distribution in the Qinghai Tibet Plateau, which lays the foundation for the spatial distribution and development of characteristic agriculture in the Qinghai Tibet Plateau. The data are from the development plan of Tibet Plateau characteristic agricultural products base (2015-2020), Qinghai province's 13th five year plan, Sichuan Province's 13th five year plan for agricultural and rural economic development, Xinjiang Uygur Autonomous Region's 13th five year plan for targeted poverty alleviation of agricultural characteristic industries (2016-2020), Yunnan Province's overall plan for plateau characteristic agricultural modernization( 2016-2020), implementation opinions on fostering and strengthening characteristic agricultural industries in Gansu Province to boost poverty alleviation, China National Geographic Indication product network (http://www.cgi.gov.cn/home/default/), regional layout planning of characteristic agricultural products (2013-2020). The data is the distribution of county-level characteristic agriculture, realizing the spatialization of county-level characteristic agriculture. The data can be applied to the research on the spatial distribution of characteristic agriculture and the development of characteristic agriculture in the future.

0 2020-05-27

PM2.5 Grids for key nodes of pan-third pole region (2000-2016)

The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016 consist of annual concentrations (micrograms per cubic meter) of ground-level fine particulate matter (PM2.5), with dust and sea-salt removed. This data set combines AOD retrievals from multiple satellite instruments including NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. Gridded data sets at 0.01 degrees are provided to allow users to agglomerate data as best meets their particular needs. With 34 key nodes of pan-third pole region as the research area, based on the Global PM2.5 data from 2000 to 2016, the forest coverage data of different regions were tailored and estimated, and the PM2.5 data of key nodes in 2000-2016 was obtained.

0 2020-05-27

Satellite precipitation dataset for key nodes of pan-third pole region (1998-2016)

This dataset algorithmically merges microwave data from multiple satellites, including SSMI, SSMIS, MHS, AMSU-B and AMSR-E, each inter-calibrated to the TRMM Combined Instrument. Algorithm 3B43 is executed once per calendar month to produce the single, best-estimate precipitation rate and RMS precipitation-error estimate field (3B43) by combining the 3-hourly merged high-quality/IR estimates (3B42) with the monthly accumulated Global Precipitation Climatology Centre (GPCC) rain gauge analysis. This dataset also combines ground-based rain gauge data to maximize the use of existing probe data, providing an optimal estimate of each grid’s precipitation for each standard observation. Taking the key nodes mainly covering Southeast Asia and the Middle East as the research area, based on the TRMM 3B43 data from 1998 to 2016, Google Earth Engine was used to clip the research area data, and finally the monthly precipitation dataset for key nodes of pan-third pole region from 1998 to 2016 was obtained. (In Minsk, Novosibirsk, and Warsaw, because the latitude is higher than 50°N, TRMM 3B43 does not have data for these areas, so the upscaling GPM data is used.)

0 2020-05-27