Tibetan Plateau, located in southwest China, is one of the key areas affecting the Asian monsoon, and it is also an early warning area and sensitive area for global climate change. As the main parameter of surface energy balance, surface temperature represents the degree of energy and water exchange between earth and atmosphere, and is widely used in climatology, hydrology and ecology. The study of land-atmosphere interaction in Qinghai-Xizang Plateau urgently needs long time series and all-weather surface temperature data set with high temporal and spatial resolution. However, the frequent cloud cover characteristics in this area limit the use of the existing satellite thermal infrared remote sensing surface temperature data set. The generation method of this data set is an integrated method of thermal infrared and passive microwave surface temperature based on the time component decomposition model of surface temperature. This method was originally applied to Northeast China and its adjacent areas, and subsequently extended to western China including the Qinghai-Xizang Plateau. The main input data of this method are Aqua MODIS,Aqua AMSR-E,GCOM-W1 AMSR2 and other data, and the auxiliary data include leaf area index (LAI) products provided by satellite remote sensing, surface cover type data and so on. This method makes full use of the steady and unstable components of surface temperature provided by satellite thermal infrared remote sensing and passive microwave remote sensing, as well as the spatial correlation of surface temperature. The obtained all-weather surface temperature has good accuracy and image quality. The time span of the dataset is from 2003 to 2018, the time resolution is 2 times a day, and the spatial resolution is 1 km, which is expected to provide data support for related applications.
A remote sensing image mosaic was generated for Greenland by processing a collection of 108 scenes of Landsat 8 OLI remote sensing images from 2014 to 2015 with DN correction, cloud removal correction, planetary reflectance correction, reflectance and RGB value conversion, image synthesis and merging, etc. The spatial resolution of the entire image is 30 m, and the stereographic projection method is adopted.
The Sentinel-1 satellite is an Earth observation satellite of the Copernicus Plan of the European Space Agency (GMES), which carries a C-band synthetic aperture radar. It was launched on April 3, 2014 and was on trial operation on August 7. Sentinel-1 can be used for Arctic sea ice and average sea ice surveying and mapping, marine environmental monitoring and research, ground motion risk monitoring, forest mapping, and water and soil management and mapping to support humanitarian assistance and crisis situations. The Sentinel-1 orbital period is approximately 96 minutes, and the operational life is expected to be 7.25 years, allowing for up to 400 km of scanning. The Antarctic and Greenland Sentinel-1A SAR data sets are extra-wide (EW) mode data sets acquired by the Sentinel-1 satellite. The SAR data of this mode can be interfered by TOPSAR technology. It is mainly used in areas with large coverage and short revisit period such as the sea, glaciers and polar regions. The revisit period is 12 days, and the resolution is 20 m×40 m. The temporal coverage of the Antarctic is from July to December of 2015, and that of Greenland is from April to October of 2015. The Ground Range Detected (GRD) type Level-1 product in the data set comprises the focused data after multiview processing and WGS-84 elliptical projection. The pixel information represents the amplitude information of the detection area. Eliminating the heat noise can improve the image quality. The data for each scene are stored in a zip file. Each zip file consists of four folders (annotation, measurement, preview, and support), a manifest.safe file, and a pdf description file.
On July 23, 1972, the United States launched the world's first resource satellite "Landsat 1" , and Landsat 2 and Landsat 3 were launched in the following 10 years. These three satellites were the first generation of resource satellites. They were equipped withreturn-beam vidicon cameras and multi-spectral scanners (MSS) with 3 and 4 spectral segments respectively, a resolution of 79m and a width of 185Km. There are 28 scenes of MSS data in Heihe River Basin currently which were obtained on the following dates: 1972-10-14, 1972-10-30, 1973-01-10, 1973-01-31, 1973-02-16, 1973-06-04, 1973. -10-07, 1973-10-28 (2 scenes), 1973-12-22, 1974-01-05, 1975-10-07, 1975-10-09, 1976-07-04, 1976-10-18 , 1976-11-07, 1976-11-27, 1976-12-30, 1977-01-19, 1977-02-07, 1977-04-20, 1977-05-06 (2 scenes), 1977-05 -08, 1977-06-10, 1977-06-29, 1977-07-18, 1978-10-09. Ortho rectification was performed on the images.
Sentine-1 SAR data were used to monitor the permafrost of Biuniugou in Heihe River Basin of Qinghai-Tibet Plateau. Based on the Sentine-1 SAR image of Bison Valley from 2014 to 2018, the active layer thickness in the study area was estimated by using the small baseline set time series InSAR (DSs-SBAS) frozen soil deformation monitoring method based on distributed radar target, combined with SAR backscattering coefficient, MODIS surface temperature and Stefan model. The results show that the thickness of active layer is between 0.8 m and 6.6 m, with an average of about 3.3 M. It is of great significance to carry out large-scale and high-resolution monitoring.
This dataset includes 44 scenes, covering the whole Heihe River Basin, which were acquired on (yy-mm-dd) 2012-08-25, 2012-09-03, 2012-09-08, 2012-09-13, 2012-09-18, 2012-09-23, 2012-09-28, 2012-10-03, 2012-10-13, 2012-10-18, 2012-10-22, 2012-11-01, 2012-11-11, 2012-11-21. The data are of multi-spectral bands with data product of Level 1. The spatial resolution is 1 m. ZY-3 dataset was acquired from purchase.
This dataset includes seven scenes; two scenes cover the Dayekou catchment on (yy-mm-dd) 2012-08-19 and 2012-08-28, one scene covers the airport desert experimental site on 2012-06-29, three scenes cover the Daman foci experimental area on 2012-06-21, 2012-07-10 and 2012-08-27, and one scene covers the natural oasis eco-hydrology experimental area in the lower reaches of the Heihe River Basin. The data were all acquired around 9:00 (BJT) of full swath mode with data product of Level 1A. PROBA CHRIS dataset was acquired through the European Space Agency (ESA)-Ministry of Science and Technology of China (MOST) Cooperative Dragon 2 (project ID: 5322) and Dragon 3 (project ID: 10649) Programme.
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
This is the MODIS data with 499 scenes covering the whole Heihe River basin in 2008 and 2009. The acquisition time is from 2008-04-23 to 2008-09-30 (295 scenes), and from 2009-05-01 to 2009-10-01 (204 scenes). MODIS data products have 36 channels with resolutions of 250m, 500m and 1000m respectively. The data format is pds, unprocessed, and the MODIS processing software is filed together with the original data. MODIS remote sensing data of Heihe Integrated Remote Sensing Joint Test are provided by Gansu Meteorological Bureau.
The data set includes the estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on GIMMS3g version 1.0, the latest version of the GIMMS NDVI data set. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 1982 to 2015, and the spatial resolution is 8 km.