This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Based on the medium resolution long time series remote sensing image Landsat, the data set obtained six periods of ecosystem type distribution maps of the Qinghai Tibet Plateau in 1990 / 1995 / 2002 / 2005 / 2010 / 2015 through image fusion, remote sensing interpretation and data inversion, and made the original ecological base map of the Qinghai Tibet Plateau in 25 years (1990-2015). According to the area statistics of various ecosystems in the Qinghai Tibet Plateau, the area of woodland and grassland decreased slightly, the area of urban land, rural residential areas and other construction land increased, the area of rivers, lakes and other water bodies increased, and the area of permanent glacier snow decreased from 1990 to 2015. The atlas can be used for the planning, design and management of ecological projects in the Qinghai Tibet Plateau, and can be used as a benchmark for the current situation of the ecosystem, to clarify the temporal and spatial pattern of major ecological projects in the Qinghai Tibet Plateau, and to reveal the change rules and regional differences of the pattern and function of the ecosystem in the Qinghai Tibet Plateau.
This dataset contains daily land surface evapotranspiration products of 2020 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, MERRA meteorological data, and China Meteorological Forcing Dataset.
Land Surface temperature is one of the important parameters of surface energy balance. This dataset is the monthly land surface temperature data of typical stations in Heihe River Basin from June to October in 2020; In flight, DJI M600 Pro UAV was equipped with the WIRIS Pro sc thermal imager. taking SD station in the wetland, DM station in the oasis and Hz station in the desert as the center, the land surface temperature was observed, and the surface brightness temperature image was obtained. The flying height of the UAV was about 300m, the pixel of the thermal imager was 336x256, and the spatial resolution of the image was 0.4m. The surface temperature retrieval algorithm is an improved single channel algorithm, which is applied to the surface brightness temperature data obtained by UAV thermal imager, and finally the land surface temperature data with 0.4 m spatial resolution is obtained.
NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides the monthly normalized differential vegetation index (NDVI) of UAV remote sensing with a spatial resolution of 0.2 m from June to October in 2020. It was measured in the midstream of Heihe River Basin over typical stations. The Pix4D mapper software was used for image mosaic and NDVI calculation.
1) Data content (including elements and significance) This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin. <br><br> 2) Data sources and processing methods In this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused. <br><br> 3) Data quality description Validations show that this data set has high spatial accuracy (Wang et al, 2021). <br><br> 4) Data application achievements and Prospects This data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.
Surface solar irradiance (SSI) is one of the products of FY-4A L2 quantitative inversion. It covers a full disk without projection, with a spatial resolution of 4km and a temporal resolution of 15min (there are 40 observation times in the whole day since 20180921, except for the observation of each hour, there is one observation every 3hr before and after the hour）, and the spectral range is 0.2µ m～5.0 µ m. The output elements of the product include total irradiance, direct irradiance on horizontal plane and scattered irradiance, the effective measurement ranges between 0-1500 w / m2. The qualitative improvement of FY-4A SSI products in coverage, spatial resolution, time continuity, output elements and other aspects makes it possible to further carry out its fine application in solar energy, agriculture, ecology, transportation and other professional meteorological services. The current research results show that the overall correlation of FY-4A SSI product in China is more than 0.75 compared with ground-based observation, which can be used for solar energy resource assessment in China.
This dataset includes data of glacier elevation changes in 2000‒2013 and 2000‒2017 at high spatial resolution (5 m). The specific areas are Namco area in the west section of Nyainqentangula Mountains (WNM) and Kangri Karpo area in the east section of Nyainqentangula Mountains (ENM). Glacier boundary refers to Randolph Glacier Inventory Version 4.0 (RGI 4.0). The glacier elevation changes were calculated from the DEM data generated by ZiYuan-3 Three-Line-Array (ZY-3 TLA) stereo images in 2013 and 2017 and SRTM DEM data in 2000, respectively. The data in the WNM include three periods, i.e., 2000‒2013, 2013‒2017 and 2000‒2017. The data in the ENM include one period, i.e., 2000‒2017. The spatial resolution of the dataset is 5 meters, the unit is m a^−1, the data format is GeoTIFF, the data type is floating-point, and the projection mode is UTM 46N for the west segment and UTM 47N for the east segment. The glacier elevation change can be transformed into the glacier mass balance (unit: w.e. a^−1) of corresponding temporal intervals by multiplying the average density of the glacier. This dataset can provide the details of the spatial patterns of glacier elevation changes to support modeling studies of glacier mass balance in this region.
The data set is NDVI data of long time series acquired by SeaWiFS. The time range of the data set is from September 1997 to 2007. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 15 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 4 km, temporal resolution is 15 days, time range: 256 days in 1997 to 365 days in 2007.