The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.
The near-surface soil freeze/thaw state characterizes the dormancy and activity of the land surface process. This freeze-thaw interphase can cause a series of complex surface process trajectory pattern mutations, affecting soil hydrothermal characteristics, surface runoff, groundwater supply, and other water cycle process; it also affects climate change through water and energy cycling mechanisms. Based on AMSR-E and AMSR2 passive microwave data, adopting the global near-surface freeze-thaw state (spatial resolution: 0.25°; temporal coverage: 2002-2014) prepared by discriminant algorithm, the data set can be used to analyze the spatial distributions and trend variations of the indexes (such as start/end dates, freeze/thaw duration, and freeze ranges) of global near-surface freeze-thaw cycle. It can also provide data support for understanding the interaction mechanism between land surface freeze-thaw cycle and water and energy exchange processes under the background of global change.
The medium-resolution MODIS river and lake ice phenology data set in the high latitudes of the northern hemisphere from 2002 to 2019 is based on the Normalized Difference Snow Index (NDSI) data of the Moderate Resolution Imaging Spectroradiometer(MODIS). Daily lake iceextent and coverage under clear-sky conditions was examined byemploying the conventional SNOWMAP algorithm, and thoseunder cloud cover conditions were re-determined using the temporal and spatial continuity of lake surface conditions througha series of steps.The lake ice phenology information obtained in this dataset was highly consistent with that from passive microwave data at an average correlation coefficient of 0.91 and an RMSE value varying from 0.07 to 0.13.
Lake ice is an important parameter of Cryosphere. Its change is closely related to climate parameters such as temperature and precipitation, and can directly reflect climate change. Therefore, lake ice is an important indicator of regional climate parameter change. However, due to the poor natural environment and sparsely populated area, it is difficult to carry out large-scale field observation, The spatial resolution of 10 m and the temporal resolution of better than 30 days were used to monitor the changes of different types of lake ice, which filled in the blank of observation. The hmrf algorithm is used to classify different types of lake ice. The distribution of different types of lake ice in some lakes with an area of more than 25km2 in the three polar regions is analyzed by time series to form the lake ice type data set. The distribution of different types of lake ice in these lakes can be obtained. The data includes the sequence number of the processed lake, the year and its serial number in the time series, and vector The data set includes the algorithm used, sentinel-1 satellite data, imaging time, polar region, lake ice type and other information. Users can determine the change of different types of lake ice in time series according to the vector file.
The coverage time of glacier runoff data set in the five major river source areas of the Qinghai Tibet Plateau is from 1971 to 2015, and the time resolution is year by year, covering the source areas of five major rivers (Yellow River source, Yangtze River source, Lancang River source, Nu River source, Yarlung Zangbo River source). The data is based on multi-source remote sensing and measured data. The glacier runoff data is simulated by using the daily scale meteorological data of five major river source areas and their surrounding meteorological stations, the global vegetation products of umd-1km, the igbp-dis soil database, the first and second glacier catalogue data, and the distributed hydrological model vic-cas coupled with the glacier module is used to simulate the glacier runoff data. The simulation results are verified by the site measured data to enhance the quality control. Data indicators include: Glacier runoff (rate of glacier runoff:%), total runoff (mm / a), snow runoff (rate of snow runoff:%), and rainfall runoff rate (rainfall runoff rate:%).
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1979~2017, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
In recent years, the Antarctic Ice Sheet experiences substantial surface melt, and a large amount of meltwater formed on the ice surface. Observing the spatial distribution and temporal evolution of surface meltwater is a crucial task for understanding mass balance across the Antarctic Ice Sheet. This dataset provides a 30 m surface meltwater coverage, extracted from Landsat images, in the typical ablation zone of the ice sheet (Alexandria Island, Antarctic Peninsula) from 2000 to 2019. The projection of this dataset is South Polar Stereographic. The formats of the dataset are vector (.shp) and raster (.tif).
The data set is the meteorological and observational data of hulugou shrub experimental area in the upper reaches of Heihe River, including meteorological data, albedo data and evapotranspiration data under shrubs. 1. Meteorological data: Qilian station longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3232.3m, scale meteorological data from January 1, 2012 to December 31, 2013. Observation items include: temperature, humidity, vapor pressure, net radiation, four component radiation, etc. The data are daily scale data, and the calculation period is 0:00-24:00 2. Albedo: daily surface albedo data from January 1, 2012 to July 3, 2014, including snow and non snow periods. The measuring instrument is the radiation instrument on the 10m gradient tower in hulugou watershed. Among them, the data from August 4 to October 2, 2012 was missing due to instrument circuit problems, and the rest data quality was good 3. Evapotranspiration: surface evapotranspiration data of Four Typical Shrub Communities in hulugou watershed. The observation period is from July 18 to August 5, 2014, which is the daily scale data. The data include precipitation data, evaporation and infiltration data observed by lysimeter. The data set can be used to analyze the evapotranspiration data of alpine shrubs and forests. The evapotranspiration of grassland under canopy was measured by a small lysimeter with a diameter of 25 cm and a depth of 30 cm. Two lysimeters were set up in each shrub plot, and one lysimeter was set for each shrub in transplanting experiment. The undisturbed undisturbed soil column with the same height as the barrel is placed in the inner bucket, and the outer bucket is buried in the soil. During the embedding, the outer bucket shall be 0.5-1.0 cm higher than the ground, and the outer edge of the inner barrel shall be designed with a rainproof board about 2.0 cm wide to prevent surface runoff from entering the lysimeter. Lysimeter was set up in the nearby meteorological stations to measure grassland evapotranspiration, and a small lysimeter with an inner diameter of 25 cm and a depth of 30 cm was also set up in the sample plot of Picea crassifolia forest to measure the evaporation under the forest. All lysimeters are weighed at 20:00 every day (the electronic balance has a sensing capacity of 1.0 g, which is equivalent to 0.013 mm evaporation). Wind proof treatment should be taken to ensure the accuracy of measurement. Data processing method: evapotranspiration is mainly calculated by mass conservation in lysimeter method. According to the design principle of lysimeter lysimeter, evapotranspiration is mainly determined by the quality difference in two consecutive days. Since it is weighed every day, it is calculated by water balance.
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, and provide parameters and verification data for the development of response and feedback models of permafrost, glacier and snow cover to global changes under GIS framework. On the other hand, the system collates and rescues valuable cryospheric data to provide a scientific, efficient and safe management and analysis tool. Chinese Cryospheric Information System contains three basic databases of different research regions. The basic database of Urumqi river basin is one of three basic databases, which covers the Urumqi river basin in tianshan mountain, east longitude 86-89 °, and north latitude 42-45 °, mainly containing the following data: 1. Cryospheric data.Include: Distribution of glacier no. 1 and glacier no. 2; 2. Natural environment and resources.Include: Terrain digital elevation: elevation, slope, slope direction; Hydrology: current situation of water resource utilization;Surface water; Surface characteristics: vegetation type;Soil type;Land resource evaluation map;Land use status map; 3. Social and economic resources: a change map of human action; Please refer to the documents (in Chinese): "Chinese Cryospheric Information System design. Doc" and "Chinese Cryospheric Information System data dictionary. Doc".
From 2013 to 2014, the Glacial Geomorphology of the upper reaches of Heihe River in the late Quaternary was investigated and sampled. Based on the field investigation and remote sensing image, the distribution map of moraine at different levels near the ridge of the upper reaches of the Bailang river was obtained.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center