The Antarctic McMurdo Dry Valleys ice velocity product is based on the Antarctic Ice Sheet Velocity and Mapping Project (AIV) data product, which is post-processed with advanced algorithms and numerical tools. The product is mapped using Sentinel-1/2/Landsat data and provides uniform, high-resolution (60m) ice velocity results for McMurdo Dry Valleys, covering the period from 2015 to 2020.
Pine Island Glacier, Swett Glacier, etc. are distributed in the basins of the Antarctic Ice Sheet 21 and 22, which is one of the areas with the most severe melting in the Southwest Antarctica. This dataset first uses Cryosat-2 data (August 2010 to October 2018) to establish a plane equation in each regular grid, taking into account terrain items, seasonal fluctuations, backscattering coefficients, wave front width, lifting rails and other factors, and calculates the elevation change of ice cover surface in the grid through least square regression. In addition, we used ICESat-2 data (October 2018 to December 2020) to calculate the surface elevation change during the two periods by obtaining the elevation difference at the intersection of satellite lifting orbits in each regular grid. The spatial resolution of surface elevation change data in two periods is 5km × 5km, the file format is GeoTIFF, the projection coordinate is polar stereo projection (EPSG 3031), and it is named by the name of the satellite altimetry data used. The data can be opened using ArcMap, QGIS and other software. The results show that the average elevation change rate of the region from 2010 to 2018 is -0.34 ± 0.08m/yr, which belongs to the area with severe melting. The annual average elevation change rate from October 2018 to November 2020 is -0.38 ± 0.06m/yr, which is in an intensified state compared with CryoSat-2 calculation results.
This data set includes five periods of lake transparency data, including 1995, 2002, 2005, 2010 and 2015. The data sources are: Landsat 5, Landsat 7 and Landsat 8. Method of use: It is convenient to measure the spectral reflectance. On the basis of analyzing the relationship between the spectral reflectance and the transparency measured synchronously, the semi empirical method is used to select the best band combination, establish the transparency algorithm of Qinghai Tibet Plateau lakes, and obtain the water transparency. The verification of measured points shows that the relative error of water transparency estimation is 35%.
The fractional snow cover (FSC) is the ratio of snow cover area (SCA) to unit pixel area. The data set is made by bv-blrm snow area proportional linear regression empirical model; The source data used are mod09ga 500m global daily surface reflectance products and mod09a1 500m 8-day synthetic global surface reflectance products; The production platform uses Google Earth engine; The data range is global, the data preparation time is from 2000 to 2021, the spatial resolution is 500 meters, and the temporal resolution is year by year. This set of data can provide quantitative information of snow cover distribution for regional climate simulation and hydrological models.
We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
The dataset is the normalized difference water index (NDWI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDWI equation which use the difference ratio between the green band and NIR band to enhance the water information, and then to weaken the information of vegetation, soil, buildings and other targets.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDWI is usually used to extract surface water information effectively, therefore it is widely used in water resoureces, hydrology, forestry and agriculture.
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a key physiological variable in the study of carbon cycling and is one of the basic variables to describe vegetation ecosystems. The classification results of surface vegetation types in Qinghai-Tibet Plateau region are obtained based on the Landsat reflectance data(30m spatial resolution). According to NDVI of different vegetation types, the remote sensing inversion model is constructed to produce the growing season FPAR products for each vegetation type. This product can be used as one of the parameters to calculate vegetation carbon sequestration and evaluate vegetation ecosystem status.
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 email@example.com
LinksNational Tibetan Plateau Data Center