Known as the "Asian water tower", the Qinghai Tibet Plateau is the source of many rivers in Southeast Asia. As an important and easily accessible water resource, the runoff provided by it supports the production and life of billions of people around it and the diversity of the ecosystem. The glacier runoff data set in the five river source areas of the Qinghai Tibet Plateau covers the period from 2005 to 2010, with a time resolution of every five years. It covers the source areas of the five major rivers in the Qinghai Tibet Plateau (the source of the Yellow River, the source of the Yangtze River, the source of the Lancang River, the source of the Nujiang River, and the source of the Yarlung Zangbo River). The spatial resolution is 1km. Based on multi-source remote sensing, simulation, statistics, and measured data, GIS methods and ecological economics methods are used, The value of water resources service in the cryosphere in the source area of the river and river is quantified, and all its data are subject to quality control.
2022-09-13
In recent years, with the acceleration of the melting of the Antarctic ice sheet, a large amount of ice melt has formed on the surface of the ice sheet from 2000 to 2019. It is of great significance to study the material balance of the Antarctic ice sheet to deeply understand the spatial-temporal distribution and dynamic changes of the melt water on the Antarctic ice sheet. This data set is based on Landsat7 and landsat8 images with 30 m spatial resolution from 2000 to 2019. By using normalized water body index, Gabor filtering and morphological path opening operations, the ice melt grid data set is generated, and the grid water body mask is converted into vector data in ArcGIS. This data set is based on the 250m ice surface melt water data set of the Antarctic ice sheet melting area (Alexander Island, Antarctic Peninsula) from 2000 to 2019 extracted from Landsat images. The time is concentrated from December to February (Southern Hemisphere summer)
2022-09-02
This product provides the monthly runoff, evapotranspiration and soil water of major Arctic river basins in 2018-2065 based on the land surface model Vic. The spatial accuracy is 10km. Major Arctic river basins include Lena, Yenisey, ob, Kolyma, Yukon and Mackenzie basins. According to the rcp2.6 (low emission intensity) and rcp8.5 (high emission intensity) scenario results provided by the ipsl-cm5a-lr model in cmip5 in the fifth assessment report of IPCC, the future climate scenario driving data applicable to the Arctic region of 0.1 ° is obtained through statistical downscaling. Using the calibrated land surface hydrological model Vic on a global scale, based on the future climate scenario driven data of 0.1 °, the monthly time series of runoff, soil water and evapotranspiration of the Arctic River Basin in the middle of this century under future climate change are estimated.
2022-09-01
(1) Data content: data set of Antarctic sea ice extent (Northernmost Latitude of Sea Ice Edge (NLSIE) [°N]) in the past 200 years; (2) Data source and processing method: the data is generated based on the statistical model using six annual resolution proxies (ice core MSA, accumulation rate, etc.); (3) Data quality description: annual resolution; Areas: Indian and western Pacific sector of the Southern Ocean (50 ° – 150 ° E, indwpac), Ross Sea (160 ° E – 140 ° W, RS), Amundsen Sea (90 ° – 140 ° W, as), Bellingshausen Sea (50 ° – 90 ° W, BS), Weddell Sea (50 ° W – 20 ° E, WS); (4) It can be used to study the interdecadal variability of Antarctic sea ice.
2022-08-30
The dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
2022-08-29
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%.
2022-08-17
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%.
2022-08-17
Firstly, the freeze thaw index is calculated by using the resampled crunep data, and then the permafrost area of circum-Arctic is predicted by the frozen number model after snow depth correction. The simulated pan Arctic permafrost area from 2000 to 2015 is 19.96 × 106 km2。 Places inconsistent with the distribution of Pan Arctic permafrost provided by the existing international snow and Ice Data Center are mainly located in island permafrost areas.
2022-08-08
According to the inducing factors of potential thermal melting disasters (mainly thermal melting landslides) in the pan Arctic, including temperature (freezing and Thawing Environment), rainfall, snow cover, soil type, topography and landform, and underground ice content, based on the basic data provided by the big data resource database of the earth, machine learning methods (logic regression, random forest, artificial neural network, support vector machine, etc.) are adopted, and the currently interpreted thermal melting landslides in the northern hemisphere are taken as training samples, Finally, the zonation map of thermal melt disaster susceptibility (occurrence probability) in the pan Arctic was obtained. According to the sensitivity of driving factors, it is found that climate factors (temperature and rainfall) have the largest contribution to the occurrence and distribution of thermal melt disasters, followed by slope factors, and ice content and radiation also have a high contribution.
2022-08-08
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