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%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
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%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
The Qinghai Tibet Plateau is known as the "Asian water tower", and its runoff, as an important and easily accessible water resource, supports the production and life of billions of people around, and supports the diversity of ecosystems. Accurately estimating the runoff of the Qinghai Tibet Plateau and revealing the variation law of runoff are conducive to water resources management and disaster risk avoidance in the plateau and its surrounding areas. The glacier runoff segmentation data set covers the five river source areas of the Qinghai Tibet Plateau from 1971 to 2015, with a time resolution of year by year, covering the five river source areas of 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), and the spatial resolution is the watershed. Based on multi-source remote sensing and measured data, it is simulated using the distributed hydrological model vic-cas coupled with the glacier module, The simulation results are verified with the measured data of the station, and all the data are subject to quality control.
WANG Shijin
The dataset includes three high-resolution DSM data as well as Orthophoto Maps of Kuqionggangri Glacier, which were measured in September 2020, June 2021 and September 2021. The dataset is generated using the image data taken by Dajiang Phantom 4 RTK UAV, and the products are generated through tilt photogrammetry technology. The spatial resolution of the data reaches 0.15 m. This dataset is a supplement to the current low-resolution open-source topographic data, and can reflect the surface morphological changes of Kuoqionggangri Glacier from 2020 to 2021. The dataset helps to accurately study the melting process of Kuoqionggangri Glacier under climate change.
LIU Jintao
The dataset contains microbial amplicon sequencing data from a total of 269 ice samples collected from 15 glaciers on the Tibetan Plateau from November 2016 to August 2020, including 24K Glacier (24K), Dongkemadi Glacier (DKMD), Dunde Glacier (DD), Jiemayangzong Glacier (JMYZ), Kuoqionggangri Glacier (KQGR), Laigu Glacier (LG), Palung 4 Glacier (PL4), Qiangtang 1 Glacier (QT), Qiangyong Glacier (QY), Quma Glacier (QM), Tanggula Glacier (TGL), Xiagangjiang Glacier (XGJ), Yala Glacier (YA), Zepugou Glacier (ZPG), ZhufengDongrongbu Glacier (ZF). The sampling areas ranged in latitude and longitude from 28.020°N to 38.100°N and 86.28°E to 95.651°E. The 16s rRNA gene was amplified by polymerase chain reaction (PCR) using 515F/907R (or 515F/806R) primers and sequenced with the Illumina Hiseq2500 sequencing platform to obtain raw data. The selected primer sequences were "515F_GTGYCAGCMGCCGCGGTAA; 907R_CCGTCAATTCMTTTRAGTTT" "515F_GTGCCAGCMGCCGCGG; 806R_ GGACTACHVGGGTWTCTAAT". The uploaded data include: sample number, sample description, sampling time, latitude and longitude coordinates, sample type, sequencing target, sequencing fragment, sequencing primer, sequencing platform, data format and other basic information. The sequencing data are stored in sequence file data format forward *.1.fq.gz and reverse *.2.fq.gz compressed files.
LIU Yongqin
This dataset consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of freshwater ice as well as its response to climate change.
LI Xingdong, LONG Di, HUANG Qi, ZHAO Fanyu
Lake surface water temperature (LSWT) at Xiashe station from 1967 to 2020; Lake ice depth and lake ice duration at Xiashe station from 1994 to 2020; Runoff at Buha station from 1956 to 2020; Lake level at Xiashe station from 1956 to 2020; Lake area from 1956 to 2020 estimated from the correlation constructed between lake area derived from Landsat images and lake level from gauge measurements in 2001−2020; Air temperature (T) at Gangcha station from 1958 to 2019; Precipitation (P) at Gangcha station from 1958 to 2019
ZHANG Guoqing
Based on gipl1.0 permafrost spatial distribution model, combined with the existing basic data, including climate change, soil types, and vegetation data, the permafrost and seasonal permafrost characteristics of Sichuan Tibet railway are simulated. The data result is 500m spatial resolution grid, including the maximum depth of permafrost and the maximum freezing depth of seasonal permafrost. The results are verified by drilling data. The data date is 2001-20192041-20602081-2100 (20-year average), in which the water body and glacier area are excluded from the calculation range through the mask (null value). The climate data is monthly mean, other data remain unchanged in the process of simulation, and the spatial resolution is 500m. Data sources and "woeldc" lim:https :// www.worldclim.org/ , DEM and vegetation soil: https://data.tpdc.ac.cn/zh-hans/ ”According to the characteristics of different data sources, the authenticity and consistency of the original data are checked and standardized; The permafrost model is used to simulate the permafrost and seasonal frozen soil. The output results are ground temperature and active layer (maximum frozen depth). The simulation results are verified with the borehole ground temperature. Finally, the spatial data set is mapped by ArcGIS. Make digital processing operation standard. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. The data can provide necessary data support for the later research on the freezing (thawing) depth of the corridor of Sichuan Tibet project.
YIN Guoan
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
WANG Junbo
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CAO Guangzhen
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).
HUANG Xiaodong
The glacial change trend in the Tarim River Basin and its impact on water resources change belong to the National Natural Science Foundation of China's Western Environment and Ecological Science major research project. The time is 2003.1-2005.12. The project submitted data: Kochikarbachi Glacier Observation Data (excel): Including precipitation, wind direction, wind speed and temperature data 1.3300a_climate (2003.6.29-2004.6.22): 4 hours data during the day, including field date, time, wind speed, wind up, temperature. 2.4200b_climate (2004.1.29-2004.5.12): 6:00, 8:00, 9:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00, 22: 00, 23:00 observation data, including field date, time, wind speed, wind up, temperature. 3.3700_Precipitation: 13 days daily precipitation from 2003.7 to 2005.9 4.4200_Precipitation: 18-day daily precipitation between 2003.7 and 2006. 6
LIU Shiyin
China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009), is derived from the decision tree classification using passive microwave remote sensing SSM / I brightness temperature data. This data set uses the EASE-Grid projection method (equal cut cylindrical projection, standard latitude is ± 30 °), with a spatial resolution of 25.067525km, and provides daily classification results of the surface freeze-thaw state of the main part of mainland China. The data set is stored by year and consists of 23 folders, from 1987 to 2009. Each folder contains the day-to-day surface freeze-thaw classification results for the current year. It is an ASCII file with the naming rule: SSMI-frozenYYYY ***. Txt, where YYYY represents the year and *** represents the Julian date (001 ~ 365 / 366). The freeze-thaw classification result txt file can be opened and viewed directly with a text program, and can also be opened with ArcView + Spatial Analyst extension module or Arcinfo's Asciigrid command. The original frozen and thawed surface data was derived from daily passive microwave data processed by the National Snow and Ice Data Center (NSIDC) since 1987. This data set uses EASE-Grid (equivalent area expandable earth grid) as a standard format . China's surface freeze-thaw long-term sequence data set-The decision tree algorithm (1987-2009) attributes consist of the spatial-temporal resolution, projection information, and data format of the data set. Spatio-temporal resolution: the time resolution is day by day, the spatial resolution is 25.067525km, the longitude range is 60 ° ~ 140 ° E, and the latitude is 15 ° ~ 55 ° N. Projection information: Global equal-area cylindrical EASE-Grid projection. For more information about EASE-Grid projection, see the description of this projection in data preparation. Data format: The data set consists of 23 folders from 1987 to 2009. Each folder contains the results of the day-to-day surface freeze-thaw classification of the year, and is stored as a txt file on a daily basis. File naming rules: For example, SMI-frozen1994001.txt represents the surface freeze-thaw classification results on the first day of 1994. The ASCII file of the data set is composed of a header file and a body content. The header file consists of 6 lines of description information such as the number of rows, the number of columns, the coordinates of the lower left point of the x-axis, the coordinates of the lower left point of the y-axis, the grid size, and the value of the data-less area. Array, with columns as the priority. The values are integers, from 1 to 4, 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. Because the space described by all ASCII files in this data set is nationwide, the header files of these files are unchanged. The header files are extracted as follows (where xllcenter, yllcenter and cellsize are in m): ncols 308 nrows 166 xllcorner 5778060 yllcorner 1880060 cellsize 25067.525 nodata_value 0 All ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the header file, the main content is a numerical representation of the surface freeze-thaw state: 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. If you want to display it with an icon, we recommend using ArcView + 3D or Spatial Analyst extension module to read it. During the reading process, a grid format file will be generated. The displayed grid file is the graphic representation of the ASCII code file. Reading method: [1] Add 3D or Spatial Analyst extension module in ArcView software, and then create a new View; [2] Activate View, click the File menu, select the Import Data Source option, the Import Data Source selection box pops up, select ASCII Raster in Select import file type: in this box, and a dialog box for selecting the source ASCII file automatically pops up Find any ASCII file in the data set and press OK; [3] Type the name of the Grid file in the Output Grid dialog box (a meaningful file name is recommended for later viewing), and click the path where the Grid file is stored, press Ok again, and then press Yes (to select an integer) Data), Yes (call the generated grid file into the current view). The generated file can be edited according to the Grid file standard. This completes the process of displaying the ASCII file as a Grid file. [4] During batch processing, you can use ARCINFO's ASCIIGRID command to write an AML file, and then use the Run command to complete in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}
LI Xin
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