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
Global solar radiation and diffuse horizontal solar radiation at Dome C (Antarctica) are measured by radiation sensors (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground are obtained from the IPEV/PNRA Project “Routine Meteorological Observation at Station Concordia”, http://www.climantartide.it. This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Lanconelli, C.; Lupi, A.; Driemel, A.; Vitale, V.; Li, K.; Song, T. 2022. Long-Term Variations of Global Solar Radiation and Its Potential Effects at Dome C (Antarctica). Int. J. Environ. Res. Public Health, 19, 3084. https://doi.org/10.3390/ijerph19053084). The observed global solar radiation and meteorological parameters are available at https://doi.org/10.1594/PANGAEA.935421. The data set can be used to study solar radiation and its attenuation at Dome C, Antarctica.
BAI Jianhui
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
BAI Jianhui
Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
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
Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
Aiming at the 179000 km2 area of the pan three rivers parallel flow area of the Qinghai Tibet Plateau, InSAR deformation observation is carried out through three kinds of SAR data: sentinel-1 lifting orbit and palsar-1 lifting orbit. According to the obtained InSAR deformation image, it is comprehensively interpreted in combination with geomorphic and optical image features. A total of 949 active landslides below 4000m above sea level were identified. It should be noted that due to the difference of observation angle, sensitivity and observation phase of different SAR data, there are some differences in the interpretation of the same landslide with different data. The scope and boundary of the landslide need to be corrected with the help of ground and optical images. The concept of landslide InSAR recognition scale is different from the traditional spatial resolution and mainly depends on the deformation intensity. Therefore, some landslides with small scale but prominent deformation characteristics and strong integrity compared with the background can also be interpreted (with SAR intensity map, topographic shadow map and optical remote sensing image as ground object reference). The minimum interpretation area can reach several pixels. For example, a highway slope landslide with only 4 pixels is interpreted with reference to the highway along the Nujiang River.
YAO Xin
This data is a high-resolution soil freeze/thaw (F/T) dataset in the Qinghai Tibet Engineering Corridor (QTEC) produced by fusing sentinel-1 SAR data, AMSR-2 microwave radiometer data, and MODIS LST products. Based on the newly proposed algorithm, this product provides the detection results of soil F/T state with a spatial resolution of 100 m on a monthly scale. Both meteorological stations and soil temperature stations were used for results evaluation. Based on the ground surface temperature data of four meteorological stations provided by the national meteorological network, the overall accuracy of soil F/T detection products achieved 84.63% and 77.09% for ascending and descending orbits, respectively. Based on the in-situ measured 5 cm soil temperature data near Naqu, the average overall accuracy of ascending and descending orbits are 78.58% and 76.66%. This high spatial resolution F/T product makes up traditional coarse resolution soil F/T products and provides the possibility of high-resolution soil F/T monitoring in the QTEC.
ZHOU Xin , LIU Xiuguo , ZHOU Junxiong , ZHANG Zhengjia , CHEN Qihao , XIE Qinghua
The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
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.
LIU Junguo
Photosynthetically active radiation (PAR) is fundamental physiological variable driving the process of material and energy exchange, and is indispensable for researches in ecological and agricultural fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global grided PAR dataset with an effective physical-based PAR model. The main inputs were cloud optical depth from the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, the routine variables (water vapor, surface pressure and ozone) from the ERA5 reanalysis data, aerosol from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products and albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) product after 2000 and CLARRA-2 product before 2000. The grided PAR products were evaluated against surface observations measured at seven experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). The instantaneous PAR was validated at the SURFRAD and NEON, and the mean bias errors (MBEs) and root mean square errors (RMSEs) are 5.6 W m-2 and 44.3 W m-2, and 5.9 W m-2 and 45.5 W m-2, respectively, and correlation coefficients (R) are both 0.94 at 10 km scale. When averaged to 30 km, the errors were obviously reduced with RMSEs decreasing to 36.3 W m-2 and 36.3 W m-2 and R both increasing to 0.96. The daily PAR was validated at the SURFRAD, NEON and CERN, and the RMSEs were 13.2 W m-2, 13.1 W m-2 and 19.6 W m-2, respectively at 10 km scale. The RMSEs were slightly reduced to 11.2 W m-2, 11.6 W m-2, and 18.6 W m-2 when upscaled to 30 km. Comparison with the other well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES) reveals that our PAR product was a more accurate dataset with higher resolution than the CRERS. Our grided PAR dataset would contribute to the ecological simulation and food yield assessment in the future.
TANG Wenjun
Based on the data of Gaogao No. 1 and No. 2 in China from 2019 to 2020, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermal melting lakes and ponds and the distribution data of thermal melting landslides. The data set can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan Anduo section of Qinghai Tibet highway is self-made based on the domestic gaogao-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from Gaogao No. 2 data; Then, ArcGIS is used for manual editing. During the production process, the operators are required to strictly abide by the operation specifications, and a special person is responsible for the quality review.
NIU Fujun, LUO Jing
Surface melting is the primary reason that affects the mass balance of Greenland ice sheet. At the same time, ice and snow have high albedo, and ice sheet surface melting will cause the difference of radiation energy budget, and then affects the energy exchange between sea-land-air. The high-resolution ice sheet surface melting product provides important information support for the study of Greenland ice sheet surface melting and its response to global climate change. This dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Greenland ice sheet surface melt daily product for 1985, 2000, 2015 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Greenland ice sheet, and the data set is stored as *.nc.
WEI Siyi,
This data includes the land cover data of Central Asia, South Asia and Indochina Peninsula in the from 1992 to 2020 with a spatial resolution of 300mLand cover data includes 10 primary categories, which are combined from the secondary categories of the original data. The data source is the surface coverage product CCI-LC of ESA, where the spatial distribution of cropland, built-up land, and water for the land cover data from 1992 to 2020. Combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 500 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), the training sample dataset of land cover interpretation were built from the consistent areas of multiple products. The Google Earth Engine and random forest algorithm were used to correct the cropland, built-up land, and water of temporal CCI-LC data. Using the high resolution images in Google Earth at 2019 and 2020, the accuracy of change areas of cropland, built-up land, and water was validated by the stratified random sampling. A total of 3,600 land parcels were selected from 1,200 land parcels of the three land cover types, indicating that the accuracy of our corrected product increased in the range of 11% to 26% for the change areas compared to the CCI-LC product.
XU Erqi
Snow, ice, and glaciers have the highest albedo of any part of Earth's surface. The increase in melting of the polar ice sheet results in a rapid and sequential decrease in albedo and subsequently influences the global energy balance. The hydrological system derived from surface melt and basal meltwater will affect the dynamic stability of ice sheet and therefore mass balance. The dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Antarctic ice sheet surface melt daily product for 1985-1986, 2000-2001, 2015-2016 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. It better reflects the melting gradient of mountainous area, groundline area and ice shelf over time, DSSMIS has a higher accuracy. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Antarctic ice sheet, and the data set is stored as *.nc.
WEI Siyi,
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.
YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng
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.
PENG Yan
Data content: The data set products include impervious surface products with a resolution of 10 meters in the Qinghai-Tibet Plateau, which can be used as a key parameter for related research on the Qinghai-Tibet Plateau ecosystem. Data source and processing method: Product inversion is mainly based on Sentinel series data, considering joint features, combining depth spatial features, long-time NDVI and other exponential features, and topographic features, and using random forest model to achieve impervious surface information extraction. Data quality: The overall accuracy is high. Data application results and prospects: The data set will be continuously updated and can be used to further clarify the impact of human activities on the ecosystem of the Qinghai-Tibet Plateau.
WANG Guizhou
Data content: The data set products include impervious surface products with a resolution of 30 meters in the Qinghai-Tibet Plateau, which can be used as a key parameter for related research on the Qinghai-Tibet Plateau ecosystem. Data source and processing method: Product inversion is mainly based on Landsat series data, considering joint features, combining depth spatial features, long-time NDVI and other exponential features, and topographic features, and using random forest model to achieve impervious surface information extraction. Data quality: The overall accuracy is high, better than 80% in most areas. Data application results and prospects: The data set will be continuously updated and can be used to further clarify the impact of human activities on the ecosystem of the Qinghai-Tibet Plateau.
WANG Guizhou
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