Glacial lake inventory from 1977-2017, based on Landsat MSS/TM/ETM+/OLI imagery, uses a semi-automatic water body classification method to distinguish between water body and non-water body information, then extracts the lake boundaries and visually checks and manually edits them by comparison with the original Landsat images. The MSS sensor data was used in 1977 with a resolution of 60 m. Image data used after 1987 had a resolution of 30 m.The relationship between glacial meltwater and glacial lake recharge was determined from RGI 6.0 and Google Earth.
KHADKA Nitesh , ZHANG Guoqing
The data files include the extent of the Poqui watershed and multiple periods of glacial lake cataloguing. The glacial lake extent in 1964, was obtained using manual outlining methods based on geo-corrected KH-4 data; the glacial lake extent during 1976-2017, based on Landsat MSS/TM/ETM+/OLI imagery, used a semi-automatic water body classification method to distinguish between water body and non-water body information, and then extracted lake boundaries, and visually checked and manually edited by comparison with the original Landsat images.The relationship between glacial meltwater and glacial lake recharge was determined from RGI 6.0 glacier catalogues and Google Earth.
ZHANG Guoqing
The global annual lake ice phenological dataset includes the freeze-up date, break-up date for 74,245 lakes in the northern hemisphere. The dataset is divided into three parts: 1) current data, obtained from MODIS productions through a DLRM model (with parameters provided), covering the period of 2001 to 2020; 2) historical and 3) future simulation data, obtained from the temperature-based lake-specific models, for the periods of 1861-2005 and 2006-2099, respectively. The historical and future simulations were only performed for 30,063 lakes that meet the model conditions and are presented in the dataset.
WANG Xinchi
This data set provides the lake ice phenology of 71 lakes on the Qinghai Tibet Plateau from 2001 to 2020, including the freeze-up start, freeze-up end, break-up start, break-up end, complete freezing duration, and ice cover duration. The data set was extracted from the cloud-gap-filled MODIS daily snow product based on a dynamic changed threshold. Compared with the coarse resolution passive microwave AMSR-E/2 lake ice phenology data set, the average absolute error of the freeze-up start date was 2.33-7.25 days, and the average absolute error of the break-up end date was 1.75-4.67 days. The data can provide a data basis for the relevant research on the response of the Qinghai Tibet Plateau lake system to climate change.
CAI Yu, KE Changqing
The extraction of glacier surface movement is of great significance in the study of glacier dynamics and material balance changes. In view of the shortcomings of the current application of autonomous remote sensing satellite data in glacier movement monitoring in China, the SAR data covering typical glaciers in alpine areas of the Qinghai Tibet Plateau from 2019 to 2020 obtained under the GF-3 satellite FSI mode was used to obtain the glacier surface velocity distribution in the study area with the help of a parallel offset tracking algorithm. With its good spatial resolution, GF-3 image has significant advantages in extracting glacier movement with small scale and slow movement, and can better reflect the details and differences of glacier movement. This study is helpful to analyze the movement law and spatio-temporal evolution characteristics of glaciers in the Qinghai Tibet Plateau under the background of climate change.
YAN Shiyong
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.
JIANG Liming JIANG Liming JIANG Liming
Glaciers are sensitive to climate change. With global warming, the melting of glaciers continues to accelerate all over the world. Surging glaciers are glaciers with intermittent and periodic acceleration, which is a sensitive indicator of climate change. Based on Landsat and Sentinel satellite images from 1980s to 2020, the study area images were obtained by filtering, stitching, and cropping. Among them, the L1GS level images collected by Landsat TM sensor were geo-registered using a second-order polynomial, and the error of the geo- registered images was less than one pixel. After image template matching with an orientation correlation algorithm, this data set provides the surface ice flow velocity of a typical surging glacier in the Greenland ice sheet, Sortebræ Glacier in different period from 1980s to 2020. It is expected to contribute to the research on the surging process of Sortebræ Glacier and the discussion on the mechanism of glacier surging in the context of global warming.
QIAO Gang , SUN Zixiang , YUAN Xiaohan
Snow cover is an important component of the cryosphere and an indispensable variable in the scientific research of global change and Earth system. The distribution range and phenological information of snow cover are important indicators to measure the variation characteristics of snow cover, and also important parameters for snow melting runoff simulation in the hydrological model of cold regions. The High Mountain Asia is the source of many international rivers, and also the hot spot of global climate change research; The ecological and environmental problems caused by the change of ice and snow in the region, such as the reduction of water resources, the increase of extreme weather events, and the frequent occurrence of disasters, have attracted extensive attention from all countries. Therefore, it is very important for climate change research, water resources management, disaster early warning and prevention to accurately obtain long-term snow distribution and snow phenology data in High Mountain Asia . The daily cloudless MODIS normalized snow cover index (NDSI) product (2000-2021500 m) in the High Mountain Asia is based on the MODIS daily snow cover product (including Terra Morning Star data product MOD10A1 and Aqua Afternoon Star data product MYD10A1, C6 versions), and is processed by the same day afternoon star data fusion and cubic spline interpolation cloud removal algorithm; Among them, when there was only Morningstar data product MOD10A1 from 2000 to 2002, the cubic spline interpolation algorithm was directly used for cloud removal. The snow cover phenological data set for hydrological years 2002-2020 is prepared based on cloudless MODIS NDSI products in hydrological years, including three parameters: snow onset date (SOD), snow end date (SED) and snow duration days (SDD). This data set has reliable accuracy.
TANG Zhiguang , DENG Gang
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
As an important part of the global carbon pool, Arctic permafrost is one of the most sensitive regions to global climate change. The rate of warming in the Arctic is twice the global average, causing rapid changes in Arctic permafrost. The NDVI change data set of different types of permafrost regions in the Northern Hemisphere from 1982 to 2015 has a temporal resolution of every five years, covers the entire Arctic Rim countries, and a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, GIS method and ecological method are used to quantify the regulation and service function of permafrost in the northern hemisphere to the ecosystem, and all the data are subject to quality control.
WANG Shijin
This dataset contains the glacier outlines in Qilian Mountain Area in 2015. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2018 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This dataset contains the glacier outlines in Qilian Mountain Area in 2021. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2021 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2021, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
Hourly spatially complete land surface temperature (LST) products have a wide range of applications in many fields such as freeze-thaw state monitoring and summer high temperature heat wave monitoring. Although the LST retrieved from thermal infrared (TIR) remote sensing observations has high accuracy, it is spatially incomplete due to the influence of cloud, which heavily limits the application of LST. LST simulated by land surface models (LSM) is with high temporal resolution and spatiotemporal continuity, while the spatial resolution is relatively coarse and the accuracy is poor. Therefore, fusing the remote sensing retrieved LST and the model simulated LST is an effective way to obtain seamless hourly LST. The authors proposed a fusion method to generate 0.02° hourly seamless LST over East Asia and produced the corresponding data set. This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ verification results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively. The time period is 2016-2021 over (0-60°N, 80°E-140°E).
CHENG Jie, DONG Shengyue, SHI Jiancheng
Using the analytic hierarchy process, this study selects eight assessment factors and assesses the comprehensive quality of eight glacier inventories of the TPR by grading. By merging products of the eight glacier inventories in terms of best-quality assessment units, A new glacier inventory product of best comprehensive quality was derived for the entire TPR. The new product greatly promotes the quality of a single glacier inventory for the entire TPR. The database provides: (1) glacier information inherited from the original inventories including longitude, latitude, area, elevation, slope, aspect, acquisition time of the remote-sensing data; (2) information of the assessment including normalized index values of the eight assessment factors, integrated index values and grades of scenes for all of the eight inventories, which is recorded in terms of individual glaciers. It would meet the needs not only for the potential users who need to know the best glacier inventory of comprehensive quality of a region, but also for those who prefer to get information on a single quality or factor (e.g. seasonal snowcover) of a given glacier inventory.
HE Xia , ZHOU Shiqiao
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
Funded by the National Key R&D Program "Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", "Multi-scale Observation and Data Product Development of Key Cryosphere Parameters", Changes and impacts of glaciers, snow cover and permafrost and how to deal with them (Grant NO.2019QZKK0201), and Pan-tertiary environmental change and the construction of green silk road (Grant NO.XDA20000000), the research group of Zhang Yinsheng, Institute of Qinghai-Tibet Plateau, Chinese Academy of Sciences developed downscaled snow water equivalent products in the Qinghai-Tibet Plateau. The sub-pixel space-time decomposition algorithm was used to downscale the 0.05° daily snow depth data set (2000-2018) over the Qinghai-Tibet Plateau. And the snow depth depletion model was used to supplement the estimation of the snow depth value in the shallow snow area that cannot be detected by passive microwave remote sensing. Finally, based on the snow density grid data, the snow depth data is converted into snow water equivalent data.
YAN Dajiang, ZHANG Yinsheng
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
Based on long-term series Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products, daily snow cover products without data gaps at 500 m spatial resolution over the Tibetan Plateau from 2002 to 2021 were generated by employing a Hidden Markov Random Field (HMRF) modeling technique. This HMRF framework optimally integrates spectral, spatiotemporal, and environmental information together, which not only fills data gaps caused by frequent clouds, but also improves the accuracy of the original MODIS snow cover products. In particular, this technology incorporates solar radiation as an environmental contextual information to improve the accuracy of snow identification in mountainous areas. Validation with in situ observations and snow cover derived from Landsat-8 OLI images revealed that these new snow cover products achieved an accuracy of 98.31% and 92.44%, respectively. Specifically, the accuracy of the new snow products is higher during the snow transition period and in complex terrains with higher elevations as well as sunny slopes. These gap-free snow cover products effectively improve the spatiotemporal continuity and the low accuracy in complex terrains of the original MODIS snow products, and is thus the basis for the study of climate change and hydrological cycling in the TP.
HUANG Yan , XU Jianghui
1. Glacial lake data sets (1960s−2020) This data set contains glacial lake data for the 1960s, 2016, 2017, 2018, 2019, and 2020, mapped from Korona KH-4, Sentinel-2, and Sentinel-1 imagery. 2. Potential Outburst Flood Hazard level of Bhutanese glacial lakes This data contains the Potential Outburst Flood Hazard level of Bhutanese glacial lakes with an area greater than 0.05 km2 (n=278). The value for each hazard assessment criteria is also provided in the data attributes.
RINZIN Sonam, ZHANG Guoqing
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,
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,
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
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
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
Kilometer-level spatially complete (seamless) land surface temperature products have a wide range of applications needs in climate change and other fields. Satellite retrieved LST has high reliability. Integrating the LST retrieved from thermal infrared and microwave remote sensing observation is an effective way to obtain the SLT with certain accuracy and spatial integrity. Based on this guiding ideology, the author developed a framework for retrieving 1km and seamless LST over China landmass, and generated the LST data set accordingly (2002-2020) Firstly, a look-up table based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled by using geographic weighted regression to obtain 1km LST. Finally, the multi-scale Kalman filter is used to fuse AMSR-E/AMSR2 LST and MODIS LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.
CHENG Jie, DONG Shengyue, SHI Jiancheng
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST – Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chengyan, MU Mei, PENG Xiaoqing, WU Xiaodong
Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently lunched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, but the short temporal coverage of the data records has limited its applications in long-term studies. While Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This dataset contains 20 years (2002-2022) global spatio-temporal consistent surface soil moisture. The resolution is 36 km at daily scale, the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. This dataset can reproduce the spatial and temporal distribution of SMAP soil moisture, with comparable accuracy as SMAP soil moisture product. This dataset also compares well with in situ SSM observations at 14 dense validation networks globally, with accuracy of 5% volumetric water content, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable though the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.
YAO Panpan, LU Hui
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
High Asia is very sensitive to climate change, and is a hot area of global change research. The changes of temperature and precipitation will be reflected in the freezing and thawing time of ice and snow. Satellite microwave remote sensing can provide continuous monitoring ability of ice and snow surface state in time and space. When a small part of ice and snow begins to melt, micro liquid water will also be reflected in active and passive microwave remote sensing signals. In the microwave band, the dielectric constant of ice and liquid water is very different, so it provides a basic theory for the microwave remote sensing monitoring of ice and snow melting. In the case of passive microwave, when ice and snow begin to melt and liquid water appears, its absorption and emissivity increase rapidly, so its emissivity, brightness temperature and backscatter coefficient will also change rapidly. This data set is the initial time of ice and snow melting in the high Asia region retrieved by using the satellite microwave radiometer and scatterometer observations from 1979 to 2018. The passive microwave remote sensing data are SMMR on satellite (1979-1987) and SSM / i-ssmis radiometer on DMSP (1988 present). The active microwave remote sensing data is the QuikSCAT satellite scatterometer (2000-2009).
Xiong Chuan, SHI Jiancheng, YAO Ruzhen, LEI Yonghui, PAN Jinmei
This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia, Li Jia, LI Jia, LI Jia, WANG Yingzheng, LI Jianjiang, LI Xin, LIU Shaomin
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
SHER Muhammad
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
On the basis of RGI6.0, we use remote sensing and geographic information system technology to update the glacier inventory data in Alaska. The updated glacier inventory uses a data source for 2018 Landsat OLI spatial resolution 15m remote sensing image, and the method used is manual interpretation. The results show that the Alaska Glacier inventory includes 27043 glaciers with a total area of 81285km2. The uncertiany of this data is 4.3%. The data will provide important data support for the study of glacier change in Alaska and the regional and global impact of glacier change in the context of global change.
SHANGGUAN Donghui,
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2020. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
DAI Liyun
The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E, AMSR2 passive microwave brightness temperature data and MODIS optical remote sensing data. The freeze-thaw discriminant function algorithm and downscaling algorithm are used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.05° from 2002 to 2017. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.
Zhao Tianjie, ZHANG Ziqian
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
QIU Yubao
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
QIU Yubao
This data set provides daily snow thickness distribution data of China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km.The original data used for the inversion of the snow depth data set came from SMMR (1978-1987), SSM/I (1987-2008) and amsr-e (2002-2012) daily passive microwave bright temperature data processed by the national snow and ice data center (NSIDC).As the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data.The time consistency of bright temperature data is improved by cross calibration of bright temperature of different sensors.Then, based on Chang algorithm, Dr. Che tao is used to carry out snow depth inversion.Refer to the data description document for specific inversion methods.
CHE Tao, LI Xin, DAI Liyun
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1980~2018, 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
CHE Tao, LI Xin, DAI Liyun
This dataset contains the glacier outlines in Qilian Mountain Area in 2015. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2018 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia, Li Jia, LI Jia, LI Jia, WANG Yingzheng, LI Jianjiang, LI Xin, LIU Shaomin
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