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, LIU Yan
This data set includes the original Landsat satellite image data TM and ETM data of Salt Lake region of Tibet Autonomous Region from January to December 2002, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 15m and 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 10.11gb.
CHEN Liang, WANG Jianping
This data set includes the original Landsat satellite image data TM and ETM data of Salt Lake area in Qinghai Province from January to December 2002, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 15m and 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 3.18GB.
CHEN Liang, WANG Jianping
This data set includes the original Landsat satellite image data TM and ETM data of Salt Lake region of Tibet Autonomous Region from 1991 to 1992, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 15m and 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 5.61MB.
CHEN Liang, WANG Jianping
This data set includes the original Landsat satellite image data TM and ETM data of Salt Lake area of Qinghai Province in 1992, 1977 and 1993, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 15m and 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 2.66gb.
CHEN Liang, WANG Jianping
This data set includes the original Landsat satellite image data TM and ETM data of Salt Lake region of Tibet Autonomous Region. The year is 1977, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 15m and 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 1.30gb.
CHEN Liang, WANG Jianping
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, LIU Yan
This data set includes the original Landsat satellite image data of Qinghai Salt Lake area in 1976, 1977 and 1978, including 4 bands of MSS sensor (spatial resolution 78m), 7 bands of TM sensor and 8 bands of ETM sensor (spatial resolution 30m). The data is based on MSS, TM, ETM and oli remote sensing image data of Salt Lake area collected and sorted on USGS official website. Strict quality assurance measures are taken in the data processing process. The data is warehoused after quality inspection, which can ensure the data quality. The data size is about 700MB.
CHEN Liang, WANG Jianping
Lidar, multispectral and thermal infrared data are important observation data in the research fields of hydrology, ecology and environmental monitoring. This data set is the observation data of UAV in the integrated observation experiment of heaven and earth in the middle reaches of Heihe River Basin in 2020. The data set includes UAV remote sensing data from August 16 to 21, 2020. The UAV platform is Dajiang Phantom 4-multispectral version. Including lidar data of Daman superstation (August 16-21), Huazhaizi station (August 19) and Wetland station (August 21). The laser scanning system is Tovos DroneScan, with scanning frequency of 300000 points/s, point density of 100 points/m2 and scanning accuracy of 5 cm; Multispectral data of Daman superstation (August 18), Huazhaizi station (August 19) and Wetland station (August 21). The data set includes five bands of images, namely blue (450nm ± 16nm), green (560nm ± 16nm), red (650nm ± 16nm), red edge (730nm ± 16nm) and near infrared (840nm ± 26nm); And NDVI and reflectance data products corresponding to Wetland station and Huazhaizi station. The spatial resolution of the above data is about 0.2 m; In addition, it also includes the thermal infrared data of Huazhaizi station (August 18 and 19) and Wetland station (August 21). The wavelength range of thermal infrared channel is 7.5-13.5 μm. Imaging system sensitivity (nedt) < 50 mk, maximum frame rate: 30Hz, scene range (high gain): 640 × 512: -25° to 135℃, 336 × 256: -25° to 100℃, scene range (low gain): -40° to 550℃.
JIN Rui
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, ZHANG Yinsheng
The observation data set of L-band ground-based microwave radiometer within Shandian River Basin collects the experimental data of ground-based L-band mobile observation carried out by Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences in Shandian River Basin in September 2018. The L-band microwave radiometer is installed on the lifting platform of Changchun Jingyuetan remote sensing vehicle, and the platform is raised to 5M for dual polarization multi angle observation. The host system of the microwave radiometer system directly stores the data as a .dat file, which can be read and processed by Excel or MATLAB. The collected data has been sorted into excel form.This data can be used to study the inversion method of soil moisture.
JIANG Tao, ZHENG Xingming, LI Xiaojie
This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.
ZHENG Xingming, JIANG Tao
This data set was collected in 2018 during the ground-based microwave radiometry and radar cooperative experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Zhenglan Banner, Inner Mongolia (115.93° E, 42.04° N, at 1362 m in altitude). The data set contains four parts, namely brightness temperature data, radar backscatter coefficient, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data was acquired every 30 minutes from 30° to 65° with an interval of 2.5°. The active microwave data is obtained by ground-based synthetic aperture radar (GBSAR), including the L- and C-band backscattering coefficients under four polarization modes (VV, VH, HH, HV), and the incidence varies from 30° to 65° (2.5° interval). The soil data contains the surface roughness, soil moisture and temperature at six depths of layer (1 cm, 3 cm, 5 cm, 10 cm, 20 cm, 50 cm). The vegetation data is mainly the vegetation water content of the grassland. The experimental period lasted from August 18 to September 25, 2018, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, ZHAO Tianjie, ZHAO Tianjie, HU Lu, GENG Deyuan, SHI Jiancheng
The dataset is the Landsat enhanced vegetation index (EVI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the EVI equation which is added backgroud adjusted parameters C1 and C2, and atmospheric adjusted parameter L based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow. Compared with NDVI, EVI has stronger ability to resist atmospheric interference and noise,so it is more suitable for weather conditions with high aerosol content and lush vegetation areas.
PENG Yan
The dataset is the normalized difference moisture index (NDMI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDMI equation which use the difference ratio between the NIR band and SWIR2 band to quantitatively reflect the water content of vegetation canopy .And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDMI is highly correlated with canopy water content and can be used to estimate vegetation water content, and it is also used to analyze the change of land surface temperature because it is strongly correlated with land surface temperature.
PENG Yan
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
The dataset is the soil adjusted vegetation index (SAVI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the SAVI equation which is added soil adjusted parameters S based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.SAVI is stable in the sparse vegetation area, but is not sensitive in the dense vegetation area .
PENG Yan
This data set was collected in summer 2017 during the ground-based microwave radiometry experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Duolun County, Inner Mongolia (116.47°E, 42.18°N, at 1269 m in altitude). The data set contains three parts, namely brightness temperature data, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data were acquired from 30° to 65° with an interval of 2.5°, and the time resolution is 0.5 hours. Soil data contains 5 layers of soil moisture and soil temperature (2.5 cm, 10 cm, 20 cm, 30 cm, 50 cm) over three croplands (corn, oats, and buckwheat), with sampling intervals of 10 minutes. The soil data also contains soil surface roughness, rainfall, irrigation flags, and soil texture. Vegetation data contains leaf area index, plant height, vegetation water content, etc. The experimental period lasted from July 19 to August 30, 2017, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, HU Lu, LI Shangnan, FAN Dong, WANG Pingkai, GENG Deyuan, SHI Jiancheng
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
