The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
WANG Xinchi
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 dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
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 data set was obtained in the Fine scale Optical Remote sensing Experiment of mixed Stand over complex Terrain (FOREST) in Genhe reserve area. The experimental site is located near Genhe Ecological Station (national ecosystem research network of Greater Khingan forest ecosystem in Inner Mongolia). The data acquisition time is 9:00-17:00 (Beijing time) on August 7, 2016, and the sampling interval is 0.5 hours. The measuring equipment is FLIR T440 camera. The brightness temperatures of six components: illumination leaf, shadow leaf, illumination trunk, shadow trunk, illumination background and shadow background were extracted through image supervision classification. After the removal of reflection term contribution and emissivity correction, the radiometric temperatures of six components were finally obtained. This data set is expected to be applied to thermal infrared modeling, component temperature inversion, evapotranspiration estimation and so on.
BIAN Zunjian, CAO Biao, LI Hua, DU Yongming, FAN Wenjie, XIAO Qing, LIU Qinhuo
The soil surface roughness data set measured simultaneously during the Soil Moisture Experiment in the Luan River (SMELR) in 2018, which covers (1) 30 quadrats in the north-south flight region of 70 km ×12 km typical experimental area, and (2) 8 quadrats in the northeast-southwest flight region of 165 km×5 km complex experimental area. The data were measured on September 17, September 18 and September 20, 2018 respectively. The soil surface roughness along the row (East-West) direction and cross the row (North-South) direction of typical features in each sample area were measured. The surface roughness of the dataset is described using three parameters; root mean square height (RMSH) and correlation length (CL). The root mean square height describes the random surface characteristics, while the correlation length and correlation function describe the periodicity of the surface. The surface roughness was calculated through the steps of soil surface height digitization, slope correction, periodic correction, and roughness calculation.
GUO Peng
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 data source is Copernicus global land service (CGLS), and the download address is:（ https://lcviewer.vito.be/ ）。 This data provides the land cover / land use type of phase I Sichuan Tibet traffic corridor (including G317, G318 and Sichuan Tibet Railway). The land cover data is automatically extracted from remote sensing images. The acquisition time of remote sensing images is from 2015 to 2019. The classification algorithm adopts proba-v, and the classification accuracy is 80%. Land use types include evergreen broad-leaved forest, deciduous broad-leaved forest, evergreen coniferous forest, deciduous coniferous forest, shrub, grassland, landing, entity, construction land, etc. The original spatial resolution of the data is 100m, and the spatial resolution after resampling is 250m. The data geographic coordinate system is wgs1984, and the projection coordinate system is Mercator projection. The data storage format is TIFF file.
SUI Tianbo
The vegetation classification data of Sichuan Tibet traffic corridor is digitized from the 1:4 million vegetation map of China (1979). The digitized data also adopts equal product cone projection and can be converted to other projections by GIS software. The completion time is 1990. This data is area SHP data, and its attributes include vegetation code and its meaning. For details, please refer to the design description of China Vegetation code, which contains a detailed description of the vegetation code of 1:4 million China vegetation map. The data basically includes natural vegetation and agricultural vegetation, and the area is Sichuan Tibet traffic corridor. The development of vegetation roots and whether they like water are important factors affecting the development of geological disasters. The statistical study of the relationship between vegetation types and disasters is of great significance for disaster prevention and reduction, and vegetation types can also be used as key factors for disaster risk assessment.
WANG Lixuan
NDVI of Landsat 5,7,8 images was used to extract the vegetation coverage of Sichuan Tibet transportation corridor from 1985 to 2020. The data is divided into seven periods, each with a time span of five years. From USGS（ https://www.usgs.gov/core-science-systems/nli/landsat/landsat-surface-reflectance ）。 The vegetation coverage can be divided into seven periods, each with a time span of 5 years. The calculation process is mainly divided into three steps: first, obtain all the images with cloud fraction less than 20 in the period, calculate the NDVI value, and synthesize the NDVI map of the current period by using the median synthesis method; The second step is to calculate the maximum and minimum NDVI, ndvimax and ndvimin; The third step is to get the vegetation coverage through the formula FVC = (NDVI ndvimin) / (ndvimax ndvimin). Due to the use of Landsat data, the spatial resolution of the original vegetation coverage product is 30 m, and this data is obtained after sampling 1000 m.
SUI Tianbo
The SMC dataset contains land soil moisture data for Chinese land spanning from 2002 to 2018, the unit is m3/m3, in monthly temporal and 0.05° spatial resolution. More specifically, it is produced by from three passive microwave remote sensing products: the Japan Aerospace Exploration Agency (JAXA)’s Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 soil moisture data, and SMOS product that was developed by the Institut National de la Recherche Agronomique) (INRA) and Centre d’Etudes Spatiales de la BIOsphère (CESBIO) soil moisture data. To overcome the deficiencies of passive microwave soil moisture products with low resolution, we construct a spatially weighted decomposition (SWD) using TVDI that calculated by Moderate Resolution Imaging Spectroradiometer (MODIS) data, including the land surface temperature (LST) MYD11C3 data and the normalized difference vegetation index (NDVI) MYD13C2 data. Overall, the downscaled soil moisture (SM) products were consistent with the in-situ measurements (R>0.78) and exhibited a low root mean square error (ubRMSE < 0.05 m3/m3), which indicates good accuracy throughout the time series. The dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models.
MAO Kebiao
The dataset includes the remote sensing products of normalized vegetation index, vegetation coverage, net primary productivity, grassland biomass, forest volume and vegetation parameters of Heihe River Basin from May 2020 to October 2020, with a spatial resolution of 10m. In this data set, gaofen-1, gaofen-6, sentry, ziyuan-3 and other remote sensing data sources are used, combined with meteorological, ground monitoring and other basic data, and vegetation parameter inversion algorithms and models such as band ratio method, mixed pixel decomposition model, CASA model are used to generate monthly vegetation index remote sensing products of Qilian mountain key point region in growing season. This data set provides data support for the diagnosis and dynamic assessment of regional eco-environmental problems by building a high spatial-temporal resolution eco-environmental monitoring data set based on high resolution satellites.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
The dataset includes the remote sensing products of normalized vegetation index, vegetation coverage, net primary productivity, grassland biomass, forest volume and vegetation parameters of Heihe River Basin from May 2020 to October 2020, with a spatial resolution of 10m. In this data set, gaofen-1, gaofen-6, sentry, ziyuan-3 and other remote sensing data sources are used, combined with meteorological, ground monitoring and other basic data, and vegetation parameter inversion algorithms and models such as band ratio method, mixed pixel decomposition model, CASA model are used to generate monthly vegetation index remote sensing products of Qilian mountain key point region in growing season. This data set provides data support for the diagnosis and dynamic assessment of regional eco-environmental problems by building a high spatial-temporal resolution eco-environmental monitoring data set based on high resolution satellites.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
It has been a difficult problem to explore the mechanism of geomorphic influence on the distribution pattern of trees in Qinghai Tibet Plateau, and how to show the geomorphic influence more accurately. According to the field and literature survey, it is found that under the better terrain shelter, both the growth of arbor and shrub vegetation has a positive effect. This effect mainly includes the sheltering of wind by terrain and the increase of soil moisture and air humidity by terrain water accumulation. In this study, 250mdem is used to simulate the effect of vegetation occlusion, which is called Topographic Sheltering Index (TSI). C language is used to program and calculate the cumulative horizontal angle of obstacles at the target point. The judgment condition of obstacles is that the distance between the obstacles and the target point is less than 15-20 times of the height difference between the obstacles and the target point, and the multiple is adjustable, which is selected as 15 times according to the actual situation of the plateau, Taking 8km as the search radius and 10 degrees as the search angle), the Topographic Sheltering Index of the Qinghai Tibet Plateau is obtained.
ZHOU Yaozhi, CHENG Ming, TIAN Rui
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2020. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2020. The land cover classification products of 30m in 2020 areproduced using change detection method based on the land cover classification product of 2019 in Google Earth engine platform with the Landsat series data . The overall accuracy of the product is better than 85%. This product is a continuation of the human activity parameter product from 1985 to 2019，which also can be downloaded from this website.
YANG Aixia, ZHONG Bo, WU Junjun
This dataset contains land cover products in Qilian Mountain Area in 2020. The dataset was produced based on the product in 2019 using change monitoring method on the Google Earth Engine platform using Landsat series data. The overall accuracy of this product is above 85%. This is a continuation of the products from 1985-2019.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2019. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun, ZHONG Bo
