The data source of this data set is the European Space Agency (ESA) multispectral satellite Sentinel-2. It includes the annual mean data of CDOM and DOC of Qinghai Tibet Plateau lakes in 2017. Method of use: Based on the CDOM data of the measured sample points, the image reflectance information is extracted, the best prediction variable is selected through Pearson correlation analysis, and a multiple stepwise regression CDOM prediction model is constructed to obtain the CDOM results of the Qinghai Tibet Plateau water body. Because CDOM has a good correlation with DOC, DOC prediction results are calculated by CDOM. Adjustment R of the CDOM model of the final Qinghai Tibet Plateau ² Up to 0.81.
SONG Kaishan
Soil moisture (SM) plays a vital role in regulating the water and energy exchange between land surfaces and the atmosphere and is declared an essential climate variable by the Global Climate Observing System (GCOS). Vegetation optical depth (VOD) is a crucial parameter describing vegetation attenuation properties in microwave radiative transfer equation, and it has been proven to be a promising ecological indicator for studying plant hydraulics, carbon stocks, and vegetation phenology. A long-term SM and polarization-, frequency-dependent VODs (C/X/Ku) product was derived from the inter-calibrated AMSR-E/2 multi-frequency brightness temperature, using the multi-channel collaborative algorithm (MCCA). The MCCA comprehensively considers the physical relationship between multiple microwave channels and could simultaneously retrieve frequency- and polarization-dependent VODs and SM. The new MCCA AMSR-E/2 SM dataset was validated over 25 dense soil moisture networks from the International Soil Moisture Network (ISMN) and United States Department of Agriculture (USDA) watersheds. The results showed that MCCA performs best in terms of ubRMSE among the current publicly available SM datasets related to AMSR-E/2. In addition, polarization-, frequency-dependent VODs from MCCA may provide new insights for better understanding the water fluxes in plant physiology.
HU Lu, ZHAO Tianjie, JU Weimin , PENG Zhiqing , YAO Panpan, SHI Jiancheng
Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.
HE Chunyang, LIU Zhifeng, XU Min , LU Wenlu
Crop phenology refers to the date when a crop reaches a critical growth period. The main planting pattern in the North China Plain (NCP) is the rotation system of winter wheat and summer maize. Changes in the key phenological periods of winter wheat and summer maize reflect the response and adaptability of them to climatic conditions and production management measures. And the critical phenology dates are important parameters for evaluating crop growth status and irrigation water consumption in the NCP This study selected the winter wheat-summer maize stable planting area in the NCP. The GIMMS3g NDVI data from 1982 to 2015 was used. Multiple characteristis such as the maximum value, minimum value, slope, and percentage value of the curve were combined to extract phenology of winter wheat and summer maize: SOS (start of the season), PEAK (peak of the season), and EOS (end of the season). The extracted phenology was compared with the phenological records from the agro-meteorological stations. The R² was above 0.9, which was with high accuracy. (Details can be found in the reference) The phenological dataset can be applied to related researches about calculating the productivity of winter wheat and summer maize, evaluating the response of crops to climate change, and estimating irrigation water consumption in this region.
LEI Huimin
The North China Plain is an important food production area in China, with a large area of cropland and a complex planting structure. Accurately identifying the distribution of typical crops in this area and tracking the dynamic changes of planting structure are fundamental for detecting crop growth, evaluating crop irrigation water consumption and optimizing agricultural water resources allocation. This study used Fourier transform to obtatin the amplitudes and phases of the 0-5 harmonics of the MOD13Q1 NDVI data. Based on the field sample points and maximum likelihood supervised classification, the planting area of 6 typical crops (winter wheat-summer maize; winter wheat-rice; other double cropping systems; spring maize; cotton; other single cropping systems) in the North China Plain from 2001 to 2018 was identified. The identification results accuracy were evaluated through confusion matrix, comparison with the winter wheat planting area in the county-level statistical yearbook, and comparison with the proportion of winter wheat extracted by Landsat images, all of which showed good performance and high accuracy. The data can be applied to related research and analysis on crop production, irrigation water consumption estimation, and groundwater protection in the North China Plain.
LEI Huimin
Terrestrial actual evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. This global ET dataset is obtained based on ETMonitor model, which combines parameterizations for different processes and land cover types, with multi-source satellite data as input. Several open accessed remote sensing variables, e.g., LAI, FVC, albedo, surface soil moisture, dynamic surface water cover and snow/ice cover, were used as input to estimate daily ET. The meteorological variables from ERA5 reanalysis dataset were also adopted. The ETMonitor model is applied at daily scale to estimate the ET components at 1-km resolution, including vegetation transpiration, soil evaporation, canopy precipitation interception loss, water surface evaporation and snow/ice sublimation on daily step, and the total actual ET is estimated as the sum of these components. Overall, the actual ET estimated by ETMonitor agreed well with ground measurements from 251 flux towers across various ecosystems and climate zones globally, with high correlation (0.75), low bias (0.08mm/d), and low root mean square error (0.93 mm/d). The estimated ET showed reasonable spatial patterns, and superior in presenting the spatial variation of ET especially in the mountain regions and in the arid irrigated cropland regions. The ET estimation is conducted at daily temporal step and 1km spatial resolution. For easier publication, the daily/1-km ET from ETMonitor (https://doi.org//10.12237/casearth.6253cddc819aec49731a4bc2) was summed to obtain monthly ET in this dataset. The data type is 16-bit signed integer, the scale factor is 0.1, and the unit is mm/month. The missing values were filled by -1.
ZHENG Chaolei , JIA Li , HU Guangcheng
Water cover is one of the basic parameters of water cycle and energy balance. Based on the AVHRR daily reflectance time series from 1982 to 2020, this data set has produced 39 year long-term daily water body mapping products (including water body icing information) on the Qinghai Tibet Plateau. This dataset contains 39 folders, named after the year (from 1982 to 2020). Each folder contains 365 / 366 GeoTIFF files, and each file contains two bands: (1) water mapping band (waterlayer); (2) Quality control information band (QC). This product provides data support for remote sensing monitoring of water bodies in the Qinghai Tibet Plateau.
JI Luyan
Timely and correct observation of the spatial and temporal patterns and dynamics of oases is important for the property socioeconomic development of arid zones. During this study, a complete of 9 periods of Landsat image knowledge in 1986, 1990, 1995, 2000, 2005, 2010, 2015, 2018, and 2020 were accustomed get oasis distribution knowledge within the Hexi region from 1986 to 2020 employing a combination of OSTU threshold methodology and manual visual interpretation, and combined with high-resolution Google Earth pictures and field validation knowledge were combined to ascertain random sample points supported confusion matrix to verify the accuracy of oasis extraction results. The overall accuracy of oasis data in Hexi Corridor is over 94%, and the Kappa coefficient is over 0.88. This dataset can provide data support for the ecological environment protection of Hexi oasis.
XIE Yaowen, ZHANG Xueyuan, LIU Yiyang, HUANG Xiaojun, LI Ruyan, ZONG Leli, XIAO Min, QIN Mengyao
Based on the Sentinel-2 and Landsat 5/7/8 multispectral instrument imageries combined with in-situ measured hydrological data, bankfull river geometry of six major exorheic river basins of the Qinghai-Tibet Plateau (the upper Yellow River, upper Jinsha River, Yalong River, Lantsang River, Nu River and Yalung Zangbo River) are presented. River surface of six mainstreams and major tributaries are included. For each river basin, two types of rivers are included: connected and disconnected rivers. Format of the dataset is .shp exported from the ArcGIS 10.5. Three products are included in the dataset: one original product (bankfull river surface dataset) and two derived products (bankfull river width dataset and bankfull river surface area dataset with a 1 km river length interval). These three products are in three folders. The first folder, “1-Bankfull River Surface”, contains river surface vectors for six river basins in the .shp file. The second folder, “2-Bankfull River Width”, contains bankfull river widths and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. The river width vectors in the .shp files are also provided in the second folder. The third folder, “3-Bankfull River Surface Area”, contains bankfull river surface areas and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. Three Supplementary Files are included: Supplementary File 1, tables and figures related to the dataset; Supplementary File 2, used for river surface extraction based on GEE platform; Supplementary File 3, used for river width extraction based on Matlab. The provided planform river hydromorphology data can supplement global hydrography datasets and effectively represent the combined fluvial geomorphology and geological background in the study area.
LI Dan , XUE Yuan , QIN Chao , WU Baosheng , CHEN Bowei , WANG Ge
Glacier surface albedo is a key parameter in the process of glacier mass and energy balance. The data include annual mean glacier surface albedo and annual minimum glacier surface albedo for each year of the 2000-2020 ablation period (June-August) in the High Mountain Asia. Based on the MODIS 500m resolution daily snow albedo products (including MOD10A1 and MYD10A1), firstly, mean-synthesis was applied to the morning star data MOD10A1 and afternoon star data MYD10A1, followed by interpolation and null-filling using mean-filtering for data within a ±2 day window, and finally based on the minimum and mean methods to obtain the annual mean albedo and annual minimum albedo for glaciers in High Mountain Asia were obtained based on the minimum and mean methods. Compared to the original data, the accuracy and coverage of the data are greatly improved. It can provide ice surface albedo input data for studying the relationship between glacier albedo and matss balance and for glacier models.
XIAO Yao
Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters to reflect the crop growth and nutrient information. According to this parameter, the N demand of crops in different seasons can be known, which is an important guide to the reasonable application of N fertilizer. Correct NDVI (C-NDVI) is the value of NDVI after excluding the influence of climate elements (temperature, precipitation, etc.) on NDVI. Taking precipitation as an example, studies on the lag effect of precipitation on vegetation growth show that the lag time of precipitation effects varies in different regions due to differences in vegetation composition and soil types. In this study, we post-processed the MODIS NDVI data and firstly correlated the NDVI value of the current month with the precipitation of the current month, the average value of the precipitation of the current month with that of the previous month, and the average value of the precipitation of the current month with that of the previous two months to determine the optimal lag time. The NDVI was regressed on precipitation and air temperature to obtain the correlation coefficients, and then the corrected NDVI values were calculated by the difference between the MODIS NDVI and the NDVI regressed on climate factors. We corrected NDVI using climate data to give reliable vegetation correction indices for the circum-Arctic Circle (range north of 66°N) and the Tibetan Plateau (range 26°N to 39.85°N and 73.45°E to 104.65°E) for 2013 and 2018. The spatial resolution of the data is 0.5 degrees and the temporal resolution is monthly values.
YE Aizhong
Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021). This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30. The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.
CAO Ruyin , XU Zichao , CHEN Yang , SHEN Miaogen , CHEN Jin
The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. 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 , LI Yi, ZHONG Bo
Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
The dataset includes three high-resolution DSM data as well as Orthophoto Maps of Kuqionggangri Glacier, which were measured in September 2020, June 2021 and September 2021. The dataset is generated using the image data taken by Dajiang Phantom 4 RTK UAV, and the products are generated through tilt photogrammetry technology. The spatial resolution of the data reaches 0.15 m. This dataset is a supplement to the current low-resolution open-source topographic data, and can reflect the surface morphological changes of Kuoqionggangri Glacier from 2020 to 2021. The dataset helps to accurately study the melting process of Kuoqionggangri Glacier under climate change.
LIU Jintao
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
