The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Tibetan plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
Based on the MODIS satellite remote sensing data, the overall vegetation coverage (VC) of the China-Mongolia-Russia Economic Corridor was calculated. The traditional VC formula selects the normalized difference vegetation index (NDVI) as a variable. For the reduction of deviation caused by soil background and the impacts of the atmosphere, the enhanced vegetation index (EVI) instead of NDVI is adopted in the calculation process of VC data set. The original data is the enhanced vegetation index data in the Terra MODIS Vegetation Index Data Version 6 (MOD13A3) with the resolution of 1 km. The MOD13A3 dataset is of higher quality than the source data because it filters the outliers or missing measurements of the MODIS satellite data. The China-Mongolia-Russia Economic Corridor is an area with high risk of desertification. At present, the development of desertification in the corridor extends along the main road between China and Mongolia, and the desertification is the most serious in densely populated urban areas. The regional desertification information can be extracted effectively from the vegetation coverage data, which will provide ecological and environmental data support for the disaster risk prevention and safe operation of transportation and pipelines.
ZHANG Xueqin
Vegetation photosynthesis is a key component of carbon cycle in terrestrial ecosystem. Simulating photosynthesis activities on different spatial and temporal scales is helpful to solve the problem of land carbon budget, and it is also an important way to accurately predict the direction of future climate change and an important prerequisite for scientific understanding of the supporting capacity of terrestrial ecosystem for sustainable development of human society. At present, although a variety of algorithms and products for estimating the total primary productivity (GPP) of ecosystems have been relatively mature, there are still great differences and uncertainties in the global GPP products of long time series, especially the trend of their temporal variation. Sunlight induced chlorophyll fluorescence (SIF) remote sensing is a new type of remote sensing technology developed rapidly in recent years. The close relationship between SIF and photosynthetic process makes it an effective probe to indicate the changes of vegetation photosynthesis and a powerful means to monitor GPP. A new vegetation index (Nirv) based on remote sensing data, namely the product of normalized vegetation index (NDVI) and near-infrared reflectance, is highly related to remote sensing SIF products; based on mechanism derivation, model simulation and analysis of remote sensing data, Nirv can be used as an alternative product of SIF to estimate global GPP. Therefore, on the basis of analyzing the feasibility of Nirv as SIF and GPP probe, this data set generates the global high-resolution long-time series GP data from 1982 to 2018 based on the AVHRR data of remote sensing and hundreds of flux stations around the world, and analyzes the temporal and spatial variation trend of global GPP. The resolution is month, 0.05 degree, and the data unit is gcm-2 The annual average global GPP is about 128.3 ± 4.0 PG Cyr − 1, and the root mean square error (RMSE) of the data is 1.95 gcm-2 D-1. The data set can be used to study global climate change and carbon cycle.
WANG Songhan, ZHANG Yongguang
Based on Landsat data (kh-9 data in 1976 as auxiliary data), glacial lake data of nearly 40 years (1970s-2018) in the western Nyainqentanglha range were obtained by manual digitization and visual interpretation. The variation characteristics of glacial lake over 0.0036 square kilometers in terms of type, size, elevation and watershed were analyzed in detail. The results show that, between 1976 and 2018, the number of glacial lakes increased by 56% from 192 to 299 and their total area increased by 35% from 6.75 ± 0.13 square kilometers to 9.12 ± 0.13 square kilometers ; the type of glacial lake is changing obviously; the smaller glacial lake is changing faster; the expansion of glacial lake is developing to higher altitude.
LUO Wei, ZHANG Guoqing
This data set includes land cover classification products of 30 meters in Qilian mountain area from 1985 to 2019. Firstly, the product uses Landsat-8/OLI to construct the 2015 time series data. According to the different NDVI time series curves of various ground features, the knowledge of different features is summarized, the rules are set to extract different features, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP classification system and from_ LC classification system can be divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impervious surface, bare land, glacier and snow. According to the accuracy evaluation of Google Earth HD images and field survey data, the overall accuracy of land cover classification products in 2015 was as high as 92.19%. Based on the land cover classification products in 2015, based on the Landsat series data and strong geodetic data processing ability of Google Earth engine platform, the land cover classification products from 1985 to 2019 are produced by using the idea and method of change detection. By comparing the classification products, it is concluded that the land cover classification products based on Google Earth engine platform have good consistency with the classification products based on time series method. In short, the land cover data set in the core area of Qilian Mountain has high overall accuracy, and the method based on Google Earth engine platform sample training can expand the existing classification products in time and space, and can reflect more land cover type change information in a long time series.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
This data set includes the distribution products of 30 m cultivated land and construction land in Qilian mountain area from 1985 to 2019. The product comes from the land cover classification products of 30m in Qilian mountain area from 1985 to 2019. NDVI products, light data products, DEM products and SAR data of sentry 1 are used in the production of the products. The total accuracy of the product is better than 85%. Among them, the peoducts from 1985-2015 have a 5 year- time resolution, and the other products have a 1 year - time resolution.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. According to the collected the published global GDP data of 2015, a downscaling model, named support vector machine regression kriging was established for predicting 100-m GDP in thirty-four key nodes along the Belt and Road. The remote sensed night light data, land cover, vegetation and terrain indices were employed as ancillary variables in downscaling process. To solve the problem of missing data existing in the ancillary datasets, we will apply kriging and function interpolation methods to fill gaps. The aggregation and resampling were used to obtain 1-km and 500-m all ancillary variables, as well as 100-m terrain indices including elevation, slope and aspect. The adopted downscaling model contains trend and residual predictions. The support vector machine regression is used to model the relationship among GDP and its ancillary variables for obtaining GDP trends at fine scale based on scale invariant of the relationship. And then, the kriging interpolation is used to estimate GDP residuals at fine scale. In the downscaling process, the mentioned downscaling model was firstly employed in 1-km and 500-m data for obtaining 500-m GDP predictions; and it was again used in 500-m and 100-m data for achieving 100-m GDP predictions. The 100-m GDP predictions in constant 2011 international US dollars would provide high spatial resolution data for risk assessments.
GE Yong, LING Feng
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
ZHANG Guoqing
The fraction snow cover (FSC) is the ratio of the snow cover area SCA to the pixel space. The data set covers the Arctic region (35 ° to 90 ° north latitude). Using Google Earth engine platform, the initial data is the global surface reflectance product with a resolution of 1000m with mod09ga, and the data preparation time is from February 24, 2000 to November 18, 2019. The methods are as follows: in the training sample area, the reference data set of FSC is prepared by using Landsat 8 surface reflectance data and snomap algorithm, and the data set is taken as the true value of FSC in the training sample area, so as to establish the linear regression model between FSC in the training sample area and NDSI based on MODIS surface reflectance products. Using this model, MODIS global surface reflectance product is used as input to prepare snow area ratio time series data in the Arctic region. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.
MA Yuan, LI Hongyi
This data set is the annual maximum value data (ndvi-am) of the normalized vegetation index of the Qinghai Tibet Plateau from 2000 to 2018. The data is in grid TIFF format, with a spatial resolution of 250m and a grid data value range of [- 1,1]. It can be used to study the change of vegetation coverage, grassland degradation and other ecological environment changes in the Qinghai Tibet Plateau, and can also provide data support for the study of urbanization and ecological environment interaction stress. The data is calculated based on the land level 2 standard data product of MODIS medium resolution sensor mod13 series (https://modis.gsfc.nasa.gov/data/dataprod/mod13.php). The level 2 Product data is a specific application data product generated after processing the original MODIS original data set. Ndvi-am data is processed by calculating the annual maximum value of NDVI of each pixel based on the normalized vegetation index data.
DU Yunyan, YI Jiawei
Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.
XU Erqi
The multi-decadal lake number and area changes in China during 1960s–2020 are derived from historical topographic maps and >42151 Landsat satellite images, including lakes as fine as ≥1 km^2 in size for the past 60 years (1960s, 1970s, 1990, 1995, 2000, 2005, 2010, 2015, 2020). From the 1960s to 2020, the total number of lakes (≥ 1 km ^ 2) in China increased from 2127 to 2621, and the area expanded from 68537 km ^ 2 to 82302 km ^ 2.
ZHANG Guoqing
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
YAN Huiming
The data set is the average wind speed of the Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The data is in tif format. The space and time resolutions are 0.25° and 3 hours respectively. The time is from 01, January, 2017 to 31, December, 2017. The data set uses the the Geodetic coordinate system. We can use the data to calculate the sand flux. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
GAO Xin
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
The dataset is the land cover of Qing-Tibet Plateau in 2014. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
The dataset is the land cover of Qing-Tibet Plateau in 2010. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture （CIAT）with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.
Food and Agriculture Organization of the United Nations（FAO）
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