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
The vegetation index mainly reflects the differences between the visible light, near-infrared reflection and soil background. The vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. At present, normalized vegetation index (NDVI) is an important data source for detecting vegetation growth status, vegetation coverage and eliminating some radiation errors. NDVI can reflect the background influence of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and it is related to vegetation coverage. Landsat satellite data product is an important data source for NDVI estimation. Taking 31 key nodes and 3 major projects in the third pole as the research area, based on the data of Landsat-5 and landsat-8 from 2000 to 2016, the NDVI of different areas was cut and estimated, and finally the 16 day time series ten meter (30M) high-resolution NDVI data of key node areas in the third pole from 2000 to 2016 was obtained.
GE Yong, LING Feng, ZHANG Yihang
This data set includes the monthly synthetic 30 m × 30 m surface Lai products in Qilian Mountain Area in 2019. The maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface and calculate the Lai by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
This data set includes the monthly synthesis of 30 m × 30 m surface NPP products in the Qilian Mountain Area in 2019. The maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface and calculate NPP by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used, and the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels are used to synthesize the monthly NDVI products on the earth's surface, and then FVC is calculated. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
This data set includes the monthly composite 30 m × 30 m surface vegetation index products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize the monthly NDVI products on the earth's surface by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
WU Jinhua, ZHONG Bo, WU Junjun
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, WANG Yingzheng, LI Jianjiang, LI Xin, LIU Shaomin
This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2019. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2019 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2018, and can be used for quantitative estimation of water resource.
LI Jia, LI Jianjiang, LI Xin, LIU Shaomin
Anthropogenic heat is one of the products of urbanization, which refers to the heat produced by human activities and released into the atmosphere, mainly from various types of energy consumption and biological metabolism. This data set is the surface anthropogenic heat emission flux data of 500m × 500m spatial resolution in China's land surface area from 2000 to 2016 (2000 / 2004 / 2008 / 2012 / 2016). Data sources and processing methods: (1) through the collection of energy consumption data and socio-economic data of provinces and cities in 2000-2016, the annual average AHF of prefecture level cities (prefectures, districts and leagues) is estimated by the inventory method; (2) The AHF estimation model is established based on multi-source remote sensing data, and the grid AHF is obtained; (3) the AHF estimation results of time series are analyzed and tested, and the deviation values are corrected to improve the accuracy of the AHF estimation results. It is of great significance to understand and master the anthropogenic heat emission and its change for understanding the impact of urbanization on climate, environment and society.
This dataset contains daily land surface evapotranspiration products of 2019 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products and China Meteorological Forcing Dataset (He Jie, Yang Kun. China Meteorological Forcing Dataset. Cold and Arid Regions Science Data Center at Lanzhou, 2011. doi:10.3972/westdc.002.2014.db).
Yunjun YAO, Shaomin LIU, Ke SHANG
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.
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
The Optimum Interpolation sea surface temperature (OISST) analysis product provides complete ocean temperature fields constructed by using an optimum interpolation (OI) technique. The SST analysis has a spatial grid resolution of 0.25 degree and temporal resolution of 1 day. The product uses Advanced Very High Resolution Radiometer (AVHRR) satellite data from the Pathfinder AVHRR SST dataset when available for September 1981 through December 2005, and the operational Navy AVHRR Multi-Channel SST data for 2006 to the present day. Pathfinder AVHRR SST was chosen because of good agreement with the in-situ observation data. The product also uses sea ice datasets, in situ data from ships and buoys, and includes a large-scale adjustment of satellite biases with respect to the in-situ data. In areas where sea ice is present, SST is estimated from sea ice concentration datasets from NASA GSFC before 2005 and then from NOAA NCEP from 2005 onwards. The SST product is of great importance in the study of storm tide. Based on the SST product from 1981 to 2016, GEE was used to tailor the masks of the sea area along the Blet and Road. Finally, the 16-day synthetic sea surface temperature dataset of the sea area along the Blet and Road from 1981 to 2016 was obtained.
GE Yong, LI Qiangzi, DONG Wen
Thematic data on desertification in Western Asia, includes two parts: Distribution Map of Sandy Land in Western Asia, Distribution Map of Grassland Degradation in Western Asia. The spatial resolution of the data is 30m. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The map of artificial oasis pattern in Amu river basin is based on Landsat TM and ETM image data in 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually.
The gridded desertification risk data of Amu River Basin in 2018 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Amu River Basin in 2018.
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).
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.
Vulnerability refers to a property of the system that is susceptible to changes in structure and function due to the system's sensitivity to internal and external disturbances and its lack of ability to respond, that is, the ability of the region to cope with disasters to reduce losses when heat waves occur. This dataset is based on the pan-third pole regional road network data, GDP data, medical facility spatial distribution data, vegetation coverage data, and water distribution data as basic data,and takes 2015 as the base year. The Euclidean Metric calculation method is adopted to determine the spatial distribution of road networks, water and medical facilities in the area. The distance from roads, water bodies, medical facilities, GDP, and vegetation coverage are used as evaluation indicators. The equal-weight overlapping addition is used to evaluate the vulnerability of heat waves at each node. In order to eliminate the impact of unit differences, the data of each index layer is normalized before the evaluation.Finally, the vulnerability level of each node is divided by the natural Jenks method.
GE Yong, YANG Fei, LIU Qingsheng
The data comes from the National Centers for environmental information (NCEI), which provides meteorological records of all stations in the world since they were built, including temperature, wind speed, dew point, precipitation and other information. There are four recorded stations near Dhaka city. The monitoring data of meteorological stations have the characteristics of high precision. Firstly, the monitoring data of stations in the world are downloaded from NCEI, and then four stations in Dhaka city are selected according to longitude and latitude. The data level records the daily meteorological station monitoring data from January 1, 2016 to December 31, 2019.
GE Yong, YANG Fei
Based on 2015 as the base year, this data set selects population density, distribution of high-risk population and GDP as the evaluation indicators to complete the assessment of high temperature heat wave exposure at 34 key nodes. Exposure refers to the degree that a certain area may be affected by the disaster when the disaster occurs. In the extreme high temperature, human and economy are the two most obvious factors affected by the high temperature heat wave. The high-risk population is defined as children younger than five years old and the elderly older than 65 years old respectively. Equal weight overlapping plus method is adopted in the assessment. In order to eliminate the influence of unit difference, the data of each indicator layer is normalized before the assessment. The spatial resolution of the assessment result is 100m, covering 34 key nodes of the third pole.
GE Yong, YANG Fei, LIU Qingsheng