The land cover dataset of Pan third pole major cities contains 14 cities (Urumqi, Xining, Lanzhou, Dhaka, Kathmandu, Lucknow, Delhi, Lahore, Islamabad, Kabul, Dushanbe, Tashkent, Bishkek and Almaty) in 2000 / 2010 / 2017, the spatial resolution of this dataset is 30 m. It includes vegetation, cultivated land, artificial surface, water body and others. Based on globeland30, mcd12q1 and globcover2009, the consistent regions were identified and retained. The inconsistent regions were reclassified by deep learning method, and the final classification results were obtained by fusing the above regions. The data has been verified by visual interpretation. The data are applied to the study of construction land dynamics and anthropogenic influence in Pan-Third Pole cities. Data type: grid. Projection mode: UTM projection.
Xin LI Wenfei LUAN
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage in 2019. The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
ZHOU Ji LIU Shaomin DONG Weishen
Land surface temperature is a critical parameter in land surface energy balance. This dataset provides the monthly land surface temperature of UAV remote sensing for typical ground stations in the middle reaches of Heihe River basin from July to September in 2019. The land surface temperature retrieval algorithm is an improved single-channel algorithm, which was applied to the land surface brightness temperature data obtained by the UAV thermal infrared remote sensing sensor, and finally the land surface temperature data with a spatial resolution of 0.4m was obtained.
ZHOU Ji LIU Shaomin WANG Ziwei
NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides a monthly land surface albedo of UAV remote sensing with a spatial resolution of 0.2 m. It measured in the midstream of Heihe River Basin during the vegetation growth season over typical stations in 2019. The pix4D mapper software was used for image mosaic and NDVI calculation.
ZHOU Ji LIU Shaomin JIN Zichun
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 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 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
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 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 dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou and Lat/Lon information.
CHAI Linna ZHU Zhongli LIU Shaomin
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.
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).
Sher Muhammad Sher Muhammad
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.
Based on 100m risk assessment data set and 100m vulnerability assessment data set, this data set respectively gives different weights to the risk and vulnerability (the risk weight is 0.8, and the vulnerability weight is 0.2), and 34 key node 100m risk assessment data sets are obtained by adding. One belt, one road area, is evaluated for flood risk in extreme areas. The data provide basis for local government departments to make decisions, and early warning before flood disasters, so that we can gain valuable time to take measures to prevent and reduce disasters, and to reduce the loss of lives and property of people caused by floods.
GE Yong LI Qiangzi LI Yi