This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
The data set contains land cover data sets from the Yellow River Source, the Yangtze River Source, and the Lancang River from 1992 to 2015. A total of 22 land cover classifications based on the UN Land Cover Classification System were included. NOAA AVHRR, SPOT, ENVISAT, PROBA-V and other vegetation classification products were integrated. In China, (1) first, combined with the 1:100,000 vegetation classification (2007) of China, quality correction and control were performed, and (2) the vegetation classification of China emphasized the combination with climate zones, when correcting CCI-LC, climate divisions and the corresponding vegetation types were combined, and the data label was comprehensively revised.
WEI Yanqiang
These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.
CHEN Jun
The land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to contact@esalandcover-cci.org.
XU Xiyan
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
Er’ba Reservoir surface temperature of water body can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This site is 14 KM away from East of ZhangYe city. It’s located in Er’ba village, JianTan town, ZhangYe city. The coordinates of this site: 38°54′57.14" N, 100°36′57.39" E. Observation Instrument: The observation system consists of two SI-111 infrared radiometers (Campbell, USA) and two 109SS temperature probes (Campbell, USA). Two SI-111 sensors, one installed vertically downward to water surface, another face to south of zenith angle 35°. Temperature probes float under water surface at 0 cm. SI-111 sensor installed at 3.0 m height, 3.4 m away from water edge. Observation Time: This site operates from 27 May, 2012 to 27 September, 2012. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data： Water surface infrared temperature (by SI-111), sky infrared temperature (by SI-111), water surface temperature (by 109ss) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: TarT_Atm, Sky infrared temperature (℃) @ facing south of zenith angle 35°; SBT_Atm, body temperature of SI-111 sensor (℃) measured sky; TarT_Sur, water surface infrared temperature @ 3.0 m height; SBT_Sur, body temperature of SI-111 sensor (℃) measured water surface; WaterT_1, WaterT_2, water surface temperature (℃) measured by 109SS temperature probes. Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, WU Shixin, ZHOU Wancun
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, WU Shixin, ZHOU Wancun
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, WU Shixin, ZHOU Wancun
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
Data for 100000 desert map qaidam river basin, cutting since China 1:100000 desert sand data set, the data of TM images in 2000 data sources, to interpret, extraction, revision, using remote sensing and geographic information system technology combining 1:100000 scale mapping, the desert, sand and gravel gobi for thematic mapping.The desert codes are as follows: mobile sandy land 2341010, semi-mobile sandy land 2341020, semi-fixed sandy land 2341030, gobi desert 2342000, saline alkaline land 2343000.
WANG Jianhua
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
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