This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CAO Guangzhen
The dataset of ground-based microwave scatterometer and snow parameter observations was obtained in the Binggou watershed experimental area on Mar. 16, 2008. Observation items included: (1) Snow backscattering coefficient by the scatterometer (2) Snow parameters as the snow surface temperature by the probe thermometer, snow grain size by the handheld microscope, snow density by the snow shovel, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer in BG-I. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) at the Dadongshu mountain pass; the major and minor axis and shape of the snow layer grain through the snow sieve. (4) Snow albedo by the total radiometer from 10:29 to 15:00 (5) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork at the Dadongshu mountain pass Two subfolders including raw data and preprocessed data were archived.
LIU Zengcan, LIU Zengcan, QIN Wei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, ZHANG Pu, JIANG Tenglong
In this study, an algorithm that combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS) (4 km) is presented to provide a daily cloud-free snow-cover product (500 m), namely Terra-Aqua-IMS (TAI). The overall accuracy of the new TAI is 92.3% as compared with ground stations in all-sky conditions; this value is significantly higher than the 63.1% of the blended MODIS Terra-Aqua product and the 54.6% and 49% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua over the Tibetan Plateau (TP) can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with the 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is similar to the 20.6% obtained from the 8-day MODIS Terra product (MOD10A2) but much larger than the 8.1% from the daily blended MODIS Terra-Aqua product due to the cloud blockage.
ZHANG Guoqing
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since the end of June 2013. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification.
KANG Jian, LI Xin, MA Mingguo
The dataset of ground truth measurements for snow synchronizing with Envisat ASAR was obtained in the Binggou watershed foci experimental area on Mar. 15, 2008. The Envisat ASAR data were acquired in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-B, BG-D, BG-E and BG-F; (2) Snow parameters including the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, snow depth by the ruler, and the snow surface temperature synchronizing with ASAR in BG-H, BG-D, BG-E and BG-F; (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with ASAR in BG-H15; the major and minor axis and shape of the snow layer grain through the self-made snow sieve. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, QU Wei, REN Jie, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
The dataset of snow density measurements was obtained in the Binggou watershed foci experimental area on Dec. 6 and Dec. 10, 2007 during the pre-observation period, to survey the snow layer and acquire the snow density for retrieval and modeling from remote sensing approaches. Observation items included: (1) Snow layer density: measured by snow shovel weighing method. Each 10cm was a unit. (2) Snow density, snow depth, snow temperature, snow-soil interface temperature, and snow grain size in BG-A. Measured were carried out in BG-A on Dec. 6, 2007, and in BG-B, BG-C and BG-D on Dec. 10, 2007. The dataset includes raw data and processed data plus GPS and calibration data for the snow shovel.
HAO Xiaohua, LIANG Ji, WANG Xufeng
The dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion and Landsat TM was obtained in the Binggou watershed foci experimental area on Mar. 17, 2008. Observation items included: (1) Snow parameters as snow depth by the ruler, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer simultaneous with the satellite in BG-A, BG-E, BG-F and BG-H. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A, BG-E and BG-H. Besides, 25-hour fixed-point continuous observation was carried out at the Binggou cold region hydrometerological station. (3) The snow spectrum by ASD (Xinjiang Meteorological Administration) (4) Snow albedo by the total radiometer Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.
QIU Yubao
This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).
HUANG Xiaodong
The dataset of snow depth measured by the elevation-graduated snow sticks was obtained in the Binggou watershed foci experimental area from Nov. 11 to 16, 2007, during the pre-observation period. 51 snow-stakes (2m long) were arranged according to different topographic landscapes, such as the flat, ubac, tailo and partial shade, and the length above the ground was recorded. From Mar. 2 to Apr. 6, 2008, the intensive observation period, ten measurements (Mar. 2, Mar. 4, Mar. 9, Mar. 16, Mar. 19, Mar. 21, Mar. 23, Mar. 29, Apr. 1 and Apr. 6) were carried out both manually and additionally by the telescope for the snow depth around the snow-stakes. Two files including raw data and preprocessed snow depth data were archived. Those provide reliable data for snow spatial heterogeneity study and snow accumulation and melt monitoring in the Binggou watershed.
BAI Yunjie, HAO Xiaohua, LI Hongyi
The dataset of ground truth measurements for snow synchronizing with MODIS was obtained in the Binggou watershed foci experimental area on Mar. 14, 2008. Those provide reliable data for snow-cover extent mapping and the retrieval of the snow surface temperature from MODIS remote sensing approaches. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface (ground surface) temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler, snow density by the snow shovel, the snow grain size by the handheld microscope and the snow surface temperature synchronizing with MODIS. (2) Snow albedo by the total radiometer in BG-A from 11:10-13:24 on Mar. 14, 2008. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with MODIS in BG-A and BG-I. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow was obtained, synchronizing with airborne microwave radiometers (K&Ka bands) mission in the Binggou watershed foci experimental area on Mar. 29, 2008. Those provide reliable ground data for retrieval of snow properties and parameters, especially snow depth and snow water equivalent study. Observation items include (1) snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) snow parameters in BG-A (18 points), BG-B (20 points), BG-EF (20 points) and BG-I (20 points): snow depth by the ruler, the snow temperature (mean of two measurements) by the probe thermometer, snow grain size by the handheld microscope, snow density by the cutting ring for each snow layer, and the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer. For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. Two files including raw data and pre-processed data were archived.
BAI Yanfen, BAI Yunjie, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, LIU Yan, ZHANG Pu
The dataset of ground truth measurements synchronizing with Terra MISR and MODIS was obtained in sampling plot BG-A of the Binggou watershed foci experimental area on Dec. 10 and Dec. 11, 2007 during the pre-observation period. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler and the snow grain size by the handheld microscope. (2) Snow density in "WATER: Dataset of snow density measurements in the Binggou watershed foci experimental area on Dec. 6 and Dec. 10, 2007 during the pre-observation period" (3) Snow properties in "WATER: Dataset of snow properties measured by the Snowfork in the Binggou watershed foci experimental area during the pre-observation period" Raw data and pre-processed data including snow parameters synchronizing with Terra MISR and MODIS and the temperature synchronizing with MODIS were archived herein.
LI Xin, WANG Jian, MA Mingguo, Wang Weizhen, CHE Tao, HAO Xiaohua, LI Hongyi, LIANG Ji, BAI Yunjie, WANG Xufeng, WU Yueru, WANG Yang, LUO Lihui, ZHANG Pu, LIU Yan
The dataset of fresh snow properties observations was obtained at the temporary sampling plot in the Qilian county on Mar. 20, 2008. Those provide reliable data for retrieval of snow parameters from remote sensing approaches. Observation items included: (1) Snow parameters such as snow depth, snow grain size by the handheld microscope, and snow density by the snow shovel (2) Fresh snow albedo by the total radiometer (3) Fresh snow spectrum by ASD Two files including raw data and preprocessed data were archived.
GE Chunmei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, LIU Yan, ZHANG Pu
The data set is the meteorological and observational data of hulugou shrub experimental area in the upper reaches of Heihe River, including meteorological data, albedo data and evapotranspiration data under shrubs. 1. Meteorological data: Qilian station longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3232.3m, scale meteorological data from January 1, 2012 to December 31, 2013. Observation items include: temperature, humidity, vapor pressure, net radiation, four component radiation, etc. The data are daily scale data, and the calculation period is 0:00-24:00 2. Albedo: daily surface albedo data from January 1, 2012 to July 3, 2014, including snow and non snow periods. The measuring instrument is the radiation instrument on the 10m gradient tower in hulugou watershed. Among them, the data from August 4 to October 2, 2012 was missing due to instrument circuit problems, and the rest data quality was good 3. Evapotranspiration: surface evapotranspiration data of Four Typical Shrub Communities in hulugou watershed. The observation period is from July 18 to August 5, 2014, which is the daily scale data. The data include precipitation data, evaporation and infiltration data observed by lysimeter. The data set can be used to analyze the evapotranspiration data of alpine shrubs and forests. The evapotranspiration of grassland under canopy was measured by a small lysimeter with a diameter of 25 cm and a depth of 30 cm. Two lysimeters were set up in each shrub plot, and one lysimeter was set for each shrub in transplanting experiment. The undisturbed undisturbed soil column with the same height as the barrel is placed in the inner bucket, and the outer bucket is buried in the soil. During the embedding, the outer bucket shall be 0.5-1.0 cm higher than the ground, and the outer edge of the inner barrel shall be designed with a rainproof board about 2.0 cm wide to prevent surface runoff from entering the lysimeter. Lysimeter was set up in the nearby meteorological stations to measure grassland evapotranspiration, and a small lysimeter with an inner diameter of 25 cm and a depth of 30 cm was also set up in the sample plot of Picea crassifolia forest to measure the evaporation under the forest. All lysimeters are weighed at 20:00 every day (the electronic balance has a sensing capacity of 1.0 g, which is equivalent to 0.013 mm evaporation). Wind proof treatment should be taken to ensure the accuracy of measurement. Data processing method: evapotranspiration is mainly calculated by mass conservation in lysimeter method. According to the design principle of lysimeter lysimeter, evapotranspiration is mainly determined by the quality difference in two consecutive days. Since it is weighed every day, it is calculated by water balance.
SONG Yaoxuan, LIU Zhangwen
This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"
KANG Jian, LI Xin, MA Mingguo
This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since January 2014. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. Please refer to "waternet data document 20141206. Docx" for details
KANG Jian, LI Xin, MA Mingguo
The dataset of ground-based RPG-8CH-DP microwave radiometers (6.925H/V, 18.7H/V and 36.5H/V) and ground truth observations for snow was obtained in the Binggou watershed foci experimental area on Mar. 24 (time-continuous from 11:42 to 17:28 BJT) and Mar. 25, 2008 (short-time multi-angle observations). A gentle slope of 10° was chosen as the observation site, where there was firn snow and the snow layer and the ice layer appeared alternately. The radiometer beam was set from -20° to -55°, with the steplength 5°. Observation items included: (1) The brightness temperature by the microwave radiometer in .BRT and .txt (the ASCII format). Each row in .txt was listed by year, month, date, hour, minute, second, 6.925GHz (h), 6.925GHz (v), 10.65GHz (h), 10.65GHz (v) , 18.7GHz (h), 18.7GHz (v), 36.5GHz (h), 36.5GHz (v), the elevation angle, and the azimuth angle. Values for 6.925GHz and 10.65GHz were zero due to the absence of these two radiometers. (2) Snow parameters including the snow profile temperature by the probe thermometer and the handheld infrared thermometer, the snow grain size by the handheld microscope, snow moisture, snow density, and snow permittivity by the snow fork. Five subfolders are archived, including the brightness temperature and the profiles of liquid water content, the snow grain size, snow density and the snow temperature.
CHANG Sheng, PENG Danqing, ZHANG Yongpan, ZHANG Zhiyu, ZHAO Shaojie, ZHENG Yue, ZHANG Zhiyu
The dataset of ground truth measurements for snow synchronizing with MODIS was obtained in the Binggou watershed foci experimental area on Mar. 19, 2008. Those provide reliable data for retrieval and verification of the snow temperature through airborne and satellite-borne remote sensing approaches. Observation items included: (1) Snow parameters, such as snow depth by the ruler (five measurements at random each point), the snow surface temperature by the infrared thermometer (several measurements at random), the snow layer temperature by the probe thermometer (10cm as an interval and two times each point), the snow grain size by the handheld microscope (10cm as an interval and three times each point) in BG-B from 12:40-13:00 (BJT) with the satellite overpass on Mar. 19, 2008. 64 points were selected by four groups. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A,automatically in coordination with ASD. (3) The snow spectrum by the portable ASD. (4) Snow albedo by the portable radiometer in BG-A. Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, MA Mingguo, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, DOU Yan, LIU Yan, ZHANG Pu
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