The global soil dataset for earth system modeling (2014)

The source data for this dataset is derived from world soil maps and multiple regional and national soil databases, including soil attributes and soil maps. We have adopted a unified data structure and data processing process to fuse diverse data. We then used the soil type connection method and the soil variable line connection method to obtain the spatial distribution of soil properties. To aggregate these data, we currently use the area weighting method. The raw data has a resolution of 30 seconds, and aggregated data with a 5-minute resolution (about 10km) is provided here. There are eight vertical layers with a maximum depth of 2.3 meters (ie 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). 1. Data characteristics: Projection: WGS_1984 Coverage: Global Resolution: 0.083333 degrees (about 10 kilometers) Data format: netCDF 2. The data set contains 11 items of general soil information and 34 properties of soil. (1) The general information of the soil is as follows, the file general.zip: No. Description Units 1 additional property 2 available water capacity 3 drainage class 4 impermeable layer 5 nonsoil class 6 phase1 7 phase2 8 reference soil depth cm 9 obstacle to roots 10 soil water regime 11 topsoil texture (2) The 34 soil properties are as follows, files 1-9.zip, 10-18.zip, 19-26.zip, 27-34.zip Soil organic carbon density: SOCD5min.zip: No. Attrubute units Scale factor 1 total carbon% of weight 0.01 2 organic carbon% of weight 0.01 3 total N% of weight 0.01 4 total S% of weight 0.01 5 CaCO3% of weight 0.01 6 gypsum% of weight 0.01 7 pH (H2O) 0.1 8 pH (KCl) 0.1 9 pH (CaCl2) 0.1 10 Electrical conductivity ds / m 0.01 11 Exchangeable calcium cmol / kg 0.01 12 Exchangeable magnesium cmol / kg 0.01 13 Exchangeable sodium cmol / kg 0.01 14 Exchangeable potassium cmol / kg 0.01 15 Exchangeable aluminum cmol / kg 0.01 16 Exchangeable acidity cmol / kg 0.01 17 Cation exchange capacity cmol / kg 0.01 18 Base saturation% 19 Sand content% of weight 20 Silt content% of weight 21 Clay content% of weight 22 Gravel content% of volume 23 Bulk density g / cm3 0.01 24 Volumetric water content at -10 kPa% of volume 25 Volumetric water content at -33 kPa% of volume 26 Volumetric water content at -1500 kPa% of volume 27 The amount of phosphorous using the Bray1 method ppm of weight 0.01 28 The amount of phosphorous by Olsen method ppm of weight 0.01 29 Phosphorous retention by New Zealand method% of weight 0.01 30 The amount of water soluble phosphorous ppm of weight 0.0001 31 The amount of phosphorous by Mehlich method ppm of weight 0.01 32 exchangeable sodium percentage% of weight 0.01 33 Total phosphorus% of weight 0.0001 34 Total potassium% of weight 0.01

0 2020-06-04

HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Populus forest station, 2015)

This data set contains the eddy correlativity observation data of the euphrate poplar forest station downstream of heihe hydrometeorological observation network from January 1, 2015 to December 31, 2015.The station is located in Inner Mongolia ejin banner four bridge, under the surface is the euphorbia poplar forest.The longitude and latitude of the observation point are 101.1236e, 41.9928n and 876m above sea level.The rack height of the vortex correlativity instrument is 22m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500) is 17cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.2m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift, etc., shall be marked in red font.On April 22, solstice and April 25, data was missing due to the calibration of the vortex system Li7500.August 17 solstice September 5, due to memory card problems, resulting in intermittent data. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Li et al. (2013), and for observation data processing, please refer to Liu et al. (2011).

0 2020-03-04

WATER：Dataset of Soil freeze/thaw experiment Observed in the upper reaches of the Heihe River Basin from Nov. 10 to Nov. 14, 2013

This data set includes the continuous observation data set of light temperature and surface temperature and humidity measured by the vehicle borne microwave radiometer from November 10 to 14, 2013 in aroucaochang, arouxiang, Qilian County, Qinghai Province. The surface temperature and humidity include six layers of temperature sensor at the soil depth of 1cm, 3cm, 5cm, 10cm, 15cm, 20cm and six layers of humidity sensor at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 10-14, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 10.65, 18.7 and 36.5ghz V polarization and H polarization data Soil temperature: use the sensor installed on dt80 and dt85 to measure the soil temperature of 1cm, 5cm, 10cm, 20cm, and 1cm, 3cm, 5cm, 10cm, 15cm, which is measured by the sensor connected to dt80 Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time 3. Data size: 16.7M 4. Data format:. Xls

0 2020-03-13

WATER: Dataset of ground truth measurement synchronizing with Envisat ASAR in the A'rou foci experimental area on July 5 and July 6, 2008

The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in No. 1, 2 and 3 quadrates of the A'rou foci experimental area on Jul. 5 and Jul. 6, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:14 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Observation items included: (1) the quadrate investigation in No. 2 and 3 quadrates: GPS by GARMIN GPS 76, plant species by manual cognition, the plant number by manual work, the height by the measuring tape repeated 4-5 times, phenology by manual work, the coverage by manual work (compartmentalizing 0.5m×0.5m into 100 to see the percentage the stellera takes) and the chlorophyll content by SPAD 502. (2) spectrum of stellera and pasture by ASD FieldSpec (350～2 500 nm), with 20% reference board. The preprocessed canopy spectrum was archived. (3) BRDF by ASD FieldSpec (350～2 500 nm), with 20% reference board. The processed reflectance and transmittivity were archived as .txt files. (4) photosynthesis of stellera and pasture by LI-6400. The data were archived in Excel format. (5) soil moisture by WET soil moisture tachometer. Acquisition time, soil moisture (%vol), Ecp (ms/m), Tmp Eb and Ecb (ms/m) of 25 corner points were archived. (6) the soil temperature by the handheld infrared thermometer. Acquisition time, the soil temperature measured three times and the land cover types were archived. The data included the canopy reflectance on Jul. 5 and 6, photosynthesis on Jul. 5 and 6, BRDF on Jul. 5, photos on Jul. 5, the infrared land surface temperature and soil moisture by WET on Jul. 5, biomass on Jul. 5 and the surface temperature along No. 3 flight on Jul. 6.

0 2019-09-11

WATER: Dataset of ground truth measurements for snow synchronizing with Envisat ASAR in the Binggou watershed foci experimental area on Mar. 15, 2008

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.

0 2019-05-23

Daily rainfall data 1990-2004 of the Heihe River Basin

Data source: China l Meteorological Administration Network; Data Content: Daily Rainfall Data Series of Heihe River Basin from 1990 to 2004; Evaporation Data of Heihe River Basin from 2000 to 2012. Data Spatial Range: Rainfall Data (Yingluoxia, Shandan, Gaoya, Pingchuan, Ganzhou Pingshan Lake, Zhengyixia Gorge, Liyuan River); Evaporation Data (Zhangye, Gaotai, Dingxin, Jiuquan, Jinta, Shandan, Ejina, Hequ)

0 2020-07-28

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

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.

0 2019-05-23

WATER: Dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion and Landsat TM in the Binggou watershed foci experimental area on Mar. 17, 2008

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.

0 2019-05-23

Snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018)

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.

0 2020-05-28

MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)

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.

0 2019-09-15

MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)

0 2020-04-29