Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
This dataset is global respiration data, including autotrophic respiration (ra) and heterotrophic respiration (rh). It is simulated by TaiESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under historical scenarios. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 0.9 ° x1.25 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.AS -RCEC.TaiESM1.historical。
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
ZHANG Pei, ZHENG Donghai, WEN Jun, ZENG Yijian, WANG Xin, WANG Zuoliang, MA Yaoming, SU Zhongbo
The three pole soil microbial post-treatment products in typical years collected the distribution and analysis results of soil samples from the north and south polar regions from 2005 to 2006 and the distribution and analysis results of soil samples from the Qinghai Tibet Plateau in 2015. Through sorting and summarizing, the post-processing data products of soil microorganisms in the three pole region are obtained. The data format is excel, which is convenient for users to view. Among them, the collection time of samples from the north and south polar regions was from December 13, 2005 to December 8, 2006, including 52 samples from three regions in the Arctic (Spitsbergen slijeringa, Spitsbergen vestpynten, and Alexandra fjord Highlands), and 171 samples from five regions in the Antarctic (Mitchell Peninsula, Casey station main power house, Robinson ridge, herring Island, browning Peninsula); The Qinghai Tibet Plateau was collected from July 1 to July 15, 2015, including meadow, grassland and desert ecosystems. There were 18 sampling points in total, and the number of samples at each sampling point was 3-5. The precipitation, air temperature and drought degree of the sampling point are estimated from the meteorological information for reference. The soil surface samples were collected and stored in liquid nitrogen, then transported back to the Sydney Laboratory for extraction by fastprep DNA kit. The extracted DNA samples were amplified with the 16S rRNA gene fragment using 27F (5'-gagttttgatcntggctca-3') and 519r (5'-gtnttacngcgckctg-3'). The amplified fragments were sequenced by 454 method, and the original data were analyzed by mothur software. The sequences with poor sequencing quality were first removed, and then the chimeric sequences were sequenced and removed. After that, the similarity between sequences is calculated. Sequences with a similarity of more than 97% are clustered into one OTU, and OTU representative sequences are defined. The OTU representative sequences were aligned with the Silva database, and were identified to belong to the first level when the reliability was greater than 80%.
YE Aizhong
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
FANG Huajun
Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.
PAN Xiaoduo, LI Hu
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
PAN Xiaoduo
Rainfall erosivity is one of the important basic data to quantify soil erosion in the Tibet Plateau. High precision rainfall erosivity data is the key to understand the current situation of soil and water loss in theTibet Plateau and formulate soil and water conservation measures. Meanwhile, it can provide a powerful reference for the prevention and control of geological disasters in the Tibet Plateau. Based on the 1-min dense precipitation observations and the grid precipitation product, a new annual rainfall erosivity dataset in Tibet Plateau from 1950 to 2020 is constructed through the steps of correction, reconstruction and validation. This dataset is the rainfall erosivity data set with the highest accuracy and the longest time series in the Tibet Plateau.
CHEN Yueli
This data set contains the results of the calculation of Net Primary Productivity (NPP) on the Tibetan Plateau based on ecological models and remote sensing data from 1982 to 2006. Ecosystem NPP of the Tibetan Plateau was generated based on the remote sensing Advanced Very High Resolution Radiometer (AVHRR) data and the Carnegie-Ames-Stanford Approach (CASA) model(1982-2006), the soil carbon content was generated based on the second soil census data, and the biomass carbon data were generated based on the High Resolution Biosphere Model (HRBM) model. Forest ecosystem NPP of the Tibetan Plateau (1982-2006): npp_forest82.e00，npp_forest83.e00，npp_forest84.e00，npp_forest85.e00，npp_forest86.e00， npp_forest87.e00，npp_forest88.e00，npp_forest89.e00，npp_forest90.e00，npp_forest91.e00， npp_forest92.e00，npp_forest93.e00，npp_forest94.e00，npp_forest95.e00，npp_forest96.e00， npp_forest97.e00，npp_forest98.e00，npp_forest99.e00，npp_forest00.e00，npp_forest01.e00， npp_forest02.e00，npp_forest03.e00，npp_forest04.e00，npp_forest05.e00，npp_forest06.e00 Grassland ecosystem NPP of the Tibetan Plateau(1982-2006)： npp_grass82.e00，npp_grass83.e00，npp_grass84.e00，npp_grass85.e00，npp_grass86.e00， npp_grass87.e00，npp_grass88.e00，npp_grass89.e00，npp_grass90.e00，npp_grass91.e00， npp_grass92.e00，npp_grass93.e00，npp_grass94.e00，npp_grass95.e00，npp_grass96.e00， npp_grass97.e00，npp_grass98.e00，npp_grass99.e00，npp_grass00.e00，npp_grass01.e00，npp_grass02.e00，npp_grass03.e00，npp_grass04.e00，npp_grass05.e00，npp_grass06.e00. Biomass carbon and soil carbon of the Tibetan Plateau： Biomass.e00，Socd.e00. The soil carbon content data （Socd） are generated based on data of the second soil census of China and Soil Map of China (1:1,000,000) by soil subclass interpolation. The NPP data are generated from the CASA model and AVHRR data simulation: Potter CS, Randerson JT, Field CB et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811–841. The biomass carbon data are generated via HRBM model simulation： McGuire AD, Sitch S, et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochem. Cycles, 2001, 15 (1), 183-206. The raw data are mainly remote sensing data and field observation data with high accuracy; the verification and adjustment of the measured data in the field during the production were undertaken to maintain the error of the simulation results and the field measured data within the acceptable range as much as possible; the verification results of the NPP data and the field measured data show that the error remains within 15%. The spatial resolution is 0.05°×0.05° (longitude×latitude).
ZHOU Caiping
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
The data products of mixed soil moisture of the Tibetan Plateau utilize remote sensing observation, in situ measurement and model simulation techniques. In situ soil moisture (SM) observation combines the classification of the Tibetan Plateau climate zone and is used to generate in situ measurements of SM climatology at plateau scales. The resulting in situ SM climatology of the Tibetan Plateau scale is used to scale the SM data simulated by the model, which are then used to scale the SM satellite observations. The climatological-scale satellites and model-simulated SMs are then objectively mixed by applying triple configuration and least square matching. The final mixed SM can replicate SM dynamics in different climate zones, from subhumid areas to semiarid and arid regions of the Tibetan Plateau. - Time resolution: day, starting from 01/05/2008 - Spatial resolution: 0.25° × 0.25° - Data set size: 61 × 121 × 975 - Unit: cm^3 cm^-3 The data quality is open to assessment.
ZENG Yijian
Based on the field survey, the aboveground and underground biomass of vegetation, and soil carbon and nitrogen contents in Nagqu， in the north of Zoige, eastern of Tibet plateau and the wind vacanofrom 2015 to 2017 were collected, and the data were collated and preliminarily analyzed. Dataset consists both of the aboveground and underground biomass of vegetation and soil carbon and nitrogen contents in different elevation gradient (subalpine meadow, alpine meadow, alpine shrub meadow), different moisture gradient (wetland, degraded swamp, swamp meadow, wet meadow, dry meadow and degraded meadow) and the different desertification degree (mild desertification, moderate desertification, severe desertification, desertification). The differences and trends of vegetation biomass and soil carbon and nitrogen contents under different gradients were analyzed. This dataset provides a theoretical basis for understanding and rational utilization of grassland resources, and also provides strong support for exploring the prediction of alpine grassland productivity under the global climate change.
ZHANG Xianzhou, ZHANG Yangjian, SU Peixi, YANG Yan
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²
ZHAO Xinquan
Soil is mineral particles of different sizes formed by weathering of rocks. Soil not only provides nutrients and water for crops, but also has a transforming effect on various nutrients. In addition, the soil also has a self-cleaning function, which can improve organic matter content, soil temperature and humidity, pH value, anion and cation. The soil pollution causes several environmental problems: industrial sewage, acid rain, exhaust emissions, accumulations, agricultural pollution. After the land is polluted, the contaminated tops with high concentration of heavy metals are easily entered under the action of wind and water. Other secondary ecological and environmental problems such as air pollution, surface water pollution, groundwater pollution and ecosystem degradation in the atmosphere and water.he data set comes from the World Soil Database (Harmonized World Soil Database version 1.1) (HWSD) UN Food and Agriculture (FAO) and the Vienna International Institute for Applied Systems Research Institute (IIASA) constructed, which provides data model input parameters for the modeler, At the same time, it provides a basis for research on ecological agriculture, food security and climate change.
Food and Agriculture Organization of the United Nation FAO
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
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
The data set contains the data set (98 ° 29′16″E, 31 ° Based on hobo temperature, moisture and small meteorological station, the monitoring data of shallow ground temperature, moisture and field meteorological elements of 36 ′ 36 ″ n) freeze-thaw landslide and thaw mud flow are obtained through field monitoring. The observation time is between August 31, 2019 and July 14, 2020. Through on-site monitoring of a complete freeze-thaw cycle, the monitoring data of ground temperature, moisture and meteorological elements automatically obtained by on-site sensors are downloaded. Through certain quality control, the data when the sensors are not fully adapted to the soil environment and the system error caused by sensor failure are eliminated. The observation depth of ground temperature is 10cm, 20cm, 40cm, 60cm, 80cm, 100cm, 150cm and 200cm, with a total of 8 layers. The observation depth of water is 20cm, 50cm, 100cm and 200cm, with a total of 4 layers. Meteorological observation elements mainly include temperature, rainfall, wind speed, wind direction and solar radiation. The observation interval is 30 minutes (Note: the maximum range of solar radiation sensor is 1276.8 w / m2, and the actual solar radiation value is 1276.9 w / m2 when it is greater than the maximum range; The minimum starting wind speed of the wind speed sensor is 0.5m/s. When the actual wind speed is less than the starting wind speed, the display value is 0. Therefore, the data can not reflect the phenomenon of super solar constant and wind speed below 0.5m/s). Quality control includes eliminating the data when the sensor is not fully adapted to the soil environment and the system error caused by sensor failure. The corrected final data is stored in Excel file. The integrity and accuracy of the obtained field data are more than 95% after review by many people. The monitoring data can provide the necessary data support for the research of freeze-thaw landslide and thaw mud flow in Southeast Tibet.
NIU Fujun
The data set includes the sample survey data of alpine grassland and alpine meadow in Maduo County in September 2016. The sample size is 50cm × 50cm. The investigation contents include coverage, species name, vegetation height, biomass (dry weight and fresh weight), longitude and latitude coordinates, slope, aspect, slope position, soil type, vegetation type, surface characteristics (litter, gravel, wind erosion, water erosion, saline alkali spot, etc.), utilization mode, utilization intensity, etc.
LI Fei, Fei Li, Zhijun Zhang, Fei Li, Zhijun Zhang
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
Soil data is important both on a global scale and on a local scale, and due to the lack of reliable soil data, land degradation assessments, environmental impact studies, and sustainable land management interventions have received significant bottlenecks . Affected by the urgent need for soil information data around the world, especially in the context of the Climate Change Convention, the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO) and the Kyoto Protocol for Soil Carbon Measurement and FAO/International The Global Agroecological Assessment Study (GAEZ v3.0) jointly established the Harmonized World Soil Database version 1.2 (HWSD V1.2). Among them, the data source in China is the second national land in 1995. Investigate 1:1,000,000 soil data provided by Nanjing Soil. The resolution is 30 seconds (about 0.083 degrees, 1km). The soil classification system used is mainly FAO-90. The core soil system unit unique verification identifier: MU_GLOBAL-HWSD database soil mapping unit identifier, connected to the GIS layer. MU_SOURCE1 and MU_SOURCE2 source database drawing unit identifiers SEQ-soil unit sequence in the composition of the soil mapping unit; The soil classification system utilizes the FAO-7 classification system or the FAO-90 classification system (SU_SYM74 resp. SU_SYM90) or FAO-85 (SU_SYM85). The main fields of the soil property sheet include: ID (database ID) MU_GLOBAL (Soil Unit Identifier) (Global) SU_SYMBOL soil drawing unit SU_SYM74 (FAO74 classification); SU_SYM85 (FAO85 classification); SU_SYM90 (name of soil in the FAO90 soil classification system); SU_CODE soil charting unit code SU_CODE74 soil unit name SU_CODE85 soil unit name SU_CODE90 soil unit name DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS(19.5); AWC_CLASS (effective soil water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification to the bottom of the soil); SWR: String (soil moisture content); ADD_PROP: Real (specific soil type in the soil unit related to agricultural use); T_TEXTURE (top soil texture); T_GRAVEL: Real (top gravel volume percentage); (unit: %vol.) T_SAND: Real (top sand content); (unit: % wt.) T_SILT: Real (surface layer sand content); (unit: % wt.) T_CLAY: Real (top clay content); (unit: % wt.) T_USDA_TEX: Real (top layer USDA soil texture classification); (unit: name) T_REF_BULK: Real (top soil bulk density); (unit: kg/dm3.) T_OC: Real (top organic carbon content); (unit: % weight) T_PH_H2O: Real (top pH) (unit: -log(H+)) T_CEC_CLAY: Real (cation exchange capacity of the top adhesive layer soil); (unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of top soil) (unit: cmol/kg) T_BS: Real (top level basic saturation); (unit: %) T_TEB: Real (top exchangeable base); (unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top sulfate content); (unit: % weight) T_ESP: Real (top exchangeable sodium salt); (unit: %) T_ECE: Real (top conductivity). (Unit: dS/m) S_GRAVEL: Real (bottom crushed stone volume percentage); (unit: %vol.) S_SAND: Real (bottom sand content); (unit: % wt.) S_SILT: Real (bottom sludge content); (unit: % wt.) S_CLAY: Real (bottom clay content); (unit: % wt.) S_USDA_TEX: Real (bottom USDA soil texture classification); (unit: name) S_REF_BULK: Real (bottom soil bulk density); (unit: kg/dm3.) S_OC: Real (underlying organic carbon content); (unit: % weight) S_PH_H2O: Real (bottom pH) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying adhesive layer soil); (unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of the bottom soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation); (unit: %) S_TEB: Real (underlying exchangeable base); (unit: cmol/kg) S_CACO3: Real (bottom carbonate or lime content) (unit: % weight) S_CASO4: Real (bottom sulfate content); (unit: % weight) S_ESP: Real (underlying exchangeable sodium salt); (unit: %) S_ECE: Real (underlying conductivity). (Unit: dS/m) The database is divided into two layers, with the top layer (T) soil thickness (0-30 cm) and the bottom layer (S) soil thickness (30-100 cm). For other attribute values, please refer to the HWSD1.2_documentation documentation.pdf, The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description and HWSD.mdb.
Meng Xianyong, Wang Hao
The output data of the distributed eco hydrological model (gbehm) in the upper reaches of Heihe River includes the spatial distribution data series of 1-km grid. Region: Heihe River (Yingluo gorge), Beida River (Binggou new land), temporal resolution: Monthly Scale, spatial resolution: 1km, period: 1960-2014. Data include precipitation, evapotranspiration, runoff depth, soil volume water content (0-100cm). All data are in ASCII format. Please refer to the basin.asc file in the reference directory for the spatial range of the basin. Projection parameters of model results: sphere_Arc_Info_Lambert_Azimuthal_Equal_Area
YANG Dawen
On July 3, 2012, airborne ground synchronous observation was carried out in plmr sample belt near Linze station. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: According to the typical ground surface type represented by three points near Linze station and taking part of neutron tube observation into account, the three routes from northwest to southeast are designed, with an interval of 200 m, a design altitude of about 300 m and a plmr ground resolution of 100 m. According to the observation characteristics of the route and plmr, three observation transects are designed on both sides of the route, each of which is about 6 km long. From west to East are L1, L2 and L3 respectively. Among them, L1 and L2 are centered on the middle route, 80 m apart; L2 and L3 are 200 m apart. Four hydroprobe data acquisition systems (HDAS, ref. 2) were used to measure at the same time. Measurement content: About 4500 points on the sample belt were obtained, each point was observed twice, that is to say, in each sampling point, once in the film (marked as a in the data record) and once out of the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Note: the observation date coincides with the irrigation of large area of farmland in this area, which makes it difficult for the observer to move forward, the field block is difficult to enter, and the observation point position deviates from the preset point position. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 2000-2012. The data include evapotranspiration, runoff depth and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
YANG Dawen
The first dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 4 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The second dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 15 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The third dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 26 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The measurements were conducted at a sampling plot southeast to the Daman Superstation with an area of around 100 m × 100 m, which was dominantly planted with maize. Steven Hydro probes were used to collect soil moisture and other measurements with an interval of 5 m. For each sampling point, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within this sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, LAI, vegetation water content, canopy height, row distance and leaf chlorophyll content. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
The data set includes soil organic carbon concentrations data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The data of soil moisture in the Pailougou include the grassland on the shady slope of 2700m above sea level and the Picea crassifolia forest of 2800m above sea level. The soil water content monitoring system EM50 was used to measure the water content in five soil layers, 10cm, 20cm, 30cm, 40cm and 60cm respectively. The in-forest survey period is from June 2012 to September 2012, and there are also data for June 2013. The meadows were measured from June 2013 to October 2013. The measurement results are all volume water content in%.
HE Zhibin
This dataset includes soil moisture and soil temperature observations of 75 BNUNET nodes during the period from May to September 2012 (UTC+8), which is one type of WSN nodes in the Heihe eco-hydrological wireless sensor network (WSN). The BNUNET located in the observation matrix of the HiWATER artificial oasis eco-hydrology experimental area. Each BNUNET node observes the soil temperature at 4 cm, 10 cm and 20 cm depth, and soil moisture at 4 cm depth with 10 minutes interval. This dataset can be used in the estimation of surface hydrothermal variables and their validation, eco-hydrological research, irrigation management and so on. The detail description please refers to "Data introduction.docx".
Liu Jun, KOU Xiaokang, MA Mingguo
The data set contains observation data from the Tianlaochi small watershed automatic weather station. The latitude and longitude of the station are 38.43N, 99.93E, and the altitude is 3100m. Observed items are time, average wind speed (m/s), maximum wind speed (m/s), 40-60cm soil moisture, 0-20 soil moisture, 20-40 soil moisture, air pressure, PAR, air temperature, relative humidity, and dew point temperature , Solar radiation, total precipitation, 20-40 soil temperature, 0-20 soil temperature, 40-60 soil temperature. The observation period is from May 25, 2011 to September 11, 2012, and all parameter data are compiled on a daily scale.
ZHAO Chuanyan, MA Wenying
On July 26, 2012, the airborne ground synchronous observation was carried out in the plmr quadrat in the dense observation area of Daman. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the matrix of the dense observation area of Daman, and the detailed plan with an area of 3.0KM × 2.4km is selected to carry out synchronous observation on the underlying surface of oasis. The selection of the sample is mainly based on the representativeness of the surface coverage, accessibility and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 5 splines (east-west distribution) were collected at an interval of 450 m in the east-west direction. Each line has 31 points (north-south direction) at an interval of 100 m, and 5 hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 150 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
The dataset of ground truth measurements synchronizing with airborne WiDAS mission was obtained in the Linze grassland foci experimental area on May 30, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included the land surface temperature measured by the hand-held infrared thermometer in the reed plot A, the saline plots B and C, the alfalfa plot D and the barley plot E, the maximum of which were 120m×120m and the minimum were 30m×30m, and soil gravimetric moisture, volumetric moisture, and soil bulk density after drying measured by the cutting ring and the mean soil temperature from 0-5cm measured by the probe thermometer in plot A, B and C; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity measured by the POGO soil sensor, and the mean soil temperature from 0-5cm measured by the probe thermometer in plot D and E. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
CAO Yongpan, CHAO Zhenhua, GE Chunmei, HAN Xujun, HU Xiaoli, HUANG Chunlin, LIANG Ji, WANG Shuguo, WU Yueru, FENG Lei, YU Fan, WANG Jing
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 Jun. 19, 2008. GPR observations were also carried out in one sampling strip. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity were acquired by the POGO soil sensor, and the mean soil temperature from 0-5cm by the probe thermometer. Those provide reliable ground data for retrieval and validation of the surface temperature and evapotranspiration from remote sensing approaches. Four files were included, ASAR data, No. 1, 2 and 3 quadrates data.
CAO Yongpan, GE Chunmei, HAN Xujun,
The dataset of ground truth measurements synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained along the sample lines 1, 2, 3, 4, 5 and 6 of the Linze grassland foci experimental area on May 25, 2008. Complementary measurements were carried out along Line 7 on Jun. 2. 25 points at intervals of 100m were selected at each line. Simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity measured by the POGO soil sensor, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured three times by the hand-held infrared thermometer in L1, L2, L3 and L4; soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured three times by the hand-held infrared thermometer in L5 and L6; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, the mean soil temperature from 0-5cm measured by the probe thermometer, and the surface radiative temperature measured by the hand-held infrared thermometer, and soil gravimetric moisture, volumetric moisture, and soil bulk density measured by the cutting ring in L7. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
CHAO Zhenhua, GE Chunmei, HAN Xujun, HUANG Chunlin, RAN Youhua, SONG Yi
The dataset of soil frozen penetration measured by the soil frozen tube was obtained at the super site (100m×100m, pure Qinghai spruce) around the Dayekou Guantan forest station. Observation time was 8:00 each morning from Jun. 1 to Dec. 31, 2008. The soil frozen tube was laid beneath the spruce for diurnal soil frozen depth changes and the maximum depth (cm) was recorded.
TAN Junlei
The data set includes soil pH data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
This data set comprises the plateau soil moisture and soil temperature observational data based on the Tibetan Plateau, and it is used to quantify the uncertainty of model products of coarse-resolution satellites, soil moisture and soil temperature. The observation data of soil temperature and moisture on the Tibetan Plateau (Tibet-Obs) are from in situ reference networks at four regional scales, which are the Nagqu network of cold and semiarid climate, the Maqu network of cold and humid climate, and the Ali network of cold and arid climate，and Pali network. These networks provided representative coverage of different climates and surface hydrometeorological conditions on the Tibetan Plateau. - Temporal resolution: 1hour - Spatial resolution: point measurement - Measurement accuracy: soil moisture, 0.00001; soil temperature, 0.1 °C; data set size: soil moisture and temperature measurements at nominal depths of 5, 10, 20, 40 - Unit: soil moisture, cm ^ 3 cm ^ -3; soil temperature, °C
BOB Su, YANG Kun
A total of 137 soil samples of different vegetation types, different altitudes and different terrains were collected from June 2012 to August 2012. The soil layer of each sample point was divided into three layers of 0-10cm, 10-20cm and 20-30cm, with an altitude of 2700-3500m m. The vegetation types were divided into five types: Picea crassifolia forest, Sabina przewalskii, subalpine scrub meadow, grassland and dry grassland. At the same time of sampling, hand-held GPS is used to record the location information and environmental information of each sampling point, including longitude, latitude, altitude, slope, aspect, terrain curvature, vegetation type, soil thickness, maximum root depth, etc. Soil bulk density: The measurement method of soil bulk density is to put the sample into an envelope and dry it in an oven at 105℃ for 24 hours, then take it out and place it for 30 minutes to weigh. The ratio of the weighing result to the volume of the ring cutter is the soil bulk density, and the unit is g/cm3. Soil mechanical composition: hydrometer method is used to measure the soil mechanical composition, which includes the content of soil sand, silt and clay.
ZHAO Chuanyan, MA Wenying
The data is based on the Harmonized World Soil Database version 1.1 (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). The data source of China is 1: 1 million soil data in the second national land survey provided by the Nanjing Soil Research Institute. The data can provide model input parameters for modelers, in agricultural perspective, it can be used to study eco-agricultural zoning, food security and climate change. The data format is grid and the projection is WGS84. The soil classification system used is mainly FAO-90. The main fields of the soil property table include: SU_SYM90 (the soil name in the FAO90 soil classification system); SU_SYM85 (FAO85 classification); T_TEXTURE (top soil texture); DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS (19.5); AWC_CLASS (soil effective water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification with obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); T_GRAVEL: Real (gravel volume percentage); T_SAND: Real (sand content); T_SILT: Real (silt content); T_CLAY: Real (clay content); T_USDA_TEX: Real (USDA soil texture classification); T_REF_BULK: Real (soil bulk density); T_OC: Real (organic carbon content); T_PH_H2O: Real (pH) T_CEC_CLAY: Real (cation exchange capacity of clay soil); T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation); T_TEB: Real (exchangeable base); T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content); T_ESP: Real (exchangeable sodium salt); T_ECE: Real (conductivity). The attribute field beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm). For the meaning of specific attribute values, please refer to the documentation * .pdf and database * .mdb in the folder.
Food and Agriculture Organization of the United Nations（FAO）, International Institute for Applied Systems Analysis
The dataset of ground truth measurements synchronizing with MODIS, ALOS PALSAR and AMSR-E was obtained in the Biandukou foci experimental area on May 24, 2008. Observation items included: (1) the surface temperature in No. 1 (grassland), No. 2 (the rape land), No. 3 (the rape land), No. 4 (the wheat land) and No. 5 quadrate (wheat and rape); (2) the soil moisture by WET in No. 2 quadrate; (3) GPR and WET; (4) The spectrum by ASD Fieldspec FRTM (Boulder, Co, USA), 350nm-2500nm, 3nm for the visible near-infrared band and 10nm for the shortwave infrared band). The spectrum data were archived in the ASCII format, with the first five rows as the file header and the following two columns as wavelength (nm) and reflectance (percentage) respectively, and can be opened by .txt or wordpad. The .txt file was not reflectance but intermediate file for further calculation. Raw data were binary files direct from ASD (by ViewSpecPro). The surface radiative temperature and the physical temperature were measured by the handheld infrared thermometer. Besides, the cover type was also recorded. The data can be opened by Microsoft Office. Soil moisture was acquired by WET and the cutting ring. The data can be opened by Microsoft Office. Six data files were included, soil moisture, the surface temperature, GPR, coverage photos and preprocessed data, ground objects spectrum and satellite images.
BAI Yunjie, CAO Yongpan, CHE Tao, DU Ziqiang, HAO Xiaohua, WANG Zhixia, WU Yueru, CHAI Yuan, CHANG Sheng, QIAN Yonggang, SUN Xiaoqing, WANG Jindi, YAO Dongping, ZHAO Shaojie, ZHENG Yue, ZHAO Yingshi, LI Xiaoyu, PATRICK Klenk, HUANG Bo, LI Shihua, LUO Zhen
Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.
YUE Tianxiang, ZHAO Na
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, and provide parameters and verification data for the development of response and feedback models of permafrost, glacier and snow cover to global changes under GIS framework. On the other hand, the system collates and rescues valuable cryospheric data to provide a scientific, efficient and safe management and analysis tool. Chinese Cryospheric Information System contains three basic databases of different research regions. The basic database of Urumqi river basin is one of three basic databases, which covers the Urumqi river basin in tianshan mountain, east longitude 86-89 °, and north latitude 42-45 °, mainly containing the following data: 1. Cryospheric data.Include: Distribution of glacier no. 1 and glacier no. 2; 2. Natural environment and resources.Include: Terrain digital elevation: elevation, slope, slope direction; Hydrology: current situation of water resource utilization;Surface water; Surface characteristics: vegetation type;Soil type;Land resource evaluation map;Land use status map; 3. Social and economic resources: a change map of human action; Please refer to the documents (in Chinese): "Chinese Cryospheric Information System design. Doc" and "Chinese Cryospheric Information System data dictionary. Doc".
LI Xin
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
WANG Xufeng, KANG Jian, Li Dazhi, Wang Zuocheng, Dong Cunhui, LI Xin, MA Mingguo
In the ecosystem, soil and vegetation are two interdependent factors. Plants affect soil and soil restricts vegetation. On the one hand, there are a lot of nutrients such as carbon, nitrogen and phosphorus in the soil. On the other hand, the availability of soil nutrients plays a key role in the growth and development of plants, directly affecting the composition and physiological activity of plant communities, and determining the structure, function and productivity level of ecosystems. Soil moisture content (or soil moisture content): In the 9 sections from Daxihaizi to taitema lake in the lower reaches of Tarim River, plant sample plots are set in the direction perpendicular to the river channel according to the arrangement of groundwater level monitoring wells. Dig one soil profile in each sample plot, collect one soil sample from 0-5 cm, 5-15 cm, 15-30 cm, 30-50 cm, 50-80 cm, 80-120 cm and 120-170cm soil layers from bottom to top in each profile layer, each soil sample is formed by multi-point sampling and mixing of corresponding soil layers, each soil layer uses aluminum boxes to collect soil samples, weighs wet weight on site, and measures soil moisture content (or soil moisture content) by drying method. Soil nutrient: the mixed soil sample is used for determining soil nutrient after removing plant root system, gravel and other impurities, air-drying indoors and sieving. Organic matter is heated by potassium dichromate, total nitrogen is treated by semi-micro-Kjeldahl method, total phosphorus is treated by sulfuric acid-perchloric acid-molybdenum antimony anti-colorimetric method, total potassium is treated by hydrofluoric acid-perchloric acid-flame photometer method, effective nitrogen is treated by alkaline hydrolysis diffusion method, effective phosphorus is treated by sodium bicarbonate leaching-molybdenum antimony anti-colorimetric method, effective potassium is treated by ammonium acetate leaching-flame photometer method, PH and conductivity are measured by acidimeter and conductivity meter respectively (water to soil ratio is 5: 1). Soil water-soluble total salt was determined by in-situ salinity meter. Drought stress is the most common form of plant adversity and is also the main factor affecting plant growth and development. Plant organs will undergo membrane lipid peroxidation under adverse circumstances, thus accumulating malondialdehyde (MDA), the final decomposition product of membrane lipid peroxide. MDA content is an important indicator reflecting the strength of membrane lipid peroxidation and the damage degree of plasma membrane, and is also an important parameter reflecting the damage of water stress to plants. At the same time, under adverse conditions, the increased metabolism of reactive oxygen species in plants will lead to the accumulation of reactive oxygen species or other peroxide radicals, thus damaging cell membranes. Superoxide dismutase (SOD) and peroxidase (POD) in plants can remove excess active oxygen in plants under drought and other adversities, maintain the metabolic balance of active oxygen, protect the structure of the membrane, and finally enhance the resistance of plants to adversities. The analysis samples take Populus euphratica, Tamarix chinensis and Phragmites communis as research objects. According to the location of groundwater monitoring wells, six sample plots are set up starting from the riverside, with an interval of 50 m between each sample plot, which are sample plots 1, 2, 3, 4, 5 and 6 in turn. Fresh leaves of plants are collected, stored at low temperature, and pretreated (dried or frozen) on the same day. PROline (Pro), cell membrane system protective enzymes superoxide dismutase (SOD) and peroxidase (POD) were tested indoors. Preparation of enzyme solution: weigh 0.5g of fresh material and add 4.5mL pH7.8 with ph 7.8. The materials were homogenized in a pre-frozen mortar, which was placed in an ice bath. Centrifuge at 10000 r/min for 15 min. The supernatant was used for determination of superoxide dismutase, peroxidase and malondialdehyde (MDA). PRO determination: put 0.03 g of material into a 20 mL large test tube, add 10mL ammonia-free distilled water, seal it, put it in a boiling water bath for 30min, cool it, filter, filtrate 5 mL+ ninhydrin 5 mL, develop color in boiling water for 60min, and extract with toluene. The extract was colorized with Shimadzu UV-265 UV spectrophotometer at 515 nm. SOD activity was measured by NBT photoreduction. The order of sample addition for enzyme reaction system is: pH 7.8 PBS 2.4mL+ riboflavin 0.2 mL+ methionine 0.2 mL+EDTA0.1 mL+ enzyme solution 0.1 mL+NBT0.2 mL. Then the test tube was reacted under 40001ux light for 20 min, and photochemical reduction was carried out. SOD activity was measured at 650 nm wavelength by UV-265 ultraviolet spectrophotometer. POD activity determination: the reaction mixture was 50 ml PBS with pH 6.0+28 μ L guaiacol+19 UL30% H2O2. 2 mL of reaction mixture +1 mL of enzyme solution, immediately start timing, reading every 1 min, reading at 470 nm. Determination of chlorophyll: ethanol acetone mixed solution method. After cutting the leaves, the mixed solution of 0.2 g and acetone: absolute ethanol = 1: 1 was weighed as the extraction solution. After extracting in the dark for 24 h, the leaves turned white and chlorophyll was dissolved in the extraction solution. The OD value of chlorophyll was measured by spectrophotometer at 652nm. Determination method of soluble sugar: phenol sulfate method is adopted. (1) The standard curve is made by taking 11 20 ml graduated test tubes, numbering them from 0 to 10 points, and adding solution and water according to Table 1 respectively. Then add 1 ml of 9% phenol solution to the test tube in sequence, shake it evenly, then add 5 ml of concentrated sulfuric acid from the front of the tube for 5 ~ 20 s, the total volume of the colorimetric solution is 8 ml, and leave it at constant temperature for 30 minutes for color development. Then, with blank as control, colorimetric determination was carried out at 485 nm wavelength. With sugar as abscissa and optical density as ordinate, a standard curve was drawn and the equation of the standard curve was obtained. (2) Extraction of soluble sugar: fresh plant leaves are taken, surface dirt is wiped clean, cut and mixed evenly, 0.1-0.3 g are weighed, 3 portions are respectively put into 3 calibration test tubes, 5-10 ml distilled water is added, plastic film is sealed, extraction is carried out in boiling water for 3O minutes, the extraction solution is filtered into a 25 ml volumetric flask, repeated flushing is carried out, and the volume is fixed to the calibration. (3) Absorb 0.5 g of sample solution into the test tube, add 1.5 ml of distilled water, and work out the content of soluble sugar in the same way as the standard curve. The amount of solution and water in each test tube Pipe number 0 1-2 3-4 5-6 7-8 9-10 1.100μg/L sugar solution 0.20 0.40 0.60 1.0 2. water/ml 2.0 1.8 1.6 1.4 1.2 1.0 3. Soluble sugar content/μ g 0 20 40 60 80 100 Determination of malondialdehyde: thiobarbituric acid method. Fresh leaves were cut to pieces, 0．5 g was weighed, 5% TCA5 ml was added, and the homogenate obtained after grinding was centrifuged at 3 000 r／rain for 10 rain. Take 2 ml supernatant, add 0.67% TBA 2 ml, mix, boil in 100 water bath for 30 rain, cool and centrifuge again. Using 0.67% TBA solution as blank, the OD values at 450, 532 and 600 nm were determined. Methods for analysis and testing of plant hormones (GA3, ABA, CK, IAA): 0.1 0.005 g plant samples were taken and ground in liquid nitrogen. 500μl methanol was extracted overnight at 4℃. Centrifuge the sample and freeze-dry the supernatant. 30μl10％% CH3CN dissolved the sample. 10μl of sample solution was analyzed by HPLC. The external standard method was used to quantify plant hormones. Standard plant hormones were purchased from sigma Company. See (Ruan Xiao, Wang Qiang, et al., 2000, Journal of Plant Physiology.26 (5), 402-406) for analysis methods.
CHEN Yaning, HAO Xingming
The dataset of the automatic meteorological observations (2008-2009) was obtained at the Pailugou grassland station (E100°17'/N38°34', 2731m) in the Dayekou watershed, Zhangye city, Gansu province. The items included multilayer (1.5m and 3m) of the air temperature and air humidity, the wind speed (2.2m and 3.7m) and direction, the air pressure, precipitation, the global radiation, the net radiation, co2 (2.8m and 3.5m), the multilayer soil temperature (10cm, 20cm, 40cm, 60cm, 120cm and 160cm), soil moisture (10cm, 20cm, 40cm, 60cm, 120cm and 160cm), and soil heat flux (5cm, 10cm and 15cm). For more details, please refer to Readme file.
HUANG Guanghui, WU Lizong, Qu Yonghua, LI Hongxing, ZHOU Hongmin, Zhang Zhihui
1、 Data Description: the data includes the content of silica in snowmelt water and soil water in hulugou small watershed from May 2013 to April 2014. 2、 Sampling location: the sampling point of snowmelt water is located near 600m below No.2 meteorological station, with ground elevation of 3514.45m, longitude and latitude of 99 ° 53 ′ 20.655 ″ e, 38 ° 14 ′ 14.987 ″ n. The sampling point of soil water is located at 300m above and below the No.2 meteorological station, with the longitude and latitude of 99 ° 53 ′ 31.333 ″ E and 38 ° 13 ′ 50.637 ″ n. 3、 Measurement method: the content of silica in the sample was measured by ICP-AES. Silicon dioxide is replaced by the value of Si in the solution.
SUN Ziyong, CHANG Qixin
First, Data Description The data includes stable hydrogen and oxygen isotope data of snow melt water, river water and soil water from July 2013 to April 2014. Second, Sampling Sites The snowmelt water sampling point is located in the middle of the third area, with a latitude and longitude of 99°53′28.004′′E, 38°13′25.781′′N, and the number of acquisitions is 3 times; The river water sampling point is located at the exit of the Hulugou Basin, with a latitude and longitude of 99°52′47.7′′E, 38°16′11′′N, and the sampling frequency is once a week; The soil water sampling point is located in the middle and lower part of the Hongnigou catchment area, with a sampling depth of 90cm and 180cm underground, and a latitude and longitude of 99°52'25.98′′E, 38°15′36.11′′N. Third, Testing Method The samples were measured by L2130-i ultra-high precision liquid water and water vapor isotope analyzer.
CHANG Qixin, SUN Ziyong
The data set contains cosmic ray instrument (CRS) observations from January 1, 2016 to December 31, 2016.The station is located in gansu province zhangye city da man irrigated area farmland, under the surface is corn field.The longitude and latitude of the observation point are 100.3722e, 38.8555n, and 1556m above sea level. The bottom of the instrument probe is 0.5m from the ground, and the sampling frequency is 1 hour. Original observations of cosmic ray instruments include: voltage Batt (V), temperature T (c), relative humidity RH (%), pressure P (hPa), fast neutron number N1C (hr), thermal neutron number N2C (hr), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s).The data published are processed and calculated. The data headers include Date Time, P (pressure hPa), N1C (fast neutron number/hour), N1C_cor (fast neutron number/hour with revised pressure) and VWC (soil volume moisture content %). The main processing steps include: 1) data filtering There are four criteria for data screening :(1) data with voltage less than and equal to 11.8 volts are excluded;(2) remove the data of air relative humidity greater than and equal to 80%;(3) data whose sampling interval is not within 60±1 minute are excluded;(4) the number of fast neutrons removed changed by more than 200 in one hour compared with that before and after.In addition, the missing data was supplemented by -6999. 2) air pressure correction According to the fast neutron pressure correction formula mentioned in the instrument instruction manual, the original data were revised to obtain the revised fast neutron number N1C_cor. 3) instrument calibration In the process of calculating soil moisture, N0 in the calculation formula should be calibrated.N0 is the number of fast neutrons under the condition of soil drying. The measured soil moisture (or through relatively dense soil moisture wireless sensor) m (Zreda et al. Here, according to Soilnet soil water data in the source area of the instrument, the instrument was calibrated to establish the relationship between soil volumetric water content v and fast neutrons.Selection of dry and wet conditions are the obvious difference of June 26, 2012-27 and July 16-17, four days of data, including June 26-27 rate data showed that soil moisture is small, so the selection of 4 cm, 10 and 20 cm as the rate of the three values of average data, its range is 22% 30%, and July 16-17 rate data showed that soil moisture is bigger, so select 4 cm and 10 cm as two value average rate data, the range of 28% - 39%, final N0 an average of 3597. 4) soil moisture calculation According to the formula, the hourly soil water content data were calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo
The dataset of the survey at the sampling plots in the transit zone between oasis and desert was obtained in the Linze station foci experimental area. Observation items included: (1) soil moisture and temperature of the soil profiles (0-10cm, 10-20cm, 20-30cm and 30-40cm) measured by the cutting ring method (50cm^3, once each layer) and the probe thermometer (15cm, twice each layer) on May 25, 2008. Data were archived as Excel files. (2) biomass (green weight and dry weight, samples from 0.5m×0.5m) with photos measured by the plant harvesting in LY07 quadrate on Jun. 22, 2008. Data were archived as Excel files. (3) vegetation coverage measured by the diagonal method on Jun. 22, 2008. By estimating the coverage along the two diagonals, the total coverage of the plot can be developed. Data were archived as Excel files.
GAO Song, PAN Xiaoduo, Qian Jinbo, SONG Yi, WANG Yang, ZHU Shijie
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