This dataset includes the emissivity spectrum of typical ground objects in middle researches of the Heihe river basin. This dataset was acquired in oasis, desert, Gobi and wetland of experiment area. Time range starts from 2012-05-25 to 2012-07-18 (UTC+8). Instrument: MODEL 102F PORTABLE FTIR (Fourier Transform Infrared Spectrometer), Handheld infrared thermometer. Measurement methods: at the first step, measure the thermal radiance of cold blackbody, warm blackbody, sample and gold plate (Downwelling Radiance). The radiance of cold blackbody and warm blackbody was used to calibrate the instrument, and eliminate the “noise” caused by the device itself. The retrieval of emissivity and temperature was then performed using iterative spectrally smooth temperature-emissivity separation (ISSTES) algorithm. The retrieved emissivity spectrum range from 8 to 14 μm, with spectral resolution of 4cm-1. Dataset contains the original recorded spectra (in ASCII format) and the log files (in doc format). The processed data are emissivity curves (ASCII) that ranged from 8 to 14 μm, and the temperatures of samples. Thermal photos of the sample, digital photo of the scene and the object are recorded in some cases.
0 2019-09-12
This data was originated from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2017. This data set is AGNP data of 1:1 million residential place names in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AGNP): Attribute Item Description Fill in Example CLASS Geographical Name Classification Code AK NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township
0 2019-05-13
A small lysimeter was made by ourselves, which simulated the natural conditions and selected typical desert plants as the object to study the water consumption and its law. Repeat 3 times for each plant. In 2011, the experiment of physiological water demand and water consumption of desert plants was carried out with the soil water content kept at (50 ± 10)% of the field water capacity; in 2012, the experiment of physiological water demand and water consumption was carried out with the soil water content kept at (20 ± 5)% of the field water capacity under stress.
0 2020-03-12
The dataset of ground truth measurement synchronizing with the airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze station foci experimental area on Jun. 6, 2008. Observation items included: (1) soil moisture (0-5cm) measured by the cutting ring (50cm^3) along LY06, LY07 and LY08 strips (repeated nine times). The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by three handheld infrared thermometers (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) in LY06 and LY07 strips. There are 49 sample points in total and each was repeated three times synchronizing with the airplane. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
0 2019-09-11
Investigation of plant sample plots can reflect the structure and distribution of plant communities, the declining succession of plant communities and their interrelation with environmental changes, reveal the ecological damage process in the lower reaches of the Tarim River, and provide scientific basis for the environmental remediation of the Tarim River Basin in the large-scale development of the western part of the country. According to the difference of species composition of plant communities in different sections of 9 monitoring sections in the lower reaches of Tarim River, plant sample plots are set up along the direction perpendicular to the river course in each monitoring section. Due to the different vegetation growth in each section, the size and number of sample plots are not equal. Among them, the sample plot of 5m×5m is arranged on the section of the herbaceous community. 30m×30m sample plots are arranged on the section where vegetation grows sparsely or is basically free of herbaceous plants, and 4 15m× 15 m arbor and shrub sample plots are arranged at intervals of 15 m; 50m×50m sample plots are arranged on the section where arbor, shrub and grass vegetation all occupy a certain proportion. In each plot of 50×50m, four plots of 25m×25m are set at 25m intervals to record the individual number, coverage, DBH, basal diameter, height and crown width of each tree (or shrub). At the same time, 4 small sample plots of 5m×5m are set up in each sample plot to record the individual number, coverage, height and other indicators of each herbaceous plant, and GPS is used to locate and record the altitude and longitude and latitude of each sample plot. Data content includes: 1. word Document for Statistics of Plant Sample Land Survey Data from 2000, 2002 to 2007 2. 2000 Inventory of Plant Sample Sites in Lower Reaches of Tarim River (Akdun, Yahopumahan, Yingsu, Abodah, Keldayi Section Vegetation Coverage, Canopy Density, Root Weight, etc.) excel Table 3. excel Table of Plant Sample Plot Survey in Lower Reaches of Tarim River in August 2002 (Data on Individual Number, Crown Width, Plant Height, Density and Coverage of Plants in Akdun, Yingsu, Khaldayi, Arakan and Shidaoban Sections) 4. 2003 Inventory of Plant Sample Sites in Lower Reaches of Tarim River (Data on Individual Number, Crown Width, Plant Height, Density and Base Diameter of Plants in Lower Reaches of Tahe River and Herbaceous Biomass in Akerdun Section) excel Table 5. In September 2004, the lower reaches of the Tarim River plant sample plot questionnaire (data of individual number, crown width, plant height, basal diameter (or DBH), coverage and biomass) excel table of the lower reaches of the Tarim River in Yahefu Mahan, Yingsu, Abodah Le, Khaldayi, Tugamale, Arakan, Yiganbuma and Kaogan sections 6. In July 2005, the lower reaches of Tarim River plant sample plot questionnaire (9 monitoring sections in the lower reaches of Tahe River and data of individual number, crown width, plant height, basal diameter (or DBH) and coverage of plants in taitema lake, and herbaceous biomass data in Akerdun section) excel table 7. In July 2006, the lower reaches of Tarim River plant sample plot questionnaire (the number of individual plants, crown width, plant height, basal diameter (or DBH) and herbaceous biomass data of Akerdun section in 9 monitoring sections in the lower reaches of Tahe River) excel table 8. July 2007, the lower reaches of Tarim river plant sample plot questionnaire (the number of individual plants, crown width, plant height, basal diameter (or DBH) and herbaceous biomass data of akdun section in 9 monitoring sections in the lower reaches of Tahe river) excel table
0 2020-06-08
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
0 2020-03-13
Precipitation is one of the elements of meteorological monitoring and a measurement basis of regional precipitation. Precipitation is the only source of water for plants’ survival in mountain areas. Therefore, precipitation is the main link of the forest hydrological cycle. This data only provides precipitation of the Pailugou watershed during the growing season.
0 2020-07-30
This data uses soil conversion functions to take sand, silt, clay, organic matter, and bulk density as inputs to estimate soil hydrological parameters, including parameters of the Clapp and Hornberger function and van Genuchten and Mualem function, field water holding capacity, and withering coefficient. Median and coefficient of variation (CV) provide estimates. The data set is in a raster format with a resolution of 30 arc seconds, and the soil is layered vertically into 7 layers with a maximum thickness of 1.38 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 meters). The data is stored in NetCDF format. The data file name and its description are as follows: 1. THSCH.nc: Saturated water content of FCH 2. PSI_S.nc: Saturated capillary potential of FCH 3. LAMBDA.nc: Pore size distribution index of FCH 4. K_SCH.nc: Saturate hydraulic conductivity of FCH 5. THR.nc: Residual moisture content of FGM 6. THSGM.nc: Saturated water content of FGM 7. ALPHA.nc: The inverse of the air-entry value of FGM 8. N.nc: The shape parameter of FGM 9. L.nc: The pore-connectivity parameter of FGM 10. K_SVG.nc: Saturated hydraulic conductivity of FGM 11. TH33.nc: Water content at -33 kPa of suction pressure, or field capacity 12. TH1500.nc: Water content at -1500 kPa of suction pressure, or permanent wilting point
0 2020-06-08
The dataset of surface roughness measurements was obtained in No. 1 and 2 quadrates of the E’bao foci experimental area during the pre-observation period. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. With the roughness board 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be calculated based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were archived. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further validation.
0 2019-09-15
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
0 2020-06-10
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