Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 660

  • Long-term glacier melt fluctuations of Qiangyong Glacier on the Tibetan Plateau over the past 2500 yr

    Long-term glacier melt fluctuations of Qiangyong Glacier on the Tibetan Plateau over the past 2500 yr

    The source of the data is paper: Zhang, J.F., Xu, B.Q., Turner, F., Zhou, L.P., Gao, P., Lü, X.M., & Nesje, A. (2017). Long-term glacier melt fluctuations over the past 2500 yr in monsoonal high asia revealed by radiocarbon-dated lacustrine pollen concentrates. Geology, 45(4), 359-362. In this paper, the researcher of Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Baiqing Xu, with his postdoctoral fellow, Jifeng Zhang, and collaborators from Peking University and other institutions, propose that the OPE (“old pollen effect”, the offset between the calibrated 14C ages of pollen in lake sediments and the sediment depositional age) as a new indicator of glacier melt intensity and fluctuations by measuring the radiocarbon ages of the sediments of the proglacial lake of Qiangyong Glacier on the southern Tibetan Plateau with multi-methods (bulk organic matter, pollen concentrates and plant residues). This research suggests that hemispheric-scale temperature variations and mid-latitude Westerlies may be the main controllers of the late Holocene glacier variability in monsoonal High Asia. It also shows that the 20th-century glacier melt intensity exceeded that of two historical warm epochs (the Medieval Warm Period, and the Iron/Roman Age Optimum) and is unprecedented at least for the past 2.5 k.y. This data is provided by the author of the paper, it contains long-term glacier melt fluctuations of Qiangyong Glacier over the past 2500 yr reconstructed by the OPE. A 3.06-m-long core (QYL09-4) and a 1.06-m-long parallel gravity core (QY-3) were retrieved by the researchers from the depositional center of Qiangyong Co. Using a new composite extraction procedure, they obtained relatively pure pollen concentrates and plant residue concentrates (PRC; >125 μm) from the finely laminated sediments. Bulk organic matter and the PRC and pollen fractions were used for 14C dating independently. All 14C ages were calibrated with IntCal13 (Reimer et al., 2013). The age-depth model is based on 210Pb and 137Cs ages and five 14C ages of PRC. Only the youngest PRC ages were used for the age-depth model, whereas older ages that produce a stratigraphic reversal and are apparently influenced by redeposited or aquatic plant material were rejected. The deposition model was constructed using the P_Sequence algorithm in Oxcal 4.2 (Bronk Ramsey, 2008). For the calculation of the offset between the calibrated pollen 14C ages and the sediment depositional age, 2σ intervals for interpolated ages according to the deposition model were subtracted from calibrated pollen ages (2σ span), resulting in the age offset between pollen and estimated sediment ages (ΔAgepollen). This data is radiocarbon ages and the calculated ΔAgepollen of core QYL09-4 from a proglacial lake of Qiangyong Glacier. The data contains fields as follows: Lab No. Dating Material Depth (cm) 14C age (yr BP) ∆Agepollen (≥95.4 % yrs) Sediment Age (CE) See attachments for data details: ZhangJF et al. 2017 GEOLOGY_Long-term glacier melt fluctuations over the past 2500 yr on the Tibetan Plateau.pdf.

    2020-06-16 716 9 View Details

  • Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983-2017)

    Dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983-2017)

    The dataset is a 34-year (1983.7-2017.6) high-resolution (3 h, 10 km) global SSR (surface solar radiation) dataset, which can be used for hydrological modeling, land surface modeling and engineering application. The dataset was produced based on ISCCP-HXG cloud products, ERA5 reanalysis data, and MODIS aerosol and albedo products with an improved physical parameterization scheme. Validation and comparisons with other global satellite radiation products indicate that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). This SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The unit is W/㎡, instantaneous value.

    2020-06-16 12357 619 View Details

  • The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.

    2020-06-15 1798 36 View Details

  • Landsat-based continuous monthly 30m×30m land surface LAI dataset in Qilian mountain area (1986-2017)

    Landsat-based continuous monthly 30m×30m land surface LAI dataset in Qilian mountain area (1986-2017)

    This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2017. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 5, Landsat 8 and sentinel 2 channels from Red and NIR channels. The data are synthesized monthly through Google Earth Engine cloud platform, and the missing pixels are interpolated by calculating the index of the model. The quality of the data is good, and it can be used in environmental change monitoring and other fields.

    2020-06-15 872 34 View Details

  • Landsat-based continuous monthly 30m×30m land surface LAI dataset in Qilian mountain area (2018)

    Landsat-based continuous monthly 30m×30m land surface LAI dataset in Qilian mountain area (2018)

    This data set includes the monthly synthesis of 30m*30m surface LAI (Leaf Area Index) products in Qilian mountain area in 2018. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels. The data are synthesized monthly through Google Earth Engine cloud platform, and the missing pixels are interpolated by calculating the index of the model. The quality of the data is good, and it can be used in environmental change monitoring and other fields.

    2020-06-15 649 28 View Details

  • Daily value dataset of 10m meteorological tower at Laohugou Glacier No.12 in the Qilian Mountains of China(V1.0) (2014-2018)

    Daily value dataset of 10m meteorological tower at Laohugou Glacier No.12 in the Qilian Mountains of China(V1.0) (2014-2018)

    This data is the log data set of the meteorological tower in 2014-2018 in Laohugou base camp of Qilian Mountains. "The 10 meter meteorological tower of Laohugou 12 glacier is located in the base camp, with an altitude of 4200 meters. Its observation elements include temperature, precipitation, wind speed, wind direction, relative humidity, air pressure, downward radiation, upward radiation, downward long wave radiation and upward long wave radiation, with a resolution of daily value. The meteorological instrument passes through China's meteorology After calibration and commissioning one belt, one road is connected with the CR1000 (Campbell), the -55 (CR1000) data collector. The data quality is complete. The data of many articles are all derived from this data. The Hexi Corridor nurtured by glacier water and melting glacier in Qilian Mountains is an important channel for the national strategic "one belt and one road". The study of its changes has great influence on Gansu, the whole country and the whole country. Therefore, this data has great research value and application value. "

    2020-06-11 907 6 View Details

  • Dataset of soil water erosion modulus with 5 m resolution in 11 watersheds of Tibet (2018)

    Dataset of soil water erosion modulus with 5 m resolution in 11 watersheds of Tibet (2018)

    1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 5 m in the year of 2017 in Tibet. 2)Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation results and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.

    2020-06-11 617 5 View Details

  • Dataset of soil water erosion modulus with 5 m resolution in 18 watersheds of Thailand (2018)

    Dataset of soil water erosion modulus with 5 m resolution in 18 watersheds of Thailand (2018)

    1) The data includes the soil erosion modulus of 18 watersheds with a resolution of 5 m in the year of 2017 in Thailand. 2) Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 18 watersheds of Thailand respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation result and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 18 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion intensity is of great significance for studying the present situation of soil erosion in Pan third polar region and better implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.

    2020-06-11 588 1 View Details

  • 1-km gridded datasets for gross domestic product of five key nodes  along One Belt One Road (2015)

    1-km gridded datasets for gross domestic product of five key nodes along One Belt One Road (2015)

    Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. We have collected the published GDP data, then obtained the 1-km gridded datasets for GDP of 2015 in five key nodes over Bengal and Myanmar, including Dacca, Chittagong, Kyaukpyu, Rangoon and Mandalay. To solve the problem of missing data existing in the current datasets, we will apply kriging and function interpolation methods to fill gaps. We will also develop the multi-source data fusion method based on geostatistics to achieve the GDP predictions of time continuously and high spatial resolution.

    2020-06-11 540 1 View Details

  • Gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road (GPWV4.0) (2015)

    Gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road (GPWV4.0) (2015)

    Gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from socioeconomic data and applications center of Columbia University, USA. The prejection transform and extraction processes were done to generate the gridded population with 1km spaital resolution of the 34 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data by the area weighted method for each administrative unit. Accurate population data at grid level are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.

    2020-06-11 467 1 View Details

  • The productivity data of land resources for the Belt and Road

    The productivity data of land resources for the Belt and Road

    The data of Land Resources Productivity for “B&R” includes: 1. Areas of cultivated land resources in regions and countries along the “B&R”; 2. Data on grain planting area and total grain output in regions and countries along the “B&R”; 3. Major crops (rice, wheat, corn) in regions and countries along the route Planting area and production data; 4. Areas of grassland resources in the region and along the country; 5. Number of livestock (bovine, sheep) in the region and along the country. Source: Cultivated land and population data from the World Bank database; food, crop, grassland, and livestock data are from FAO. Data application: According to the data provided, the basic characteristics analysis of land resources and the analysis of land resource output can be carried out in the Belt and Road region and the countries along the route, so that the land resource productivity evaluation analysis can be carried out.

    2020-06-11 545 6 View Details

  • Land cover of core countries of the Belt and Road in 2015 (Version 1.0)

    Land cover of core countries of the Belt and Road in 2015 (Version 1.0)

    Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).

    2020-06-11 1707 59 View Details

  • Spectral characteristics of sample plots in typical countries along the belt (2015)

    Spectral characteristics of sample plots in typical countries along the belt (2015)

    Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.

    2020-06-11 638 0 View Details

  • The combined 1000 yr temperature reconstruction records derived from a stalagmite and tree rings (1000 A.D.-2000 A.D.)

    The combined 1000 yr temperature reconstruction records derived from a stalagmite and tree rings (1000 A.D.-2000 A.D.)

    The application of general circulation models (GCMs) can improve our understanding of climate forcing. In addition, longer climate records and a wider range of climate states can help assess the ability of the models to simulate climate differences from the present. First, we try to find a substitute index that combines the effects of temperature in different seasons and then combine it with the Beijing stalagmite layer sequence and the Qilian tree-ring sequence to carry out a large-scale temperature reconstruction of China over the past millennium. We then compare the results with the simulated temperature record based on a GCM and ECH-G for the past millennium. Based on the 31-year average, the correlation coefficient between the simulated and reconstructed temperature records was 0.61 (with P < 0.01). The asymmetric V-type low-frequency variation revealed by the combination of the substitute index and the simulation series is the main long-term model of China's millennium-scale temperature. Therefore, solar irradiance and greenhouse gases can account for most of the low-frequency variation. To preserve low-frequency information, conservative detrended methods were used to eliminate age-related growth trends in the experiment. Each tree-ring series has a negative exponential curve installed while retaining all changes. The four fields of the combined 1000-yr (1000 AD-2000 AD) reconstructed temperature records derived from stalagmite and tree-ring archives (excel table) are as follows: 1) Year 2) Annual average temperature reconstruction 3) Reconstructed temperature deviation 4) Simulated temperature deviation

    2020-06-09 10279 13 View Details

  • Inventory dataset of glacial lakes in the Sikkim Region, India (2000)

    Inventory dataset of glacial lakes in the Sikkim Region, India (2000)

    This glacial lake inventory receives joint support from International Centre for Integrated Mountain Development (ICIMOD) and United Nations Environment Programme/Regional Resource Centre, Asia and the Pacific (UNEP/RRC-AP). 1. This glacial lake inventory referred to Landsat 4/5 (MSS, TM/1984/1999), Landsat 7 (TM & ETM+), IRS-1C, LISS-III (1995 IRS-1C), (1997 IRS-1D) and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in 2000. 2. Glacial Lake Inventory Coverage: Tista Basin, Sikkim Region 3. Glacial Lake Inventory includes: glacial lake inventory, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 4. Projection parameter: Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Sikkim) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00’00” E Central parallel: 26°00’00” N Scale factor: 0.998786 Standard parallel 1: 23°09’28.17” N Standard parallel 2: 28°49’8.18” N Minimum X Value: 2545172 Maximum X Value: 2645240 Minimum Y Value: 1026436 Maximum Y Value: 1163523 For a detailed data description, please refer to the data file and report.

    2020-06-09 5069 11 View Details

  • Inventory of glacial lakes in Pakistan (2003-2004)

    Inventory of glacial lakes in Pakistan (2003-2004)

    This glacial lake inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resource Centre, Asia and The Pacific (UNEP/RRC-AP). 1. The glacial lake inventory adopts the Landsat remote sensing data and reflects the status of glacial lakes in the Pakistan region from 2003 to 2004. 2. In terms of spatial coverage, the glacial lake inventory covers the Swat, Chitral, Gilgit, Hunza, Shigar, Shyok, Upper, Indus, Shingo, Astor and Jhelum river basins in the upper reaches of the Indus River. 3. The glacial lake inventory data include the glacial lake code, glacial lake type, glacial lake area, distance between the glacier and the glacial lake, glaciers related to the glacial lake, etc. For detailed descriptions of the data, please refer to the data file and report.

    2020-06-09 8724 29 View Details

  • Glacier inventory dataset of Nepal (2000)

    Glacier inventory dataset of Nepal (2000)

    This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP)。 1、The glacier inventory uses the remote sensing data of Landsat,reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4、Data Projection: Grid Zone IIA Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 23°09'28.17"N Standard parallel 2: 28°49'8.18"N Minimum X Value: 1920240 Maximum X Value: 2651760 Minimum Y Value: 914398 Maximum Y Value: 1188720 Grid Zone IIB Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 21°30'00"N Standard parallel 2: 30°00'00"N Minimum X Value: 1823188 Maximum X Value: 2000644 Minimum Y Value: 1306643 Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.

    2020-06-09 7483 28 View Details

  • Glacial lake inventory of the Pumqu Basin in the Himalayan Region of China (2004)

    Glacial lake inventory of the Pumqu Basin in the Himalayan Region of China (2004)

    This glacial lake inventory receives joint support from the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resources Centre for Asia and the Pacific (UNEP/RRC-AP), Cold and Arid Region Environmental and Engineering Research Institute (CAREERI). 9. This glacial lake cataloging uses Landsat (TM and ETM), Aster and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in the Himalayas in 2004. 10. Glacial lake catalogue coverage: the Himalayan region, Pumqu (Arun), Rongxer (Tama Koshi), Poiqu (Bhote-Sun Koshi), Jilongcangbu (Trishuli), Zangbuqin (Budhigandaki), Majiacangbu (Humla Karnali) and others. 11. Glacial Lake cataloging includes glacial lake cataloging, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 12. Data projection information: Projection: Transverse_Mercator False_Easting: 500000.000000 False_Northing: 0.000000 Central_Meridian: 87.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_WGS_1984 Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_WGS_1984 Spheroid: WGS_1984 Semimajor Axis: 6378137.000000000000000000 Semiminor Axis: 6356752.314245179300000000 Inverse Flattening: 298.257223563000030000 For a detailed data description, please refer to the data file and report.

    2020-06-09 7144 29 View Details

  • Inventory of glacial lakes in Bhutan (2000)

    Inventory of glacial lakes in Bhutan (2000)

    This glacial lake inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resource Centre, Asia and The Pacific (UNEP/RRC-AP). 1. The glacial lake inventory incorporates topographic map data and reflects the status of glacial lakes in the region in 2000. 2. The spatial coverage of the glacial lake inventory is as follows: Pa Chu Sub-basin, Mo Chu Sub-basin, Thim Chu Sub-basin, Pho Chu Sub-basin, Mangde Chu Sub-basin, Chamkhar Chu Sub-basin, Kuri Chu Sub-basin, Dangme Chu Sub-basin, Northern Basin, etc. 3. The glacial lake inventory includes the following data fields: glacial lake code, glacial lake types, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length, etc. 4. Data projection: Projection: Polyconic Ellipsoid: Everest (India 1956) Datum: Indian (India, Nepal) False easting: 2,743,196.4 False northing: 914,398.80 Central meridian: 90°0'00'' E Central parallel: 26°0'00'' N Scale factor: 0.998786 For a detailed description of the data, please refer to the data file and report.

    2020-06-09 7853 6 View Details

  • Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.

    2020-06-09 12674 54 View Details