LANDSAT MSS remote sensing dataset in Western China

The dataset covers western China. MSS remote sensing images Dataset properties: Pixel Size: 60m Reflective bands 4-7 (Landsat 1-3) and Bands 1-4 (Landsat 4-5) Output Format: GeoTIFF Resampling method: cubic convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) Data sources were partially downloaded from http://eros.usgs.gov/ and some were collected from various projects. The data folder is named the row and column number where the image is located. The folder contains the MSS 4 band images (* .tif), header files (* .met, * .hdr), and thumbnails (jpg). The naming format of image files is row and column number_TM image mark (2m), and image acquisition time_band number. It is mainly used for thematic analysis and compilation of different scale thematic maps on agriculture, forestry, water, soil, geology, geography, geography, surveying and mapping, regional planning, and environmental monitoring.

0 2020-03-30

Daily cloudless MODIS snow albedo dataset of Babaohe River basin (2008-2014)

The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.

0 2020-03-29

Dataset of passive microwave SSM / I and SSMIS brightness temperature in China (1987-2015)

This dataset mainly includes the twice a day (ascending-descending orbit) brightness temperature (K) of the space-borne microwave radiometers SSM / I and SSMIS carried by the US Defense Meteorological Satellite Program satellites (DMSP-F08, DMSP-F11, DMSP-F13, and DMSP-F17), time coverage from September 15, 1987 to December 31, 2015. The SSM/I brightness temperature of DMSP-F08, DMSP-F11 and DMSP-F13 include 7 channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 85.50H and 85.50V; The SSMIS brightness temperature observation of DMSP-F17 consists of seven channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 91.66H and 91.66v. Among them, DMSP-F08 satellite brightness temperature coverage time is from September 15, 1987 to December 31, 1991; DMSP-F11 satellite brightness temperature coverage time is from January 1, 1992 to December 31, 1995; The coverage time of DMSP-F13 satellite brightness temperature is from January 1, 1996 to April 29, 2009; The coverage time of DMSP-F17 satellite brightness temperature is from January 1, 2009 to December 31, 2015. 1. File format and naming: The brightness temperature is stored separately in units of years, and each directory is composed of remote sensing data files of each frequency, and the SSMIS data also contains the .TIM time information file. The data file names and their naming rules are as follows: EASE-Fnn-ML / HyyyydddA / D.subset.ccH / V (remote sensing data) EASE-Fnn-ML / HyyyydddA / D.subset.TIM (time information file) Among them: EASE stands for EASE-Grid projection method; Fnn stands for satellite number (F08, F11, F13, F17); ML / H stands for multi-channel low-resolution and multi-channel high-resolution respectively; yyyy represents the year; ddd represents Julian Day of the year (1-365 / 366); A / D stands for ascending (A) and descending (D) respectively; subset represents brightness temperature data in China; cc represents frequency (19.35GHz, 22.24 GHz, 37.05GHz, (85.50GHz, 91.66GHz); H / V stands for horizontal polarization (H) and vertical polarization (V), respectively. 2. Coordinate system and projection: The projection method of this data set is EASE-Grid, which is an equal area secant cylindrical projection, and the double standard parallels are 30 ° north and south. For more information about EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection to Geographic projection, please refer to the ease2geo.prj file, the content is 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 integer binary, each data occupies 2 bytes. The actual data stored in this dataset is the brightness temperature * 10. After reading the data, you need to divide by 10 to get the real brightness temperature. 4. Data resolution: Spatial resolution: 25.067525km, 12.5km (SSM / I 85GHz, SSMIS 91GHz) Time resolution: daily, from 1978 to 2015. 5. Spatial range: Longitude: 60.1 ° -140.0 ° east longitude; Latitude: 14.9 ° -55.0 ° north latitude. 6. Data reading: Remote sensing image data files in each set of data can be opened in ArcMap, ENVI and ERDAS software.

0 2020-03-28

Dataset of passive microwave SMMR brightness temperature in China (1978-1987)

This dataset mainly includes the passive microwave brightness temperature obtained from the Scanning Multichannel Microwave Radiometer (SMMR) carried by the Nimbus-7 satellite, including 06H, 06V, 10H, 10V, 18H, 18V, 21H, 21V, 37H, 37V, a total of ten microwave channels with two transits (ascending & descending) brightness temperature per day from October 25, 1978 to August 20, 1987, where H represents horizontal polarization and V represents vertical polarization. Nimbus-7, launched in October 1978, is a solar-synchronous polar-orbiting satellite. The microwave sensor SMMR is a dual-polarization microwave radiometer that measures the brightness temperature of five frequencies (6.6GHz, 10.69GHz, 18.0GHz, 21.0GHz, 37.0GHz) on the surface. It scans the surface at a fixed incident angle of about 50.3 °, with a width of 780 km, and passes through the equator at noon 12:00 (ascending orbit) and 24:00 (descending orbit). The time resolution of SMMR is daily, but due to the wide distance between swaths, the same surface will be revisited every 5-6 days. 1. File format and naming: Each set of data is composed of remote sensing data files. The name and naming rules of each group of data files in the SMMR_Grid_China directory are as follows: SMMR-MLyyyydddA / D.subset.ccH / V (remote sensing data) Among them: SMMR stands for SMMR sensor; ML stands for multi-channel low resolution; yyyy stands for year; ddd stands for Julian Day of the year (1-365 / 366); A / D stands for ascending (A) and derailing (D ); subset represents the brightness temperature data in China; cc represents the frequency (6.6GHz, 10.69GHz, 18.0GHz, 21.0GHz, 37.0GHz); 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 parallels are 30 degrees north and south. For more information about EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection to Geographic projection, please refer to the ease2geo.prj file, the content is 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 integer binary, each data occupies 2 bytes. The actual data stored in this dataset is the brightness temperature * 10. After reading the data, you need to divide by 10 to get the real brightness temperature. Spatial resolution: 25km; Time resolution: daily, from 1978 to 1987. 4. Spatial range: Longitude: 60.1 ° -140.0 ° East longitude; Latitude: 14.9 ° -55.0 ° north latitude. 5. Data reading Remote sensing image data files for each set of data can be opened in ENVI and ERDAS software.

0 2020-03-28

Global land surface microwave emissivity dataset from AMSR-E (2002-2011)

Microwave emissivity of the surface characterization of the object to launch the ability of microwave radiation, spaceborne passive microwave emissivity can on macro, large scale integral expression of epicontinental microwave radiation is a passive microwave surface parameters in quantitative inversion experience for one of the important basic data, is also on the large scale understand epicontinental microwave radiation in a way.This data set is considered to carry on the Aqua satellite advanced microwave scanning radiometer (amsr-e) and moderate resolution imaging spectroradiometer (MODIS) synchronous observation characteristics, using the MODIS land surface temperature and atmospheric water vapor data as input, by considering the effects of atmospheric emissivity estimation model, produced a global sky conditions during the running of amsr-e sensor (June 2002 ~ October 2011) of the epicontinental multichannel bipolar microwave instantaneous emission rate.Through product low-frequency radio signal, data alignment, statistic analysis, the different emissivity characteristics of surface coverage condition, frequency dependence and correlation studies conducted confirmatory analysis, the results show that the instantaneous dynamic details of emissivity is rich, standard deviation within 0.02 month daily variation, the change of time and space, frequency dependent on and related to the understanding of the natural physical process. This data set includes amsr-e global land surface daily, daily, daily, monthly and monthly products in the whole life cycle, which can be used to carry out satellite based passive microwave remote sensing simulation, land surface model, and inversion research of land surface temperature, snow cover, atmospheric precipitation/moisture/precipitation.The projection coordinates of the data adopt the standard EASE-GRID projection, and the data storage method is binary floating point lattice (the size of the matrix is 1383*586). After the data is obtained, ENVI/IDL and other software or the corresponding program code can be read in the form of binary files. All land surface emissivity data produced are named according to the following rules: RADI_AMSRE_EM # # # # _yyymmdd_EG_V. Bin For example, file name: RADI_AMSRE_EM01_20060101_EG_V# EM##: 01 means daily, 05 means 5 days, 10 means ten days, HM means half a month, MO means a month Yyyymmdd: yyyy means year, mm means month, and dd means date V##: version number, such as 0.1, 1.0, etc., the units digit is the official version RADI: institute of remote sensing and digital earth, Chinese academy of sciences AMSRE: advanced microwave scanning radiometer

0 2020-03-28

Landsat TM remote sensing dataset over Western China (1980‘s)

This dataset is TM remote sensing data covers western China, around the 1980s. Data attributes: Pixel Size: 30-meter reflective: Bands 1-5 and 7                 60-meter thermal: Band 6                 Output Format: GeoTIFF                 Resampling method: cubic convolution (CC)                 Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica.                 Image Orientation: Map (North Up) The data was partially downloaded from the USGS http://eros.usgs.gov/ website, and partly collected from various projects. The data folder is named the row and column number where the image is located. The folder contains TM 7 bands images (* .tif), header files (* .met, * .hdr) and thumbnails (jpg). The naming format of image files is row and column number _TM image mark (5t), and image acquisition time _ band number.

0 2020-03-27

Long-term series of daily snow depth dataset in China (1979-2019)

This data set is an upgraded version of the “Long-term series of daily snow depth dataset in China". This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2019, with a spatial resolution of 0.25 degrees. The original data used to derive the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2019) which were archived in the National Snow and Ice Data Center (NSIDC). Because the brightness temperatures come from different sensors, there is a certain system inconsistency among them. Therefore, before the derivation of snow depth, the inter-sensor calibration were performed to improve the temporal consistency of the brightness temperature data. Based on the calibrated brightness temperatures, the modified Chang algorithm developed by Dr. Tao Che, was used to retrieve daily snow depth. The algorithm details were introduced in the data specification document- “Long-term Sequence Data Set of China Snow Depth (1979-2019) Introduction. doc". The projection of the data set is latitude and longitude. The data of each day was stored in a file, and the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data specification document.

0 2020-03-19

Water temperature observation data at Nam Co Lake in Tibet (2011-2014)

This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.

0 2020-03-16

The QuickBird images of the Heihe River Basin (2004)

QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.

0 2020-03-15

Remote sensing products of thermal collapse in Heihe permafrost region of the Tibetan Plateau (2009-2018)

Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.

0 2020-03-14