Long-term series of daily global snow depth (1979-2017)

The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1979~2017, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1

0 2020-08-03

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.

0 2020-06-09

NSIDC Antarctic sea ice dataset (1978-2017)

The data sets include four sets of data obtained from the Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors using passive microwave remote sensing inversion. SMMR was aboard the Nimbus-7 satellite, and its working period was from October 26, 1978 to July 8, 1987. Since July 1987, the data provided by the SSM/I and the SSMIS aboard the US Defense Meteorological Satellite Program (DMSP) satellite group have been used. The first three data sets contain sea ice concentration data, covering the Antarctic region with a spatial resolution of 25 km: (1) The data were obtained from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Version 1 by applying the NASA Team algorithm inversion. The temporal coverage is from November 1978 to February 2017, with a temporal resolution of one month. A bin file is stored every month. (2) The data source is the same as the first set. The temporal coverage is from 1978-10-26 to 2017-2-28. The temporal resolution is two days, and the spatial resolution is 25 km. A folder was stored every year, and a bin file was stored every other day. (3) The data were obtained from near-real-time DMSP SSMIS by applying the NASA Team algorithm inversion. The temporal coverage is from 2015-1-1 to 2018-2-3, and the temporal resolution is one day. A bin file is stored every day. Each file consists of a 300-byte file title (data time information, projection pattern, file name) and a 316*332 matrix. The fourth set of data is the sea ice coverage and sea ice area time series. The temporal coverage is from November 1978 to December 2017. This data set is a time series sequence of sea ice coverage and sea ice area in the Antarctic. The temporal resolution is one month, and an ASCII file is stored every month. Each file consists of a file title (time, data type), a 39*1 sea ice cover matrix and a 39*1 sea ice area matrix. For further details on the data, please visit the US Ice and Snow Data Center NSIDC website - Data Description http://nsidc.org/data/NSIDC-0051; http://nsidc.org/data/NSIDC-0081; http://nsidc.org/data/G02135

0 2020-06-03

Global land surface microwave emissivity dataset from AMSR-E (2002-2012)

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.

0 2020-06-03

Daily cloud-free snow products of MODIS in the northern hemisphere (2000-2016)

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

0 2020-06-03

Long-term serial data of snow area on the Tibetan Plateau (2007-2015)

The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.

0 2020-04-29

Snowmelt onset time of High Mountain Asia (1979-2018)

High Asia is very sensitive to climate change, and is a hot area of global change research. The changes of temperature and precipitation will be reflected in the freezing and thawing time of ice and snow. Satellite microwave remote sensing can provide continuous monitoring ability of ice and snow surface state in time and space. When a small part of ice and snow begins to melt, micro liquid water will also be reflected in active and passive microwave remote sensing signals. In the microwave band, the dielectric constant of ice and liquid water is very different, so it provides a basic theory for the microwave remote sensing monitoring of ice and snow melting. In the case of passive microwave, when ice and snow begin to melt and liquid water appears, its absorption and emissivity increase rapidly, so its emissivity, brightness temperature and backscatter coefficient will also change rapidly. This data set is the initial time of ice and snow melting in the high Asia region retrieved by using the satellite microwave radiometer and scatterometer observations from 1979 to 2018. The passive microwave remote sensing data are SMMR on satellite (1979-1987) and SSM / i-ssmis radiometer on DMSP (1988 present). The active microwave remote sensing data is the QuikSCAT satellite scatterometer (2000-2009).

0 2020-04-07

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