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As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.
Long, D. G. and J. Stroeve. 2011. Enhanced-Resolution SSM/I and AMSR-E Daily Polar Brightness Temperatures. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center.
Northern and Southern Hemispheres, and full global
SSM/I: 1995 – 2008
Flat binary, GIF
This data set contains enhanced-resolution brightness temperatures produced using the Scatterometer Image Reconstruction (SIR) algorithm developed by the Microwave Earth Remote Sensing (MERS) group at Brigham Young University (BYU). Enhanced-resolution brightness temperature images for the Arctic and Antarctic were generated using antenna temperature data from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument. SSM/I images are available for 1995 through 2008, and AMSR-E images are available for 2002 through 2011. All available measurements for a single day are averaged into twice-daily gridded files for three different equal-area spatial coverages: Northern Hemisphere, Southern Hemisphere, and full global. The spatial resolution is 12.5 km for SSM/I channels 19 and 22 GHz, 7.5 km for 37 GHz, and 2.5 km for the 85 GHz channel. For AMSR-E, the resolution is 12.5 km for the 6, 10, 18, and 23 GHz channels; 7.5 km for the 36 GHz channel; and 2.5 km for the 89 GHz channel. Software for reading the SIR-formatted SSM/I and AMSR-E data is also available. As scatterometer and radiometer data complement each other, these enhanced-resolution radiometer data, together with SIR format scatterometer data available from BYU, will facilitate polar research that combines information from both radiometers and scatterometers. Data are stored as scaled 2-byte integers in binary arrays with a 512-byte header and are available via FTP.
Data are provided in zipped binary files, and are stored as scaled 2-byte integers representing brightness temperature values ranging from 100 K to 427.67 K. A factor of 200 is applied to the brightness temperature value minus 100 prior to converting the value to an integer. Conversion from integer value K to brightness temperature is given in the formula Tb=100+K/200. For example, a stored integer value of 2358 represents a brightness temperature value of 111.79 K. A value of 0 represents missing data. Note that these scale factors and the projection parameters are contained in the file header so they can be used by the available file readers.
Data on the FTP site are divided into two main directories, amsre and ssmi. Within these, yearly data are contained in subdirectories labeled by year. Browse imagery are contained in the browse subdirectory and are further organized into subdirectories labeled by year. The yearly subdirectories for both the data and browse imagery are then divided into three subdirectories: global, north, and south.
The files are named according to the following convention and as described in Table 1:
|IIII(I)||Instrument (SSMI, AMSRE)|
|(ppp)||SSM/I platform (F13)|
|hirz||Indicates data are high-resolution|
|doy||3-digit day of year|
|t||Time of day (m: morning, a: afternoon, e: evening, n: night)|
|X||EASE-Grid projection region (N: North, S: South, M: Global)|
|CC||Channel frequency (06, 10, 18, 19, 22, 23, 36, 37, 85, 89)|
|c||Channel polarization (v: vertical, h: horizontal)|
|vVV||Data version number (for example, v01)|
|.sir||Indicates file format is Scatterometer Image Reconstruction (SIR)|
|.gz||Indicates file has been compressed using Gzip|
|.gif||Indicates this is a GIF image file|
All brightness temperature data files are compressed using Gzip, as indicated by the gz file extension. Note that browse image files are not zipped, and range in size from approximately 5 KB to 7 MB.
Whether zipped or unzipped, SSM/I and AMSR-E data file sizes vary depending on the region and/or frequency, as shown in Tables 2 and 3, respectively. File sizes are approximate.
|Region||SSM/I Channels (GHz)||Zipped Size (MB)||Unzipped Size (MB)|
|Region||AMSR-E Channels (GHz)||Zipped Size (MB)||Unzipped Size (MB)|
These data files are provided in three different equal-area, spatial coverages: Northern Hemisphere azimuthal, Southern Hemisphere azimuthal, and global cylindrical. Please see the Grid Extent Table on the EASE-Grid: A Versatile Set of Equal-Area Projections and Grids Web page for specific latitude and longitude values. Figure 1 shows a map of the three different coverages.
Figure 1. Left two images: coverage of Northern and Southern Hemispheres (based on Lambert's equal-area, azimuthal projection). Right image: global coverage (based on cylindrical, equal-area projection).
The low-frequency channel resolution of this data set is similar to European Space Agency (ESA) Earth Remote Sensing (ERS) scatterometer images, while the high-frequency channel resolution is similar to that of NASA Scatterometer (NSCAT) and Seawinds scatterometer images. The spatial resolution is 12.5 km for SSM/I channels 19.3 and 22.2 GHz (in 1441x1441 arrays), 7.5 km for 37.0 GHz (in 2881x2881 arrays), and 2.5 km for the 85.5 GHz channel (in 5761x5761 arrays). The resolution for AMSR-E 6.9, 10.7, 18.7, 23.8, and 36.5 GHz channels is 12.5 km; the 36.5 GHz is 7.5 km; and the 89.0 GHz channel is 2.5 km.
The SSM/I and AMSR-E EASE-Grids are a set of three equal-area projections: two azimuthal equal-area projections, one for the Northern and one for the Southern Hemisphere; and a global cylindrical equal-area projection. Please see the EASE-Grid: A Versatile Set of Equal-Area Projections and Grids for more information on the EASE-Grid.
Temporal coverage for the SSM/I data spans 1995 through 2008, and 2002 through 2011 for AMSR-E.
Twice-daily enhanced-resolution brightness temperature images were generated for the Arctic and Antarctic using SSM/I and AMSR-E data.
Theoretically, brightness temperature is the effective temperature of a blackbody radiating the same amount of energy per unit area at the same wavelengths as the observed body. Empirically, brightness temperature is the apparent radiant temperature of a non-blackbody determined by measurement with an optical pyrometer or radiometer. The brightness temperature (Tb) at a given wavelength (Λ) is the product of the physical temperature (Tp) and the emissivity (Σ) at the given wavelength of the surface viewed by the radiometer. Refer to Equation 1.
Tb(Λ) = Σ(Λ)Tp
Equation 1 is the Rayleigh-Jean approximation of Plank's law for the passive microwave region of the electromagnetic spectrum. It is an approximation and does not take into account effects of the atmosphere on the microwave radiation.
Table 4 compares the channels for each instrument at which the brightness temperatures for this data set are calculated. As indicated in the table, with the exception of 22.2 GHz, each frequencyis both vertically and horizontally polarized.
|19.3V, 19.3H||18.7V, 18.7H|
|37.0V, 37.0H||36.5V, 36.5H|
|85.5V, 85.5H||89.0V, 89.0H|
Data are stored as scaled 2-byte integers representing brightness temperature values, ranging from 100 K to 427.67 K. A factor of 200 is applied to the brightness temperature value minus 100 prior to converting the value to an integer. Conversion from integer value K to brightness temperature (Tb) is given in the formula Tb=100+K/200. For example, a stored integer value of 2358 represents a brightness temperature value of 111.79 K. A value of 0 represents missing data. Note that these scale factors and the projection parameters are contained in the file header so they can be used by the available file readers.
Brightness temperatures are measured in kelvins (K), and are precise to .01 K.
Figure 2 displays sample browse images acquired from the AMSR-E 85 GHz channel (left) and the 89 GHz SSM/I channel (right).
While a local time-of-day processing was used to minimize variations in the surface (since the surface does not change much over a few hours), the atmosphere can change fairly rapidly. Thus, a change in brightness temperature distribution when combining all passes to produce this high resolution data set can produce image artifacts. While all images can have artifacts, they are most evident at 85 and 89 GHz due to atmospheric effects. Though the 85 and 89 GHz channels were chosen for their atmospheric sensitivity, it can be argued that multi-pass resolution enhancement for these channels may not be applicable for some studies.
A number of errors have been introduced in the SSM/I time series. However, with the exception of geolocation errors, SSM/I F13 data are largely devoid of such errors. Refer to the Error Sources section of the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures product for complete information regarding all error sources.
Level-2A AMSR-E used in the derivation of this data set include unsmoothed Level 1B data derived from antenna temperatures. Refer to the AMSR-E Instrument Description Web page for a description of the error sources associated with radiometer calibration. More information is also provided in the Error Sources section of the AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures user guide.
Enhanced SSM/I brightness temperatures were produced without calibration changes from the Remote Sensing Systems (RSS) Version 6 (V6) SSM/I swath product for DMSP-F13. Enhanced AMSR-E brightness temperatures were produced, also without adjustments, from the RSS V4 AMSR-E swath product. For details regarding calibration and validation of these source products, please visit RSS.
Data are available via FTP.
Unzipped, the total volume of this data set is approximately 4.3 TB. When zipped, it is 1 TB.
The following IDL tools are available via FTP in the directory named idl.
These IDL programs were written by BYU-MERS and redistributed at NSIDC for user convenience. Please read the readme file for instructions on using them. Other IDL and MATLAB programs are available from BYU-MERS. The IDL code may be copied and modified as long as original or modified code is not redistributed for profit, and acknowledgement is made that the original code was obtained courtesy of David G. Long at the Microwave Earth Remote Sensing Laboratory at Brigham Young University.
loadsir.pro: Simple program to read and display SIR files. Use the following command at the IDL prompt to display a SIR file in a 512 by 512 window:
xsir.pro: A more robust program to read and display SIR files. This program calls a number of IDL subroutines contained in other files, including pixtolatlon.pro, ilambert1.pro, ipolster.pro, and ieasegrid.pro. These routines convert pixel location to lat/lon (pixtolatlon.pro) or lat/lon to pixel location (latlon2pix.pro) based on the info array containing the file header information.
Microwave radiometers such as SSM/I and AMSR-E have wide application in atmospheric remote sensing over the ocean and provide essential inputs to numerical weather-prediction models. SSM/I and AMSR-E data have also been used for land and ice studies, including snow-cover classification, measurements of soil and plant moisture content, atmospheric moisture over land, land surface temperature, and mapping polar ice. Unfortunately, the relatively low resolution of SSM/I and AMSR-E is a limiting factor in their application for other land and ice studies. The intrinsic horizontal resolution of both instruments is determined by the antenna-beam pattern and, depending on the channel, its resolution varies from approximately 70 to 15 km. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. Resolution enhancement is, in effect, equivalent to antenna-pattern deconvolution. Two resolution-enhancement algorithms developed for microwave data are the Backus Gilbert Inversion (BGI) method, which has been applied to SSM/I data for both resolution enhancement and optimal interpolation, and the scatterometer image reconstruction (SIR) algorithm. The SIR algorithm was originally developed for SEASAT-A scatterometer data, but since scatterometer and radiometer data compliment each other, it has been adapted for use with SSM/I and AMSR-E data. The SIR algorithm requires less computation than the BGI method. See Long and Daum (1998) for a comparison of these two algorithms in generating enhanced resolution brightness images from SSM/I and AMSR-E data over land areas.
Note that both the BGI and SIR resolution enhancement algorithms generate images from the radiometer measurements. While they effectively deconvolve the antenna pattern, the algorithms do not enhance previously produced images as do image-processing algorithms; rather, they are reconstruction algorithms, which attempt to reconstruct the underlying brightness distribution (Tb) on a high-resolution grid from lower resolution measurements. And while the grid can be made arbitrarily fine, the resolution enhancement capabilities of both the BGI and SIR algorithms are dependent on the sampling pattern and the overlap in the response functions of the measurements. In general, the higher the sampling density (leading to more overlap in the measurements), the better the resolution enhancement. Note that an overly fine grid increases the computational requirements without improving the effective resolution of the output image. Like all resolution-enhancement techniques, both algorithms provide improved resolution at the expense of an increased noise level in the images. (Long and Daum 1998)
The instruments used to acquire these data are the SSM/I instrument on the Defense Meteorological Satellite Program (DMSP) F-13 satellite and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Aqua satellite.
The SSM/I instrument is a seven-channel, four-frequency, orthogonally polarized, passive microwave radiometric system. The instrument measures combined atmosphere and surface radiances at 19.3 GHz, 22.2 GHz, 37.0 GHz and 85.5 GHz. Refer to the SSM/I Instrument Description for more details.
The AMSR-E is a twelve-channel, six-frequency, conically-scanning, passive microwave radiometric system. It measures combined atmosphere and surface radiances ranging from 6.9 GHz to 89.0 GHz. Refer to the AMSR-E Instrument Description for more details.
The input brightness temperatures used for this data set were acquired from the SSM/I instrument mounted on the DMSP-F13 satellite, and from the AMSR-E instrument on the NASA Aqua satellite.
Input brightness temperatures from the SSM/I and AMSR-E instruments were acquired from Remote Sensing Systems (RSS), Santa Rosa, California (Wentz 1993). The input data were then processed and enhanced by the BYU MERS Laboratory.
The following has been adapted from Long and Daum (1998), pp. 408-410:
The SIR algorithm, originally designed to produce multivariate scatterometer images, has been adapted for radiometer measurements. It produces radiometric images by using an iterative procedure from an initial brightness estimate. The procedure is nonlinear and depends on the antenna pattern dimension, shape, and measurement overlap to obtain resolution enhancement. The SIR algorithm is a variation of the multiplicative algebraic-reconstruction technique (MART), a maximum entropy reconstruction method. The detailed derivation for scatterometer data is found in Long et al. (1993) with further elaboration in Early and Long (2001), and the radiometer version of the algorithm is provided in its entirety in Long and Daum 1998.
Ignoring the effects of the atmosphere, an SSM/I measurement can be modeled as a product of the surface brightness and the antenna pattern. The ith measurement Ta(i), in K, is obtained by integrating the product of surface brightness response Tb(x,y), in K, and the antenna at the surface Gi(x,y):
where integrals are over the surface area corresponding to the non-negligible gain of the antenna. The dependence of G on i arises from the boresight pointing of the antenna, which changes as the antenna scans the surface. Note that the antenna pattern acts as a non-ideal, low-pass filter of the surface brightness.
SIR provides a maximum-entropy estimate of the brightness temperature for each element of a rectilinear grid of pixels. Assuming that the brightness temperature is constant within each pixel, Equation 2 can be written as:
and Li, Ri, Bi, and Ti define a bounding rectangle for the ith measurement. In implementing the SIR algorithm, an initial guess for the Tb image is first made, typically the average expected brightness temperature. A predicted value, or forward projection, of each measurement is calculated from a current estimate of Tb and is compared to the measurement. A scale factor is then computed as the ratio of the measurement to the forward projection. An update term is computed for each pixel in the measurement cell by multiplying by the scale factor. Between iterations, each pixel in the image is updated by averaging the update terms for the pixel. As the process iterates, the scale factors approach unity and all of the forward projections match the measurements. When noise is present in the measurements, however, the scale factors may not always converge to unity. In such a case, the algorithm attempts to balance the various scale factors for each measurement and pixel using maximum entropy. In SIR, the scale factors calculated for each forward projection are damped by taking the square root. In addition, the update terms are computed in a manner that limits the amount of change for a single update (refer to Equation 9). These steps tend to reduce the sensitivity of the update terms to noise.
Let Tb(x,y) be denoted by Pj and Gi(x,y) by Hij where j is the row-scanned pixel number and i is the measurement number. Then for the kth iteration, and for the ith measurement in the data set zi and its corresponding weighting function, hij, the forward projection,
is calculated as:
(Equations 6 and 7)
where M is the number of pixels in the image. The scale factor,
is then computed as:
The non-linear update term,
is then computed according to:
After the entire data set has been processed, each pixel estimate,
is updated by computing a weighted average of the update terms, as shown in Equation 10, where Nm is the number of measurements:
This set of equations is iterated over k for N iterations until the scale factors approach unity. Because of the damping employed in the update terms, the algorithm always converges and the iteration may be continued even for very large k values. As shown in the next section, however, the accuracy of the algorithm at first increases with the iteration number, but then may decrease as the iteration continues. This is a common problem with algebraic reconstruction algorithms and is the result of excessive noise amplification. Fortunately, the algorithm's performance is not particularly sensitive to the exact number of iterations used and a range of values will produce good results. This range depends on the antenna pattern size and sampling density. In Long and Daum (1998), simulation was used to determine the optimum number of iterations to use for each channel. With SSM/I data, for example, convergence is typically obtained within 15 iterations.
In producing the enhanced resolution SSM/I and AMSR-E images, multiple passes are combined, using all the passes within a narrow time window at the same local time of day at each pixel. A detailed description of the local time-of-day image creation is contained in Gunn (2007). The averaging is split into morning and evening passes to remove artifacts introduced by diurnal brightness temperature variations during the day in summer. At low latitudes, there are essentially only two times of day approximately 12 hours apart for which brightness temperature measurements can be obtained. At the poles, there is a spread, up to a few hours, in the local time-of-day of the observations. For SSM/I, by splitting the data by local time-of-day at 0800 and 2000 hours, the two data sets have narrow local time-of-day distributions, and thus similar brightness temperature values. The only complication to this scheme is that, as the local time-of-day is a function of longitude, there must be a longitude at which the day changes. This is normally at 180 degrees East.
In the North polar region, afternoon corresponds to the period 0500 through 1700 and night corresponds to 1700 through 2900, which is actually the next calendar day. Hence, the data are not all from the same calendar day. For the South pole, these times are altered to 0800 through 2000 and 2000 through 3200. These times are based on natural splits in the data. The images are originally produced in polar stereographic at the poles and Lambert Equal Area elsewhere. The images are then remapped to produce EASE-Grid north, south, and global views. Note: This method means that the EASE-Grid images do not show some portions of swaths over the ocean that were not included in the original land images.
Table 5 outlines the processing and algorithm history for this product.
|V01||Jul 2011||Original version of data.|
Long, D. G., D. Early, and M. R. Drinkwater. 1994. Enhanced Resolution ERS-1 Scatterometer Imaging of Southern Hemisphere Polar Ice. Proc. Int. Geosci. Rem. Sens. Sym., Pasadena, California, 8-12 August, 156-158.
Gunn, B. 2007. Temporal Resolution Enhancement for AMSR Images. MERS Technical Report 07-02, 04 Dec. 2007. http://www.scp.byu.edu/docs/pdf/MERS0702.pdf
Stroeve, J. 1998. Impact of Various Processing Options on SSM/I-Derived Brightness Temperatures. NSIDC Special Report-7. http://nsidc.org/pubs/special/7/index.html
Microwave Earth Remote Sensing (MERS) Laboratory
Brigham Young University (BYU)
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National Snow and Ice Data Center (NSIDC)
Cooperative Institute for Research in Environmental Sciences (CIRES)
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Boulder, Colorado 80309-0449 USA
NSIDC User Services
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This research was supported by National Aeronautics and Space Administration (NASA) grant #NNG04GG72A.
Table 6 lists acronyms used in this document.
|AMSR-E||Advanced Microwave Scanning Radiometer - Earth Observing System|
|BGI||Backus Gilbert Inversion|
|BYU||Brigham Young University|
|CIRES||Cooperative Institute for Research in Environmental Sciences|
|DAAC||Distributed Active Archive Center|
|DMSP||Defense Meteorological Satellite Program|
|EASE-Grid||Equal Area Scalable Earth-Grid|
|ERS||Earth Remote Sensing|
|ESA||European Space Agency|
|FTP||File Transfer Protocol|
|Gzip||GNU zipping/data compression program|
|GIF||Graphic Interchange Format|
|IDL||Interactive Data Language|
|MART||Multiplicative Algebraic-Reconstruction Technique|
|MERS||Microwave Earth Remote Sensing|
|NASA||National Aeronautics and Space Administration|
|NSIDC||National Snow and Ice Data Center|
|RSS||Remote Sensing Systems|
|SIR||Scatterometer Image Reconstruction|
|SSM/I||Special Sensor Microwave Imager|
|SSMIS||Special Sensor Microwave Imager/Sounder|
|URL||Uniform Resource Locator|
|UTC||Coordinated Universal Time|