AMSR-E/Aqua Monthly Global Microwave Land Surface Emissivity

Table of Contents

  1. Detailed Data Description
  2. Data Access and Tools
  3. Data Acquisition and Processing
  4. References and Related Publications
  5. Contacts and Acknowledgments
  6. Document Information

Citing These Data

We kindly request that you cite the use of this data set in a publication using the following citation example. For more information, see our Use and Copyright Web page.

Data Citation

Norouzi, H., M. Temimi, W. B. Rossow, and R. Khanbilvardi. 2013. AMSR-E/Aqua Monthly Global Microwave Land Surface Emissivity. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center.

Literature Citation

Norouzi, H., M. Temimi, W. Rossow, C. Pearl, M. Azarderakhsh, and R. Khanbilvardi. 2011. The Sensitivity of Land Emissivity Estimates from AMSR-E at C- and X-Bands to Surface Properties. Journal of Hydrology, Earth Systems Science 15:3577-3589. http://dx.doi.org/10.5194/hess-15-3577-2011.

Overview

Platform

Aqua

Sensor

AMSR-E

Spatial Coverage

Global

Spatial Resolution

0.25°

Temporal Coverage

July 2002 – June 2008

Temporal Resolution

Monthly

Parameter

Land surface emissivity

Data Format

HDF4

Metadata Access

View Metadata Record

Get Data

FTP

1. Detailed Data Description

Format

Data are stored as 64-bit (8-byte) floating-point integers in Version 4 Hierarchical Data Format (HDF4) files. The files contain a data layer for each channel, such as the 6.9 V layer, which contains all the 6.9 GHz vertically-polarized measurements.

Data in compressed HDF files are arranged in a table format that image processing programs can easily visualize. This data compression should be transparent to most users since HDF-capable software tools automatically uncompress the data. Various software packages, such as HDFView, Panoply, or similar HDF-compatible applications, support the HDF data format. Visit the HDF–EOS Tools and Information Center Web page for more information about the HDF format, and for instructions on uncompressing and converting the data to binary format.

File and Directory Structure

Data files are organized on the FTP site at: ftp://sidads.colorado.edu/pub/DATASETS/nsidc0543_amsre_emiss/

File Naming Convention

The files are named according to the following convention, which is parsed and described in Table 1:

CREST-AMSR-E-Emissivity-Monthly-yyyymm-VX.hdf

Where:

Table 1. File Naming Convention Description
Variable Description
CREST Identifies this as a file containing data compiled at the National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center (NOAA-CREST)
AMSR-E Sensor
Emissivity Parameter
Monthly Temporal resolution
yyyy 4-digit year
mm 2-digit month
VX Version number (V1: Version 1)
.hdf HDF file extension

File Size

Each file is approximately 95 MB.

Volume

The volume of this data set is approximately 7.2 GB.

Spatial Coverage and Resolution

Data provide full global coverage at a quarter-degree latitude and longitude resolution.

Projection and Grid Description

The quarter-degree data are in one global cylindrical, equidistant latitude-longitude projection, and are gridded with 1440 columns and 720 rows.

Temporal Coverage and Resolution

The data span from July 2002 to June 2008 and are provided at a monthly resolution.

Parameter or Variable

Land surface emissivity estimates for this data set were collected at the following vertically and horizontally polarized (H- and V-pol) frequencies: 6.9, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. Valid land surface emissivity values range from 0.000 to 1.000. Missing values are filled with -999.

Sample Data Record

Figure 2 shows a sample data record.

Sample Data Record

Figure 2. This sample data record shows land surface emissivity data values in the vertically-polarized 10.7 GHz channel for December 2005.

2. Data Access

Get Data

Data are available via FTP.

3. Data Acquisition and Processing

Sensor or Instrument Description

For information on AMSR-E, please refer to the AMSR-E Instrument Description document.

Theory of Measurements

This product is a global land emissivity product using passive microwave observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). The developed product complements existing land emissivity products from the Special Sensor Microwave Imager (SSM/I) and from the Advanced Microwave Sounding Unit (AMSU) by adding land emissivity estimates at two lower frequencies, 6.9 and 10.65 GHz (in the C- and X-band, respectively). Observations at these low frequencies penetrate deeper into the soil layer.

Ancillary data used in the analysis, such as surface skin temperature and cloud mask, are obtained from International Satellite Cloud Climatology Project (ISCCP). Atmospheric properties are obtained from the TIROS Operational Vertical Sounder (TOVS) observations to determine the small upwelling and downwelling atmospheric emissions as well as the atmospheric transmission. This data set was extracted from instantaneous emissivity estimates.

Data Acquisition Methods

Emissivity Data

Land surface emissivity estimates for this data set were derived from the AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures, Version 2 data set.
AMSR-E is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz (Njoku and Li, 1999). Vertically and horizontally polarized measurements are made at all frequencies. The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 m in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 degrees. The spatial resolution of the individual measurements varies from 5.4 km at 89.0 GHz to 56 km at 6.9 GHz. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures (for both ascending and descending overpasses) were used for the analysis and were obtained from the National Snow and Ice Data Center (NSIDC). Higher frequency observations are resampled to match the lower frequencies spatial resolution. For each frequency, we select the resampled data having the closest location to the original satellite footprint and re-project these footprints to a 0.25 degree grid that is equidistant at the equator.

Ancillary Data

Satellite infrared-visible-based products from the International Satellite Cloud Climatology Project (ISCCP) provide cloud cover and surface skin temperatures. The ISCCP-DX data provides information every three hours since 1983 at approximately 30 km spatial resolution, based on merged observations from geostationary and polar-orbiting satellites (Rossow and Schiffer, 1999). The ISCCP quantities were chosen for the satellite view closest to nadir from among all available results and resampled to match the quarter-degree equidistant grid adopted for the passive microwave observations. The infrared-based skin temperatures represent the top surface temperature, which can be the top of very dense vegetation canopies or a mix of canopy and soil temperatures for less dense vegetation.

The TOVS data set available with ISCCP (Rossow and Schiffer, 1991) provides global information on air temperature and water vapor profiles at nine vertical layers ranging from the surface to 1 mb pressure. These profiles are available on a daily basis. We assume that the impact of diurnal variations on the observed brightness temperature is minimal. Data are originally available in a 280 km equal-area map, but are regridded to coincide with the AMSR-E data. These atmospheric parameters are used to calculate the upwelling and downwelling brightness temperatures, as well as the atmospheric transmission. TOVS data were selected in this study to be consistent with ISCCP products such as skin temperature, which is also based on TOVS data. See Zhang et al. (2006) for comparisons of the TOVS product with other atmospheric data sets.

Derivation Techniques and Algorithms

The following is adapted from Prigent et al, 1997 and Norouzi et al., 2011:

Assuming that land surface is flat and specular, and considering the atmosphere as a non-scattering plane-parallel medium, the emissivity can be written as:

Equation 1 (Equation 1)

 

 

where Equation 1a and Equation 1b are the land surface emissivity and the measured brightness temperatures at polarization p (horizontal, H, or vertical, V) and frequency Equation 1c, respectively. Ts is the skin temperature and Equation 1e and Equation 1d are the downwelling and upwelling brightness temperatures from the atmosphere, respectively:

Equation 2 (Equation 2)

 

 

Equation 3 (Equation 3)

 


In these equations, Equation 2a is the atmospheric temperature profile, Equation 2b the atmospheric absorption at altitude Equation 2c , Equation 2c-2 the cosine of incidence angle, and Equation 2d the atmospheric extinction between two altitudes, which is written as:

Equation 4 (Equation 4)

 


The implementation of this algorithm requires an accurate characterization of the atmospheric temperature and humidity to determine atmospheric transmissivity. Another key parameter is the thermal skin temperature.

AMSR-E overpass times are near 1:30 a.m. (ascending) and 1:30 p.m. (descending) local time at the equator. Since skin temperatures from ISCCP-DX data are available every three hours, microwave and thermal observations are not necessarily coincident. Therefore, a Spline interpolation between the eight available skin temperature measurements every day is used to infer the complete skin temperature diurnal cycle. The Spline method estimates the daily maxima and minima that can occur between two 3-hour samples (Aires et al., 2004). Actual acquisition time for each microwave pixel at each swath is used in the Spline interpolation to estimate more accurately the physical temperature. This may be critical in arid regions where the temperature diurnal cycle has much larger amplitude. Also, if either of two consecutive (before and after AMSR-E acquisition times) cloud flags indicates cloudy conditions, the microwave pixel is flagged as cloudy.

The upwelling and downwelling atmospheric emissions are estimated using the Liebe MPM model to determine the atmospheric absorption (Liebe et al., 1993). Upwelling and downwelling brightness temperatures, as well as atmospheric transmission, are calculated using Equations 2, 3, and 4 for the AMSR-E incidence angle of 55 degrees. Atmospheric corrections are applied to the ascending and descending overpasses. Because of the TOVS daily resolution, the same atmospheric profiles are used to correct atmospheric effects for both the ascending and descending overpasses.

Monthly composite emissivity maps are created for each frequency and polarization from the instantaneous cloud-free land surface emissivity maps. In the case of persistent cloud cover (longer than 30 days, which is possible in some tropical locations), land emissivity is not retrieved (resulting in a data value of -999).

Error Sources

The uncertainty in the atmospheric water vapor profile can be as much as 20-25 percent (English, 1995; Lin and Rossow, 1994; Zhang et al., 2006). A 25 percent change in water vapor leads to a global mean 0.0016 change of emissivity at 6.9 GHz and 0.03 at 89.0 GHz. TOVS data may include climatological values when actual measures are missing which can introduce an error in the atmospheric corrections (Prigent et al., 1998).

The physical skin temperature plays an important role at lower frequencies, since the microwave radiation is more sensitive to the surface than to the atmosphere. Recent studies show that available global skin temperatures have significant differences, generally only a few degrees but up to 20 K in deserts (Jimenez et al., 2011). ISCCP skin temperature has some uncertainties that tend to increase as temperature increases. The recent study shows that root mean square (rms) differences between ISCCP and MODIS skin temperature could be 5 K and 2.5 K for day and night, respectively (Moncet et al., 2011). The sensitivity analysis showed that the difference in global mean emissivity retrieval could be as much as 0.025 for skin temperature differences of 5 K. Although possible biases in skin temperatures from ISCCP can affect the absolute emissivity value, its effect on emissivity variability should not be significant during the AMSR-E operational life time because the ISCCP results are homogeneous in quality over this time period (Zhang et al., 2006). ISCCP-DX also was used for cloud detection. Possible discrepancies in cloud mask can affect the retrieval.

A 3 K decrease in observed brightness temperature leads to 0.01 decrease of emissivity at 36.5 GHz (H. polarization). The absolute accuracy of AMSR-E brightness temperatures has been reported as 1.0 K (Kawanishi et al., 2003).

Quality Assessment

Several quality controls have been conducted and the standard deviation of instantaneous emissivity estimates within each month to be less than 0.015. The results have been also evaluated with other available global data such as SSM/I.

4. References and Related Publications

Aires, F., C. Prigent, and W. B. Rossow. 2004. Temporal Interpolation of Global Surface Skin Temperature Diurnal Cycle over Land under Clear and Cloudy Conditions. Journal of Geophysical Research-Atmospheres. 109. doi:10.1029/2003jd003527.

English, S. J. 1995. Airborne Radiometric Observations of Cloud Liquid-Water Emission at 89 and 157 GHz – Application to Retrieval of Liquid-Water Path. Quarterly Journal of the Royal Meteorological Society. 121:1501-1524.

Jimenez, C., C. Prigent, B. Mueller, S. I. Seneviratne, M. F. McCabe, E. F. Wood., W. B. Rossow, G. Balsamo, A. K. Betts, P. A. Dirmeyer, J. B. Fisher, M. Jung, M. Kanamitsu, R. H. Reichle, M. Reichstein, M. Rodell, J. Sheffield, K. Tu, and K. Wang. 2011. Global Intercomparison of 12 Land Surface Heat Flux Estimates. Journal of Geophysical Research-Atmospheres. 116. doi:10.1029/2010jd014545.

Kawanishi, T., T. Sezai, Y. Ito, K. Imaoka, T. Takeshima, Y. Ishido, A. Shibata, M. Miura, H. Inahata, and R. W. Spencer. 2003. The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's Contribution to the EOS for Global Energy and Water Cycle Studies. IEEE Transactions on Geoscience and Remote Sensing, 41:184-194. doi:10.1109/tgrs.2002.808331.

Liebe, H. J., G. A. Hufford, and M. G. Cotton. 1993. Propagation Modelling of Moist Air and Suspended Water/Ice Particles at Frequencies below 1000 GHz. Specialist Meeting of the Electromagnetic Wave Propagation Panel Symposium, AGARD Conference Proceedings 542, Atmospheric Propagation Effects through Natural and Man–Made Obscurants for Visible through MW-Wave Radiation, March 1–10, 1993: Palma de Mallorca, Spain.

Lin, B., and W. B. Rossow. 1994. Observations of Cloud Liquid Water Path over Oceans – Optical and Microwave Remote-Sensing Methods. Journal of Geophysical Research-Atmospheres. 99:20907-20927.

Moncet, J., P. Liang, A. Lipton, J. Galantowicz, and C. Prigent. 2011. Discrepancies between MODIS and ISCCP Land Surface Temperature Products Analyzed with Microwave Measurements. J. Geophys. Res. doi:10.1029/2010JD015432.

Njoku, E. G., and L. Li. 1999. Retrieval of Land Surface Parameters using Passive Microwave Measurements at 6–18 GHz. IEEE Transactions on Geoscience and Remote Sensing. 37: 79-93.

Norouzi, H., M. Temimi, W. Rossow, C. Pearl, M. Azarderakhsh, and R. Khanbilvardi. 2011. The Sensitivity of Land Emissivity Estimates from AMSR-E at C- and X-Bands to Surface Properties. Journal of Hydrology, Earth Systems Science. 15:3577–3589. doi:10.5194/hess-15-3577-2011.

Norouzi, H., W. Rossow, M. Temimi, C. Prigent, M. Azarderakhsh, S. Boukabara, and R. Khanbilvardi. 2012. Using Microwave Brightness Temperature Diurnal Cycle To Improve Emissivity Retrievals Over Land. Remote Sensing of Environment. 123:470–482. doi:10.1016/j.rse.2012.04.015.

Prigent, C., W. B. Rossow, and E. Matthews. 1997. Microwave Land Surface Emissivities Estimated from SSM/I Observations. Journal of Geophysical Research-Atmospheres. 102:21867-21890.

Prigent, C., Rossow, W. B., and E. Matthews. 1998. Global Maps of Microwave Land Surface Emissivities: Potential for Land Surface Characterization. Radio Science. 33:745–751.

Rossow, W. B., and R. A. Schiffer. 1991. ISCCP Cloud Data Products. Bulletin of the American Meteorological Society. 72, 2–20.

Rossow, W. B., and R. A. Schiffer. 1999. Advances in Understanding Clouds from ISCCP. Bulletin of the American Meteorological Society. 80:2261–2287.

Zhang, Y. C., Rossow, W. B., and Stackhouse, P. W. 2006. Comparison of Different Global Information Sources used in Surface Radiative Flux Calculation: Radiative Properties of the Near-Surface Atmosphere. Journal of Geophysical Research-Atmospheres. 111. doi:10.1029/2005jd006873.

5. Contacts and Acknowledgments

Investigators

Hamidreza Norouzi
New York City College of Technology, The City University of New York (CUNY)
NOAA Cooperative Remote Sensing Science and Technology Center (NOAA-CREST)
300 Jay Street, Vorhees 424
Brooklyn, New York USA 11201

Marouane Temimi, William B. Rossow, Reza Khanbilvardi
The City College of New York (CCNY), CUNY
NOAA Cooperative Remote Sensing Science and Technology Center (NOAA-CREST)
160 Convent Ave, Steinman Hall (T-107)
New York, New York USA 10031

Technical Contact

NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
e-mail: nsidc@nsidc.org

Acknowledgements

This study was partially supported by National Oceanic and Atmospheric Administration (NOAA) under grant NA06OAR4810162, and NASA Energy and Water Study (NEWS) under grant NNXD7AO90G.

6. Document Information

Acronyms and Abbreviations

The acronyms used in this document are listed in Table 2.

Table 2. Acronyms and Abbreviations
Acronym Description
AMSR-E Advanced Microwave Scanning Radiometer - Earth Observing System
AMSU Advanced Microwave Sounding Unit
CCNY City College of New York
CUNY City University of New York
CREST Cooperative Remote Sensing Science and Technology Center
EOS Earth Observing System
FTP File Transfer Protocol
GB Gigabytes
HDF-EOS Hierarchical Data Format - EOS
ISCCP International Satellite Cloud Climatology Project
MODIS Moderate Resolution Imaging Spectroradiometer
MPM Multiplatform-Merged
NASA National Aeronautics and Space Administration
NEWS NASA Energy and Water Study
NOAA National Oceanic and Atmospheric Administration
NSIDC National Snow and Ice Data Center
RFI Radio Frequency Interference
RMS Root Mean Square
SSM/I Special Sensor Microwave Imager
TIROS Television Infrared Observation Satellite Program
TOVS TIROS Operational Vertical Sounder

Document Creation Date

August 2013

Document URL

http://nsidc.org/data/docs/daac/nsidc0543_amsre_emiss/index.html