AMSR-E/Aqua Monthly L3 Global Snow Water Equivalent EASE-Grids
Entry ID:
AE_MoSno
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Summary
Abstract:
The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (EOS) Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids). Data are stored in Hierarchical Data Format - Earth Observing System (HDF-EOS) format, and are available from 19 June 2002 to the present via FTP.
Related URL
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Description:
AMSR-E Web site at NSIDC
Description:
NASA AMSR-E Web site
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Geographic Coverage
(Click for Interactive Map)
Spatial coordinates
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N: 90.0
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S: -90.0
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E: 180.0
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W: -180.0
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Data Set Citation
Dataset Originator/Creator:
A. Chang
Dataset Title:
AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids
Dataset Release Date:
2004-01-01
Dataset Release Place:
Boulder, CO U.S.A.
Dataset Publisher:
National Snow and Ice Data Center (NSIDC)
Version:
2
Online Resource:
http://nsidc.org/data/ae_mosno.html
Data Resolution
Latitude Resolution:
25 km
Longitude Resolution:
25 km
Temporal Resolution:
daily
Quality
Quality Assessment
Each HDF-EOS file contains core metadata with Quality Assessment (QA) metadata flags that are set by the Science Investigator-led Processing System (SIPS) at the Global Hydrology and Climate Center (GHCC) prior to delivery to NSIDC. A separate metadata ... file in XML format is also delivered to NSIDC with the HDF-EOS file; it contains the same information as the core metadata. Three levels of QA are conducted with the AMSR-E Level-2 and Level-3 products: automatic, operational, and science QA. If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a granule fails QA, SIPS does not send the granule to NSIDC until it is reprocessed. Level-3 products that fail QA are never delivered to NSIDC (Conway 2002).
Automatic QA
Chang visually examined random samples of SWE products to ensure they were consistent with an understanding of climate and that no gross errors were present. Future validation will involve comparing retrieved SWE values with estimates from airborne gamma observations over the U.S. (Carroll 1997) and with snow gauge data (Carroll et al. 1995), as well as comparing snow extent with MODIS snow maps (Chang and Rango 2000).
Operational QA
AMSR-E Level-2A data arriving at GHCC are subject to operational QA prior to processing higher-level products. Operational QA varies by product, but it typically checks the following criteria for a given file (Conway 2002):
* File is correctly named and sized
* File contains all expected elements
* File is in the expected format
* Required EOS fields of time, latitude, and longitude are present and populated
* Structural metadata is correct and complete
* The file is not a duplicate
* The HDF-EOS version number is provided in the global attributes
* The correct number of input files were available and processed
Science QA
AMSR-E Level-2A data arriving at GHCC are also subject to science QA prior to processing higher-level products. If less than 50 percent of a granule's data is good, the science QA flag is marked 'suspect' when the granule is delivered to NSIDC. In the SIPS environment, the science QA includes checking the maximum and minimum variable values, and percent of missing data and out-of-bounds data per variable value. At the Science Computing Facility (SCF), also at GHCC, science QA involves reviewing the operational QA files, generating browse images, and performing the following additional automated QA procedures (Conway 2002):
* Historical data comparisons
* Detection of errors in geolocation
* Verification of calibration data
* Trends in calibration data
* Detection of large scatter among data points that should be consistent Geolocation errors are corrected during Level-2A processing to prevent processing anomalies such as extended execution times and large percentages of out-of-bounds data in the products derived from Level-2A data.
The Team Lead SIPS (TLSIPS) developed tools for use at SIPS and SCF for inspecting the data granules. These tools generate a QA browse image in Portable Network Graphics (PNG) format and a QA summary report in text format for each data granule. Each browse file shows Level-2A and Level-2B data. These are forwarded from Remote Sensing Systems (RSS) to GHCC along with associated granule information, where they are converted to HDF raster images prior to delivery to NSIDC. SWE is estimated for SD retrievals greater than 1 mm. Based on the 2002-2003 winter AMSR-E data and 38 coincident ground observations in the World Meteorological Organization (WMO) Global Telecommunications System (GTS) network, the standard error is 24.2 cm. Further validation is planned using multiple local, regional, and global data sets. See NSIDC's AMSR-E Validation Data for information about data used to check the accuracy and precision of AMSR-E observations.
5. References and Related Publications
Basist, A., N.C. Grody, T. C. Peterson, and C. N. Williams. 1998. Using the Special Sensor Microwave Imager to Monitor Land Surface Temperatures, Wetness and Snow Cover. Journal of Applied Meteorology 37(9): 888-911.
Brodzik, M. J. 1997. EASE-Grid: A Versatile Set of Equal-Area Projections and Grids. Boulder, CO, USA: National Snow and Ice Data Center.
Brown, R. D. and R. O. Braaten, 1998. Spatial and Temporal Variability of Canadian Monthly Snow Depths, 1946-1995. Atmosphere-Ocean 36: 37-45.
Chang, A. T. C., and A. Rango. 2000. Algorithm Theoretical Basis Document for the AMSR-E Snow Water Equivalent Algorithm, Version 3.1. Greenbelt, MD, USA: NASA Goddard Space Flight Center. (view PDF file)
Chang, A. T. C., J. L. Foster, Dorothy K. Hall, B. E. Goodison, A. E. Walker, and J. R. Metcalfe. 1997. Snow Parameters Derived from Microwave Measurements During the BOREAS Winter Field Experiment. Journal of Geophysical Research 102: 29663-29671.
Chang, A. T. C., J. L. Foster, and Dorothy K. Hall. 1987. Nimbus-7 Derived Global Snow Cover Parameters. Annals of Glaciology 9: 39-44.
Chang, A. T. C., J. L. Foster, Dorothy Hall, A. Rango, and B. Hartline. 1982. Snow Water Equivalence Determination by Microwave Radiometry. Cold Regions Science and Technology 5: 259-267.
Chang, A. T. C., R. E. J. Kelly, J. L. Foster, and Dorothy K. Hall. The Testing of AMSR-E Snow Depth and Snow Water Equivalent Estimates in the Northern Hemisphere. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003a.
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Global SWE Monitoring Using AMSR-E Data. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Estimation of Snow Depth from AMSR-E in the GAME-Siberia Experiment Region. Poster presented at the Proceedings of IGARSS, Alaska, USA 2004.
Carroll, T. R. 1997. Integrated Ground-based, Airborne, and Satellite Snow Cover Observations in the National Weather Service. 77th AMS Annual Meeting; Symposium on Integrated Observing Systems, Long Beach, CA.
Carroll, S. S., G. N. Day, N. Cressie, and T. R. Carroll. 1995. Spatial Modeling of Snow Water Equivalent Using Airborne and Ground Based Snow Data. Environmetrics 6: 127-139.
Conway, D. 2002. Advanced Microwave Scanning Radiometer - EOS Quality Assurance Plan. Huntsville, AL: Global Hydrology and Climate Center.
Dewey, K. F. and R. Heim, Jr. 1981. Satellite Observations of Variations in Northern Hemisphere Seasonal Snow Cover. NOAA Technical Report NESS 87.
Foster, J. L., A. T. C. Chang, Dorothy K. Hall, and A. Rango. 1991. Derivation of Snow Water Equivalent in Boreal Forests Using Microwave Radiometry. Arctic 44(1):147-152.
Goodison, B., A.E. Walker, and F.W. Thirkettle. 1990. Determination of Snowcover on the Canadian Prairies Using Passive Microwave Data. Proceedings of the International Symposium on Remote Sensing and Water Resources. Enschede, The Netherlands, 127-136.
Grody, N. C. and A. N. Basist. 1997. Interpretation of SSM/I Measurements Over Greenland. IEEE Transactions on Geoscience and Remote Sensing 35: 360-366.
Hallikainen, M. T., and P. A. Jolma. 1986. Retrieval of Water Equivalent of Snow Cover in Finland by Satellite Microwave Radiometry. Geoscience and Remote Sensing: IEEE Transactions GE-24(6):855-862.
Hansen, M., R. DeFries, J. R. Townshend, M. Carroll, C. Dimiceli, and R. Sohlberg. 2003. 500m MODIS Vegetation Continuous Fields. College Park, Maryland: The Global Land Cover Facility.
Hansen, M. C., R. S. DeFries, J. R. G. Townshend, M. Carroll, C. Dimiceli, and R. A. Sohlberg. 2003. Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Continuous Fields Algorithm. Earth Interactions, 7 10:15.
Josberger, E. G. and N. M. Mognard. 2000. A Passive Microwave Snow Depth Algorithm with a Proxy for Snow Metamorphism. Proceedings of the Fourth International Workshop on Applications of Remote Sensing in Hydrology, Santa Fe, NM.
Kelly, Richard E. J. and J. L. Foster. The AMSR-E Snow Water Equivalent Product: Status and Future Development. Poster presented at the American Geophysical Union Fall Meeting, San Francisco, CA., 5-9 December 2005a.
Kelly, Richard E. J., J. L. Foster and Dorothy K. Hall. The AMSR-E Snow Water Equivalent Product: Algorithm Development and Progress in Product Validation. Poster presented at the Proceedings of the 28th General Assembly of the Union of International Radio Science, New Delhi, India, 23-29 October 2005b.
Kelly, Richard. E. J., A. T. C. Chang, L. Tsang, and J. L. Foster. 2003. A Prototype AMSR-E Global Snow Area and Snow Depth Algorithm. IEEE Transactions on Geoscience and Remote Sensing 41(2): 230-242.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, Dorothy K. Hall, B. b. Stankov, and A. J. Gasiewski, A.J. Testing AMSR-E Snow Retrievals with Cold Lands Processes Experiment Data. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, and Dorothy K. Hall. The Effect of Sub-pixel A real Distribution of Snow on the Estimation of Snow Depth from Spaceborne Passive Microwave Instruments. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003.
Knowles, K. 2004. EASE-Grid Land Cover Data Resampled from Boston University Version of Global 1 km Land Cover from MODIS 2001, Version 4. Boulder CO, USA: National Snow and Ice Data Center. Digital media.
Krenke, A. 1998, updated 2004. Former Soviet Union Hydrological Snow Surveys, 1966-1996. Edited by NSIDC. Boulder, CO: National Snow and Ice Data Center/World Data Center for Glaciology. Digital media.
Kunzi, K. F., S. Patil and H. Rott. 1982. Snow-cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data. Geoscience and Remote Sensing: IEEE Transactions GE-20(4):452-467.
Matzler, C. 1987. Applications of the Interaction of Microwaves with the Natural Snow Cover. Remote Sensing Reviews Series, Vol. 2. London: Taylor and Frances, Inc. Remote Sensing Rev., 2, 259-391.
Pulliainen, J. T., J. Grandell, and M. T. Hallikainen. 1997. Retrieval of surface temperature in boreal forest zone from SSM/I data. IEEE Transactions on Geosciences and Remote Sensing 35: 1188-1200.
Robinson, D. A. and G. Kukla. 1985. Maximum Surface Albedo of Seasonally Snow Covered Lands in the Northern Hemisphere. Journal of Climate and Applied Meteorology 24: 402-411.
Rott, H. and J. Aschbacher. 1989. Proceedings of the IAHS Third International Assembly on Remote Sensing and Large Scale Global Processes, Baltimore, MD. Publicaton No. 186.
Sturm, M., J. Holmgren, and G. E. Liston. 1995. A Seasonal Snow Cover Classification System for Local to Global Applications. Journal of Climate 8: 1261-1283.
Sun, C. Y., C. M. U. Neale, and J. J. McDonnell. 1996. Snow Wetness Estimates of Vegetated Terrain from Satellite Passive Microwave Data. Hydrologic Processes 10: 1619-1628.
Walker, A. E. and B. E. Goodison. 1993. Discrimination of Wet Snow Cover Using Passive Microwave Satellite Data. Annals of Glaciology 17: 307-311.
For more information regarding related publications, see the Research Using AMSR-E Data Web page. 
Data Set Progress
IN WORK
Distribution
Distribution Media:
FTP
Distribution Size:
Each daily granule is approximately 2.1
Distribution Format:
HDF-EOS
Personnel
Role:
INVESTIGATOR
City:
Waterloo
Province or State:
Ontario
Postal Code:
N2L 3G1
Country:
Canada
Role:
TECHNICAL CONTACT
Phone:
+1 (303) 492-6199
Fax:
+1 (303) 492-2468
Email:
nsidc at nsidc.org
Contact Address:
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
City:
Boulder
Province or State:
CO
Postal Code:
80309-0449
Country:
USA
Role:
INVESTIGATOR
Phone:
301-614-5769
Fax:
301-614-5808
Email:
James.L.Foster.1 at gsfc.nasa.gov
Contact Address:
Laboratory for Hydrospheric Processes
Code 614.3
NASA Goddard Space Flight Center
City:
Greenbelt
Province or State:
MD
Postal Code:
20771
Country:
USA
Role:
INVESTIGATOR
Contact Address:
City University of New York and NASA GSFC
Department of Earth and Atmosphere Sciences
City:
New York
Province or State:
NY
Postal Code:
10031
Country:
USA
Publications/References
Quality Assessment Each HDF-EOS file contains core metadata with Quality Assessment (QA) metadata flags that are set by the Science Investigator-led Processing System (SIPS) at the Global Hydrology and Climate Center (GHCC) prior to delivery to NSIDC. A separate metadata file in XML format is also delivered to NSIDC with the HDF-EOS file; it contains the same information as the core metadata. ... Three levels of QA are conducted with the AMSR-E Level-2 and Level-3 products: automatic, operational, and science QA. If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a granule fails QA, SIPS does not send the granule to NSIDC until it is reprocessed. Level-3 products that fail QA are never delivered to NSIDC (Conway 2002).
Automatic QA
Chang visually examined random samples of SWE products to ensure they were consistent with an understanding of climate and that no gross errors were present. Future validation will involve comparing retrieved SWE values with estimates from airborne gamma observations over the U.S. (Carroll 1997) and with snow gauge data (Carroll et al. 1995), as well as comparing snow extent with MODIS snow maps (Chang and Rango 2000).
Operational QA
AMSR-E Level-2A data arriving at GHCC are subject to operational QA prior to processing higher-level products. Operational QA varies by product, but it typically checks the following criteria for a given file (Conway 2002):
* File is correctly named and sized
* File contains all expected elements
* File is in the expected format
* Required EOS fields of time, latitude, and longitude are present and populated
* Structural metadata is correct and complete
* The file is not a duplicate
* The HDF-EOS version number is provided in the global attributes
* The correct number of input files were available and processed
Science QA
AMSR-E Level-2A data arriving at GHCC are also subject to science QA prior to processing higher-level products. If less than 50 percent of a granule's data is good, the science QA flag is marked 'suspect' when the granule is delivered to NSIDC. In the SIPS environment, the science QA includes checking the maximum and minimum variable values, and percent of missing data and out-of-bounds data per variable value. At the Science Computing Facility (SCF), also at GHCC, science QA involves reviewing the operational QA files, generating browse images, and performing the following additional automated QA procedures (Conway 2002):
* Historical data comparisons
* Detection of errors in geolocation
* Verification of calibration data
* Trends in calibration data
* Detection of large scatter among data points that should be consistent Geolocation errors are corrected during Level-2A processing to prevent processing anomalies such as extended execution times and large percentages of out-of-bounds data in the products derived from Level-2A data.
The Team Lead SIPS (TLSIPS) developed tools for use at SIPS and SCF for inspecting the data granules. These tools generate a QA browse image in Portable Network Graphics (PNG) format and a QA summary report in text format for each data granule. Each browse file shows Level-2A and Level-2B data. These are forwarded from Remote Sensing Systems (RSS) to GHCC along with associated granule information, where they are converted to HDF raster images prior to delivery to NSIDC. SWE is estimated for SD retrievals greater than 1 mm. Based on the 2002-2003 winter AMSR-E data and 38 coincident ground observations in the World Meteorological Organization (WMO) Global Telecommunications System (GTS) network, the standard error is 24.2 cm. Further validation is planned using multiple local, regional, and global data sets. See NSIDC's AMSR-E Validation Data for information about data used to check the accuracy and precision of AMSR-E observations.
5. References and Related Publications
Basist, A., N.C. Grody, T. C. Peterson, and C. N. Williams. 1998. Using the Special Sensor Microwave Imager to Monitor Land Surface Temperatures, Wetness and Snow Cover. Journal of Applied Meteorology 37(9): 888-911.
Brodzik, M. J. 1997. EASE-Grid: A Versatile Set of Equal-Area Projections and Grids. Boulder, CO, USA: National Snow and Ice Data Center.
Brown, R. D. and R. O. Braaten, 1998. Spatial and Temporal Variability of Canadian Monthly Snow Depths, 1946-1995. Atmosphere-Ocean 36: 37-45.
Chang, A. T. C., and A. Rango. 2000. Algorithm Theoretical Basis Document for the AMSR-E Snow Water Equivalent Algorithm, Version 3.1. Greenbelt, MD, USA: NASA Goddard Space Flight Center. (view PDF file)
Chang, A. T. C., J. L. Foster, Dorothy K. Hall, B. E. Goodison, A. E. Walker, and J. R. Metcalfe. 1997. Snow Parameters Derived from Microwave Measurements During the BOREAS Winter Field Experiment. Journal of Geophysical Research 102: 29663-29671.
Chang, A. T. C., J. L. Foster, and Dorothy K. Hall. 1987. Nimbus-7 Derived Global Snow Cover Parameters. Annals of Glaciology 9: 39-44.
Chang, A. T. C., J. L. Foster, Dorothy Hall, A. Rango, and B. Hartline. 1982. Snow Water Equivalence Determination by Microwave Radiometry. Cold Regions Science and Technology 5: 259-267.
Chang, A. T. C., R. E. J. Kelly, J. L. Foster, and Dorothy K. Hall. The Testing of AMSR-E Snow Depth and Snow Water Equivalent Estimates in the Northern Hemisphere. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003a.
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Global SWE Monitoring Using AMSR-E Data. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003
Chang, A. T. C., Richard E. J. Kelly, J. L. Foster, and Dorothy K. Hall. Estimation of Snow Depth from AMSR-E in the GAME-Siberia Experiment Region. Poster presented at the Proceedings of IGARSS, Alaska, USA 2004.
Carroll, T. R. 1997. Integrated Ground-based, Airborne, and Satellite Snow Cover Observations in the National Weather Service. 77th AMS Annual Meeting; Symposium on Integrated Observing Systems, Long Beach, CA.
Carroll, S. S., G. N. Day, N. Cressie, and T. R. Carroll. 1995. Spatial Modeling of Snow Water Equivalent Using Airborne and Ground Based Snow Data. Environmetrics 6: 127-139.
Conway, D. 2002. Advanced Microwave Scanning Radiometer - EOS Quality Assurance Plan. Huntsville, AL: Global Hydrology and Climate Center.
Dewey, K. F. and R. Heim, Jr. 1981. Satellite Observations of Variations in Northern Hemisphere Seasonal Snow Cover. NOAA Technical Report NESS 87.
Foster, J. L., A. T. C. Chang, Dorothy K. Hall, and A. Rango. 1991. Derivation of Snow Water Equivalent in Boreal Forests Using Microwave Radiometry. Arctic 44(1):147-152.
Goodison, B., A.E. Walker, and F.W. Thirkettle. 1990. Determination of Snowcover on the Canadian Prairies Using Passive Microwave Data. Proceedings of the International Symposium on Remote Sensing and Water Resources. Enschede, The Netherlands, 127-136.
Grody, N. C. and A. N. Basist. 1997. Interpretation of SSM/I Measurements Over Greenland. IEEE Transactions on Geoscience and Remote Sensing 35: 360-366.
Hallikainen, M. T., and P. A. Jolma. 1986. Retrieval of Water Equivalent of Snow Cover in Finland by Satellite Microwave Radiometry. Geoscience and Remote Sensing: IEEE Transactions GE-24(6):855-862.
Hansen, M., R. DeFries, J. R. Townshend, M. Carroll, C. Dimiceli, and R. Sohlberg. 2003. 500m MODIS Vegetation Continuous Fields. College Park, Maryland: The Global Land Cover Facility.
Hansen, M. C., R. S. DeFries, J. R. G. Townshend, M. Carroll, C. Dimiceli, and R. A. Sohlberg. 2003. Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Continuous Fields Algorithm. Earth Interactions, 7 10:15.
Josberger, E. G. and N. M. Mognard. 2000. A Passive Microwave Snow Depth Algorithm with a Proxy for Snow Metamorphism. Proceedings of the Fourth International Workshop on Applications of Remote Sensing in Hydrology, Santa Fe, NM.
Kelly, Richard E. J. and J. L. Foster. The AMSR-E Snow Water Equivalent Product: Status and Future Development. Poster presented at the American Geophysical Union Fall Meeting, San Francisco, CA., 5-9 December 2005a.
Kelly, Richard E. J., J. L. Foster and Dorothy K. Hall. The AMSR-E Snow Water Equivalent Product: Algorithm Development and Progress in Product Validation. Poster presented at the Proceedings of the 28th General Assembly of the Union of International Radio Science, New Delhi, India, 23-29 October 2005b.
Kelly, Richard. E. J., A. T. C. Chang, L. Tsang, and J. L. Foster. 2003. A Prototype AMSR-E Global Snow Area and Snow Depth Algorithm. IEEE Transactions on Geoscience and Remote Sensing 41(2): 230-242.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, Dorothy K. Hall, B. b. Stankov, and A. J. Gasiewski, A.J. Testing AMSR-E Snow Retrievals with Cold Lands Processes Experiment Data. Poster presented at the AGU Fall Meeting, San Fransisco, CA., 8-12 December 2003.
Kelly, Richard E. J., A. T. C. Chang, J. L. Foster, and Dorothy K. Hall. The Effect of Sub-pixel A real Distribution of Snow on the Estimation of Snow Depth from Spaceborne Passive Microwave Instruments. Poster presented at the Proceedings of IGARSS, Toulouse, France, 21-25 July 2003.
Knowles, K. 2004. EASE-Grid Land Cover Data Resampled from Boston University Version of Global 1 km Land Cover from MODIS 2001, Version 4. Boulder CO, USA: National Snow and Ice Data Center. Digital media.
Krenke, A. 1998, updated 2004. Former Soviet Union Hydrological Snow Surveys, 1966-1996. Edited by NSIDC. Boulder, CO: National Snow and Ice Data Center/World Data Center for Glaciology. Digital media.
Kunzi, K. F., S. Patil and H. Rott. 1982. Snow-cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data. Geoscience and Remote Sensing: IEEE Transactions GE-20(4):452-467.
Matzler, C. 1987. Applications of the Interaction of Microwaves with the Natural Snow Cover. Remote Sensing Reviews Series, Vol. 2. London: Taylor and Frances, Inc. Remote Sensing Rev., 2, 259-391.
Pulliainen, J. T., J. Grandell, and M. T. Hallikainen. 1997. Retrieval of surface temperature in boreal forest zone from SSM/I data. IEEE Transactions on Geosciences and Remote Sensing 35: 1188-1200.
Robinson, D. A. and G. Kukla. 1985. Maximum Surface Albedo of Seasonally Snow Covered Lands in the Northern Hemisphere. Journal of Climate and Applied Meteorology 24: 402-411.
Rott, H. and J. Aschbacher. 1989. Proceedings of the IAHS Third International Assembly on Remote Sensing and Large Scale Global Processes, Baltimore, MD. Publicaton No. 186.
Sturm, M., J. Holmgren, and G. E. Liston. 1995. A Seasonal Snow Cover Classification System for Local to Global Applications. Journal of Climate 8: 1261-1283.
Sun, C. Y., C. M. U. Neale, and J. J. McDonnell. 1996. Snow Wetness Estimates of Vegetated Terrain from Satellite Passive Microwave Data. Hydrologic Processes 10: 1619-1628.
Walker, A. E. and B. E. Goodison. 1993. Discrimination of Wet Snow Cover Using Passive Microwave Satellite Data. Annals of Glaciology 17: 307-311.
For more information regarding related publications, see the Research Using AMSR-E Data Web page.
Extended Metadata Properties
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Creation and Review Dates
DIF Creation Date:
2004-03-01
Last DIF Revision Date:
2012-05-03
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