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Data Set ID:

AMSR-E/AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture, Version 1

The AMSR-E/AMSR2 Unified Level-2B land product provides a long-term data record by combining AMSR-E and AMSR2 data. This data set includes surface soil moisture estimates derived from L1R brightness temperatures using the Normalized Polarization Difference algorithm (NPD) and the Single Channel Algorithm (SCA) along with ancillary information gridded to the 25 km Equal-Area Scalable Earth Grid (EASE-Grid).

Geographic Coverage

Spatial Coverage:
  • N: 89.24, S: -89.24, E: 180, W: -180

Spatial Resolution:
  • 25 km x 25 km
Temporal Coverage:
  • 23 July 2002 to 4 October 2011
  • 18 May 2012
Temporal Resolution: 50 minute
  • Soils > Soil Moisture/Water Content
  • Vegetation > Plant Characteristics > Vegetation Water Content
Platform(s) AQUA, GCOM-W1
Sensor(s): AMSR-E, AMSR2
Data Format(s):
Version: V1
Data Contributor(s): Thomas Jackson, Steven Chan, Rajat Bindlish, Eni Njoku

Data Citation

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.

Jackson, T., S. Chan, R. Bindlish, and E. G. Njoku. 2016. AMSR-E/AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].

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Detailed Data Description


Level-2B soil moisture data consist of point data in Hierarchical Data Format - Earth Observing System5 (HDF-EOS5) format where the resulting grid is in a table format rather than a grid that image processing programs can easily visualize. Files contain core metadata, product-specific attributes, and data fields. The data fields are summarized in Table 2, which uses the data types in Table 1 in the field descriptions:

Table 1.Data Types
Notation Description
Float64: 64-bit (8-byte) floating-point integer
Float32: 32-bit (4-byte) floating-point integer
Int32: 32-bit (4-byte) signed integer

Note: The number of records per granule depends on the number of gridded points over land.

Table 2. Data Fields
Field Name Data Type Description Units Fill Value
Time Float64 Scan start time in International Atomic Time in seconds with 01 January 1993 00:00:00 as the zero base (TAI93). TAI93 n/a
Latitude Float64 Gridded acquisition latitude (-90.0 to 90.0) degrees 99 / 98
Longitude Float64 Gridded acquisition longitude (-180.0 to 180.0) degrees 999 / 998
RowIndex Int32 EASE-Grid row index (1-586) n/a -9999
ColumnIndex Int32 EASE-Grid column index (0-1382) n/a -9999
TBH10r2* Float32 10.7 GHz H-polarized brightness temperature (TB) K -9999
TBV10r2 Float32 10.7 GHz V-polarized TB K -9999
TBH18r2 Float32 18.7 GHz H-polarized TB K -9999
TBV18r2 Float32 18.7 GHz V-polarized TB K -9999
TBH23r2 Float32 23.8 GHz H-polarized TB K -9999
TBV23r2 Float32 23.8 GHz V-polarized TB K -9999
TBH36r2 Float32 36.5 GHz H-polarized TB K -9999
TBV36r2 Float32 36.5 GHz V-polarized TB K -9999
TBH89r2 Float32 89.0 GHz H-polarized TB K -9999
TBV89r2 Float32 89.0 GHz V-polarized TB K -9999
VegetationRoughnessNPD Float32 Vegetation roughness n/a -9999
SoilMoistureNPD Float32 Soil moisture as determined by the NPD algorithm  cm3/cm3 -9999
RetrievalQualityFlagNPD Int32 Value: 0= valid retrieval, 1= invalid retrieval n/a -9999
SoilMoistureSCA Float32 Soil moisture as determined by the SCA algorithm, measured in volume fraction of water/soil cm3/cm3 -9999
RetrievalQualityFlagSCA Int32 Value: 0= valid retrieval, 1= invalid retrieval n/a -9999
FlagCountAllSamples** Int32 Number of TB footprints n/a n/a
FlagCountGoodSamples Int32 Number of good TB footprints n/a n/a
FlagCountRFI Int32 Number of RFI contaminated TB footprints n/a n/a
FlagCountInvalidTBRange Int32 Number of out of range TB footprints n/a n/a
FlagCountWater Int32 Number of TB footprints over water n/a n/a
FlagCountIce Int32 Number of TB footprints over ice n/a n/a
FlagCountSnow Int32 Number of TB footprints over snow n/a n/a
FlagCountFrozenGround Int32 Number of TB footprints over frozen ground n/a n/a
FlagCountRain Int32 Number of TB footprints over rain n/a n/a
FlagCountWetland Int32 Number of TB footprints over wetland n/a n/a
FlagCountUrban Int32 Number of TB footprints over an urban area n/a n/a
FlagCountLow2ModerateVWC Int32 Number of TB footprints over low to moderate vegetation water content n/a n/a
FlagCountDenseVWC Int32 Number of TB footprints over dense vegetation water content n/a n/a
FlagCountMissingSoilTexture Int32 Numberof TB footprints over missing soil texture data n/a n/a
FlagCountMissingNDVI Int32 Number of TB footprints over missing NDVI data n/a n/a

*All gridded Brightness Temperatures are derived from the JAXA L1R Resolution 2 (resolution of the 10 GHz channels) TB data.
**All SCA flag values are related to pixels initially identified as land. All SCA flag values are per grid cell.

Sample Data Record

Figure 1 is a sample of some of the data fields for soil moisture data.

Figure 1. Sample Data File

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File Naming Convention

This section explains the file naming convention used for this product with an example. The date and time correspond to the first scan of the granule. 

Example file name: AMSR_U2_L2_Land_B01_201610260414_D.h5


Refer to Table 3 for the valid values for the file name variables listed above.

Variable Description
Table 3. Valid Values for the File Name Variables
AMSR Satellite sensor
Ui Indicates the sensor used (i=2 for AMSR2)
L2 Level-2 data
X Product Maturity Code (Refer to Table 4 for valid values.)
## version number
yyyy four-digit year
mm two-digit month
dd two-digit day
hh hour, listed in UTC time, of first scan in the file
mm minute, listed in UTC time, of first scan in the file
f orbit direction flag (A = ascending, D = descending)
he5 HDF-EOS5 data format

Table 4. Valid Values for the Product Maturity Code
Product Maturity Code Description
P Preliminary - refers to non-standard, near-real-time data available from NSIDC. These data are only available for a limited time until the corresponding standard product is ingested at NSIDC.
B Beta - indicates a developing algorithm with updates anticipated.
T Transitional - period between beta and validated where the product is past the beta stage, but not quite ready for validation. This is where the algorithm matures and stabilizes.
V Validated - products are upgraded to Validated once the algorithm is verified by the algorithm team and validated by the validation teams. Validated products have an associated validation stage. Refer to Table 5 for a description of the stages.
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File Size and Volume

Each half-orbit granule is on the average 1.8 MB. The actual size depends on the number of gridded points over land.

The volume of the data set is ~11.9 MB/day.

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Spatial Coverage

Spatial Coverage Map

The following map shows the typical coverage of a single granule.

Figure 2. Spatial Coverage for Oct. 27, 2016

Coverage is global between 89.24° N and 89.24° S, except for snow-covered and densely-vegetated areas. See AMSR-E Pole Hole page for a description of holes that occur at the North and South Poles. The swath width is 1450 km.

Spatial Resolution

Input brightness temperature data for all channels are resampled to a global cylindrical 25 km EASE-Grid cell spacing and have an effective spatial resolution of approximately 48 km (resolution of the 10.7 GHz channel).


The projection is EASE-Grid.

Grid Description

Level-1R brightness temperatures are resampled to a global cylindrical EASE-Grid (1383 columns by 586 rows) with a nominal grid spacing of 25 km (true at 30° S). The data consist of HDF-EOS point data where the resulting grid is in table format, rather than a grid that image processing programs can easily visualize. In the case of the Level-2R soil moisture data, each geophysical variable value has a corresponding EASE-Grid row and column index.

Please refer to All About EASE-Grid for more information on related products and tools.

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Temporal Coverage

Temporal coverage is from 19 June 2002 to 04 October 2011 for the AMSR-E data, and 18 May 2012 to present for AMSR2 data.

Temporal Resolution

Each swath spans approximately 50 minutes. The data sampling interval is 2.6 msec per sample for the 6.9 GHz to 36.5 GHz channels, and 1.3 msec for the 89.0 GHz channel. A full scan takes approximately 1.5 seconds. Both AMSR-E and AMSR2 collect 243 data points per scan for the 6.9 GHz to 36.5 GHz channels, and 486 data points for the 89.0 GHz channel.

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Parameter or Variable

Surface soil moisture is the parameter for this data set.

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Software and Tools

Software and Tools

For tools that work with HDF-EOS data, refer to the NSIDC: Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site.

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Data Acquisition and Processing

NOTE: Backprocessing of the AMSR-E and AMSR2 data is ongoing. Forward processing continues for the AMSR2 data.

Data Source

The AMSR-U data consists of resampled L1R brightness temperatures, geolocation information, metadata, quality assessment flags, and ancillary data from two sensors: AMSR-E and AMSR2.

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Derivation Techniques and Algorithms

Processing of the AMSR-E/AMSR2 Unified data set includes both an updated Normalized Polarization Difference (NPD) algorithm and the first standard Single Channel Algorithm (SCA). NPD and SCA codes are integrated together to create consistent AMSR-E and AMSR2 soil moisture products. Further information regarding the algorithms will be available in the Algorithm Theoretical Basis Document when it is completed

NPD Algorithm

The NPD soil moisture algorithm uses Polarization Ratios (PR), which are sometimes called normalized polarization differences of the AMSR channel brightness temperatures. PR is the difference between the vertical and horizontal brightness temperatures at a given frequency divided by their sum. This effectively eliminates or reduces surface temperature effects, which is necessary since no dynamic ancillary surface temperature data are input to the algorithm. The algorithm first computes a vegetation/roughness parameter g using PR 10.7 GHz and PR 18.7 GHz, plus three empirical coefficients. Soil moisture is then computed using departures of PR 10.7 GHz from a baseline value, plus four additional coefficients. The baseline values for PR 10.7 GHz are based on monthly minima at each grid cell over an annual cycle.

The vegetation/roughness parameter g incorporates effects of vegetation and roughness together, because both have the same functional form (exponential) in their influence on the normalized polarization differences in the simplified model used in the retrieval algorithm. g may be interpreted as an equivalent vegetation water content with units of kg m-2. In a desert with no vegetation, any value of g greater than zero is due to roughness only. The value of g reflects the influence of roughness on the polarization ratio as if equivalent vegetation of amount g (kg m-2) were present. If the surface were smooth everywhere, then g would equal the vegetation water content in kg m-2 since the roughness contribution would be zero. Vegetation water content and roughness cannot be determined independently from g, and it is computed primarily as a lumped correction factor for the soil moisture retrieval (Njoku and Chan 2006).

Refer to Njoku et al. (2004) for an assessment of calibration biases over land, and methods used to correct these biases.

SCA Algorithm

The SCA approach uses horizontally polarized (h-pol) brightness temperature observations from the lowest frequency channel used (in this case 10.7 GHz) due to its highest sensitivity to soil moisture observations. The SCA approach is based on the simplified radiative transfer model developed under the assumption of equal canopy and soil temperature (Jackson 1993). In the SCA approach, brightness temperatures are converted to emissivity using a surrogate for the effective physical temperature (T) of the emitting layer. The derived emissivity (eobs) is corrected for vegetation and surface roughness to obtain the smooth soil emissivity (esmooth). The Fresnel equation is then used to determine the dielectric constant of the soil-water mixture (k). Finally, a dielectric mixing model is used to obtain the soil moisture (SM).

Processing Steps

AMSR-U consists of the completed AMSR-E dataset and the ongoing AMSR2 dataset, processed using the JAXA L1R as input and joint algorithms (NPD and SCA) for processing. All L1R input data is ingested via internet transfer to the Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center Distributed Active Archive Center (GHRC). The granule is defined as one-half of one orbit, the division being at the poles, so that a granule is descending (North Pole to South Pole) or ascending (South Pole to North Pole). There are approximately 29 Level 1R granules per day. The AMSR-U Level 2B land product is generated at the AMSR SIPS and includes metadata and browse imagery. These data are re-mapped to a 25 km Equal-Area Scalable Earth Grid (EASE-Grid).  Each granule is stored in HDF-EOS5. More detailed information is forthcoming.

Error Sources

Quality Assessment

Each HDF-EOS file contains core metadata with Quality Assessment (QA) metadata flags, RetrievalQualityFlagNPD and RetrievalQualityFlagSCA, that are set by the SIPS-GHRC prior to delivery to NSIDC. A separate metadata file (.xml file extension) 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: 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 (Conway 2002).

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Sensor or Instrument Description

For detailed descriptions of the AMSR-E and AMSR2 instruments, refer to the AMSR-E Instrument Description and AMSR2 Channel Specification and Products respectively.

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References and Related Publications

Contacts and Acknowledgments

Investigator(s) Name and Title

Thomas Jackson
United States Department of Agriculture
Agricultural Research Service
Hydrology and Remote Sensing Laboratory
Beltsville, MD 20705 USA

Steven Chan
Jet Propulsion Laboratory
M/S 300-362A
4800 Oak Grove Drive
Pasadena, CA 91109

Rajat Bindlish
NASA Goddard Space Flight Center
Hydrological Sciences Laboratory
Code 617, Bldg 33, G216
Greenbelt, MD 20771 USA

Eni G. Njoku
NASA/Jet Propulsion Laboratory
M/S 300-233
4800 Oak Grove Drive
Pasadena, CA 91109 USA

Document Information


December 2016


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