Data Set ID:
AU_SI12

AMSR-E/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1

The AMSR-E/AMSR2 Unified Level-3 12.5 km product provides brightness temperatures, sea ice concentration, and snow depth over sea ice for the Northern and Southern Hemisphere, as well as sea ice motion for the Arctic. This data set includes daily brightness temperature fields for channels ranging from 18.7 GHz through 89.0 GHz, daily sea ice concentration fields, and daily sea ice concentration difference fields for ascending orbits, descending orbits, and full orbit daily averages. Snow depth over sea ice is provided as a five-day running average for the Arctic and Antarctic. Sea Ice motion is provided daily for tracking ice movement over consecutive days in the Arctic.

Note: This product uses the Japan Aerospace Exploration Agency (JAXA) AMSR2 Level-1R input brightness temperatures that are calibrated, or unified, across the JAXA AMSR-E and JAXA AMSR2 Level-1R products.

This is the most recent version of these data.

Version Summary:

Initial release

COMPREHENSIVE Level of Service

Data: Data integrity and usability verified; data customization services available for select data

Documentation: Key metadata and comprehensive user guide available

User Support: Assistance with data access and usage; guidance on use of data in tools and data customization services

See All Level of Service Details

Parameter(s):
  • Microwave > Brightness Temperature
  • Sea Ice > Sea Ice Concentration
  • Sea Ice > Snow Depth
Data Format(s):
  • HDF-EOS
Spatial Coverage:
N: -39.23, 
N: 89.24, 
S: -89.24, 
S: 30.98, 
E: 180, 
E: 180, 
W: -180
W: -180
Platform(s):GCOM-W1
Spatial Resolution:
  • 12.5 km x 12.5 km
Sensor(s):AMSR2
Temporal Coverage:
  • 2 July 2012
Version(s):V1
Temporal Resolution1 day, 2 day, 5 dayMetadata XML:View Metadata Record
Data Contributor(s):Walter Meier, Thorsten Markus, Josephino 'Joey' Comiso

Geographic Coverage

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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.

Meier, W. N., T. Markus, and J. C. Comiso. 2018. AMSR-E/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/RA1MIJOYPK3P. [Date Accessed].
Created: 
9 July 2018
Last modified: 
18 March 2019

Data Description

Parameters

Parameter Description Values
Brightness Temperature

Gridded vertical and horizontal brightness temperature, in kelvin, from the following channels: 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz. There are separate HDF-EOS5 fields for afternoon ascending orbits, morning descending orbits, and full orbit daily averages. Brightness temperatures are referred to as Tb in this document.

Brightness temperature data have a scale factor of 0.1. Multiply data values by 0.1 to obtain Tb in kelvin (K).

  • 0: Missing or out-of-bounds data
  • 50-320 K
Sea Ice Concentration Gridded Arctic and Antarctic sea ice concentration, in percent, using the NASA Team 2 (NT2) algorithm, with separate fields for ascending orbits, descending orbits, and full orbit daily averages.  
  • 0: Open Water
  • 1 - 100: Percent sea ice concentration
  • 110: Missing data
  • 120: Land

Sea Ice Concentration Algorithm Differences

The Bootstrap and NT2 algorithms are both used for computing sea ice concentration. This parameter provides the Arctic and Antarctic differences between the Bootstrap and NT2 algorithms in gridded format, with separate fields for ascending orbits, descending orbits, and full orbit daily averages.

  • 0: Bootstrap sea ice concentration is equal to the NT2 concentration
  • 1 - 100: Bootstrap sea ice concentration is greater than NT2 concentration
  • -1 to -100: Bootstrap sea ice concentration is less than NT2 concentration
  • 110: Missing data
  • 120: Land
Snow Depth on Sea Ice Gridded Arctic and Antarctic five-day running average for snow depth on sea ice, measured in centimeters.

  • -1: Not calculated
  • 110: Missing data
  • 120: Land
  • 130: Indeterminate
  • 140: Multiyear sea ice
  • 150: Variability in snow depth
  • 160: Snow melt

Note: A value of -1 indicates snow depth processing was skipped because an input file required for processing was unavailable. When this occurs the entire snow depth grid array is assigned a value of -1.

Sea ice Motion Non-gridded, daily tracking of sea ice drift for the Arctic only. Provides sea ice motion over consecutive days in the X and Y direction in cm/s. Ice motion is not available day one because there is no previous day precedent for comparison. See Figure 1 and Tables 2 and 3 for a description of values.

Parameter Details

A sea ice motion tabular data field is included in each HDF file. The first two rows in the motion data file are header information; rows 3 and beyond are data. The figure and tables below provide guidance for interpreting the header and data values beginning with row 2 of the motion data file.

Figure 1. This figure shows a sample sea ice motion data file.
Table 1. Header values from line two of the sea ice motion data file
Column Value Definition
1 1736 Number of motion estimates in the sample file. This value is date dependent and will vary across files.
2 1 Array of 1 dimension corresponding to 1736 x 1
3 608 Number of columns in in the 12.5 km input Tb grid
4 896 Number of rows in in the 12.5 km input Tb grid
5 6.9119 Variable artifact which can be ignored

Table 2. Data values from line three of the sample sea ice motion data file
Column Value Definition
1 317 Column number from the 12.5 km input Tb grid
2 305 Row number from the 12.5 km input Tb grid
3 0.00 Sea ice motion in the X direction in cm/s
4 -0.00 Sea ice motion in the Y direction in cm/s
5 1.0 Quality correlation feature matching parameter between the two images. This values vary between 0 and 1, with 1 indicating the best correlation and 0 indicating the lowest correlation.

    File Information

    Format

    Data are in HDF-EOS5 32-bit signed integer format. For software and more information, visit the HDF-EOS website.

    File Structure

    Each data file includes 62 gridded parameter fields (31 Northern Hemisphere and 31 Southern Hemisphere), three metadata fields (CoreMetadata, StructMetadata, and Processing_Facility), one sea ice motion tabular data field, and four supplemental gridded fields. Figure 2 shows a subset of the gridded parameter fields.

    Figure 2. This figure shows a subset of the Northern Hemisphere parameter fields as displayed with HDFView software.

    Ancillary Data

    There are two ancillary text files (.qa and .ph) included with each day of data. The .qa text file provides a quality assessment summary. The .ph text file provides a list of the input data files.

    Parameter Naming Convention

    Table 3 explains the parameter name variable values using the parameter convention example below.

    Example parameter convention: 
    SI_12km_HE_PARAM_TIME
    Table 3. Parameter Name Variables
    Variable Values
    SI Indicates sea ice.
    12km Indicates a nominal spatial resolution of 12 km.
    HE Indicates the observation hemisphere; NH: Northern Hemisphere, SH Southern Hemisphere.
    PARAM Indicates the measured parameter; 18 GHz Tbs, 23 GHz Tbs, 36 GHz Tbs, 89 GHz Tbs, ICECON, ICEDIFF and SNOWDEPTH. Brightness Temperature parameters also include a polarization identifier; V: Vertical and H: Horizontal.
    TIME Indicates the observation time period; ASC: 12 hour ascending orbit, DSC: 12 hour decending orbit, DAY: Full orbit daily average, 5DAY: Five day running average.
    Example parameter name: 
    SI_12km_NH_89H_ASC
    
    

    File Naming Convention

    Table 4 explains the file name variable values using the file name convention example below.

    Example file convention:
    AMSR_U2_L3_SeaIce12km_X##_yyyymmdd.he5
    Table 4. File Name Variables
    Variable Description
    AMSR Advanced Microwave Sounding Radiometer
    U Unified
    2 AMSR2
    L3 Level-3
    12km Indicates each cell has a nominal resolution of 12.5 km x 12.5 km
    X## Product Maturity Code and Version (refer to Table 5)
    yyyy Four-digit year
    mm Two-digit month
    dd Two-digit day
    he5 Indicates HDF-EOS5 file format
    Example file name:
    AMSR_U2_L3_SeaIce12km_B02_20180509.he5

    Table 5 provides the meaning for the product maturity code variable values.

    Table 5. Variable Values for the Product Maturity Code
    Variables Description
    B Beta: Indicates a developing algorithm with updates anticipated.
    T Transitional: Indicates the period between Beta and Validated where the product is past the Beta stage but not ready for validation. At this stage the algorithm matures and stabilizes.
    V Validated: Products are upgraded to Validated once the algorithm is verified and validated by the science team. Validated products have an associated validation stage. Refer to Table 2 in the Naming Conventions section of the AMSR Unified Data Versions page for a description of the stages.

    Supplemental Files

    The gridded fields listed below are supplemental to the gridded parameter fields described above.

    • mask_date: This field shows the year, month, and day the multiyear mask was created.
    • multiyear_init_year: This field shows the year the multiyear and variability_5day masks were initialized. 
    • multiyear_mask: This field is updated daily and is used with the 5-day snow depth on sea ice grid to mask multiyear ice.
    • variability_5day: This field provides a running 5-day snow melt mask and is used with the 5-day snow depth on sea ice grid to identify melt areas.
    Figure 3. This figure shows the supplementary gridded fields included with the data file.

    Spatial Information

    Coverage

    Figure 4. This figure shows the Northern Hemisphere Coverage Extent
    Figure 5. This figure shows the Southern Hemisphere Coverage Extent
    Note that a small gap in coverage exists at the poles due to the path of the ascending and descending orbits. Known as the pole hole, this gap is consistent for both AMSR2 and AMSR-E data sets. For additional information see the AMSR-E Pole Hole page.

    Spatial Resolution

    The nominal spatial resolution of the polar grids is 12.5 km. However, because the polar grids are not equal area, the actual resolution varies by latitude.

    Geolocation

    Tables  6, 7, 8, and 9 provide projection and grid details for this data set.

    Table 6. Projection Details
    Region Northern Hemisphere Southern Hemisphere
    Geographic coordinate system Not Specified Not Specified
    Projected coordinate system NSIDC Sea Ice Polar Stereographic North NSIDC Sea Ice Polar Stereographic South
    Longitude of true origin 0 0
    Latitude of true origin 70° N 70° S
    Scale factor at longitude of true origin 1 1
    Datum Not Specified Not Specified
    Ellipsoid/spheroid Hughes 1980 Hughes 1980
    Units Meter Meter
    False easting 0 0
    False northing 0 0
    EPSG code 3411 3412
    PROJ4 string

    +proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +a=6378273 +b=6356889.449 +units=m +no_defs

    +proj=stere +lat_0=-90 +lat_ts=-70 +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378273 +b=6356889.449 +units=m +no_defs 

    Reference http://epsg.io/3411 http://epsg.io/3412

    Table 7. Grid Details
    Region Northern Hemisphere Southern Hemisphere
    Grid cell size (x, y pixel dimensions) 12.5 km 12.5 km
    Number of rows 896 664
    Number of columns 608 632
    Geolocated lower left point in grid -3850 E, -5350 N -3950 E, -3950 S
    Nominal gridded resolution 12.5 km 12.5 km
    Grid rotation N/A N/A
    ulxmap – x-axis map coordinate of the center of the upper-left pixel (XLLCORNER for ASCII data) -3850 -3950
    ulymap – y-axis map coordinate of the center of the upper-left pixel (YLLCORNER for ASCII data) 5850 4350

    The origin of each x, y grid is the pole. The tables below show the approximate outer boundaries for the Arctic and Antarctic grids. Corner points are listed starting from the upper left corner and progress clockwise. Interim rows define boundary midpoints.

    Table 8. Arctic Grid Boundary Details

    X (km)

    Y (km)

    Latitude (deg)

    Longitude (deg)

    Pixel Location

    -3850 5850 30.98 168.35 corner
    0 5850 39.43 135.00 midpoint
    3750 5850 31.37 102.34 corner
    3750 0 56.35 45.00 midpoint
    3750 -5350 34.35 350.03 corner
    0 -5350 43.28 315.00 midpoint
    -3850 -5350 33.92 279.26 corner
    -3850 0 55.50 225.00 midpoint

    Table 9. Antarctic Grid Boundary Details

    X (km)

    Y (km)

    Latitude (deg)

    Longitude (deg)

    Pixel Location

    -3950 4350 -39.23 317.76 corner
    0 4350 -51.32 0.00 midpoint
    3950 4350 -39.23 42.24 corner
    3950 0 -54.66 90.00 midpoint
    3950 -3950 -41.45 135.00 corner
    0 -3950 -54.66 180.00 midpoint
    -3950 -3950 -41.45 225.00 corner
    -3950 0 -54.66 270.00 midpoint

    Geolocation Tools

    NSIDC provides geolocation tools for polar stereographic data sets. The first two tools in the list below enable users to obtain latitude/longitude coordinates from (i,j) coordinates and vice versa. The last tool in the list enables users to identify pixel areas.

    Land Masks

    A 12.5 km Northern Hemisphere land mask called amsr_gsfc_12n.hdf and a 12.5 km Southern Hemisphere land mask called amsr_nic_12s.hdf are available for use with this product.

    Temporal Information

    Coverage

    The temporal coverage of this data set extends from 02 July 2012 to the present.

    Resolution

    Brightness temperatures, sea ice concentrations, and sea ice concentration difference fields are provided in three daily-averaged composites: ascending orbits, descending orbits, and full orbits. Snow depth over sea ice is provided as five-day running averages for full orbits. The sea ice motion tabular data field is provided daily. It compares Tbs for the current run with Tbs from the previous run. Typically, the comparison will be based on consecutive calendar days. Therefore, the motion product will always be generated except for the first day, which has no precedent for comparison.

    Sample Data Images

    Figure 6. Sample Data Image
    This Land, Atmosphere, Near real-time Capability for EOS (LANCE) image shows AMSR2 Northern Hemisphere 12.5 km Sea Ice Concentration for 05 July 2016.
    File name: AMSR_2_L3_SeaIce12km_R00_20160705_N_CON.png

    Data Acquisition and Processing

    Background

    The AMSR-E/AMSR2 Unified Level-3 Tbs, sea ice concentration, and snow depth products are derived from JAXA AMSR2 Level-1R (L1R) data. These data consist of swath observations from six channels which have been resampled at JAXA. The Tb sensor footprints, also known as the instantaneous field of view (IFOV), vary with frequency. The resampling procedure remaps the Tbs to sets of consistent footprint sizes using the Backus-Gilbert method (Backus et al. 1967). Each resampled set corresponds to the footprint of one frequency and contains that frequency plus all higher-resolution frequencies. Therefore, the number of channels in each resampled set of Tbs varies. See the JAXA Level 1R documentation or Maeda et al. 2016 for more information.

    Sources

    Table 10 below specifies the input sources for the parameters included with this product. The L1R inputs are swath Tbs which are intercalibrated to match the JAXA AMSR-E Tb observations, thus enabling consistent Tbs to be obtained across the JAXA AMSR2 and AMSR-E products.

    Table 10.  Source Information for Data Set Parameters
    Data Set Input Sources
    Brightness Temperatures Grids AMSR2 L1R resampled Tb observations from channel: 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89 GHz
    Sea Ice Concentration Grids AMSR2 L1R resampled Tb observations from channel: 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89 GHz
    Sea Ice Concentration Difference Grids AMSR2 L3 NT2 sea ice concentration grids and AMSR2 L3 Bootstrap sea ice concentration grids
    Snow Depth on Sea Ice Grid AMSR2 L1R resampled Tb observations from channel: 18.7 GHz and 89 GHz
    Sea Ice Motion Field AMSR2 L3 Tb observations from channel: 36.5 GHz

    Derivation Techniques for Brightness Temperature Grids

    Swath data from the 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89 GHz channels are mapped onto the 12.5 km polar stereographic grid by converting the geodetic latitude and longitude for the center of each scene station, such as the observation footprint, into AMSR2 map-grid coordinates. Scene station map-grid coordinates determine grid cell assignments. Observations that fall outside the AMSR2 polar grid are ignored. For each grid cell, Tbs observed over a 24-hour period (midnight to midnight Greenwich Mean Time) are summed and then divided by the total number of observations to obtain a daily-averaged brightness temperature value. If no observations fall within a grid cell for a given day, the average Tb is labeled 'missing'. The 24-hour averaging is done three ways: for all ascending orbits, for all descending orbits, and for the daily average of all orbits.

    Derivation Techniques for Sea Ice Concentration Grids

    Sea ice concentration is derived using JAXA AMSR2 L1R resampled Tb observations from the 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89 GHz channels. Figure 7 shows the sources for the NASA AMSR2 and NASA AMSR-E sea ice concentration products.

    Note that the NASA AMSR-E (AE_SI12) products at the National Snow and Ice Data Center (NSIDC DAAC) currently only use the L2A Tb observations provided by Remote Sensing Systems (RSS). In the future, NASA AMSR-E Tb observations and sea ice concentration products may be reprocessed with the unified JAXA L1R Tb observations and archived at the NSIDC DAAC. The use of swath Tbs instead of averaged Tbs is important because atmospheric influence on the Tbs is nonlinear, and the use of averaged Tbs would dilute the atmospheric signal.
    Figure 7. This figure shows the sources for the NASA AMSR2 L3 (AU_SI12) and NASA AMSR-E L3 (AE_SI12) sea ice concentration products. The NASA AMSR2 L3 sea ice concentration product is derived from JAXA AMSR2 L1R Tbs, which are intercalibrated with JAXA AMSR-E L1R Tbs (boxes with white background). The NASA AMSR-E L3 sea ice concentration product is derived from RSS AMSR-E L2A Tbs (boxes with yellow background). In the future, the NASA AMSR-E L3 sea ice concentration product may be reprocessed using JAXA AMSR-E L1R Tbs.

    Intercalibration of Sea Ice Concentration

    NASA sea ice concentration estimates derived from AMSR2 Tbs were intercalibrated with NASA sea ice concentration estimates derived from AMSR-E Tbs through a two-step process. First, AMSR-E slow rotation (2 RPM) raw Tbs were used to derive regression equations from colocated pairs of AMSR2 and AMSR-E Tbs, as shown in Figure 8. The regression equations are used by the Science Investigator-led Processing Systems (SIPS) to modify AMSR2 Tbs into AMSR-E equivalent Tbs, which are then input into the same NT2 sea ice concentration algorithm that was used for the AMSR-E standard products. The average regression and correlation coefficients used in the AMSR2 processing are provided in Table 1 of the AMSR2 Sea Ice Algorithm Theoretical Basis Document (ATBD), (Meier et al. 2017).

    Figure 8. This figure shows an AMSR2 swath from January 2013 overlaid with AMSR-E slow rotation observations shown as red, orange, and yellow lines. From January 2013 to January 2014, the AMSR-E instrument continued to operate in slow rotation mode. During this time, regression equations were derived from collocated pairs of AMSR2 and AMSR-E Tbs. The regression equations are used to modify AMSR2 Tbs into AMSR-E equivalent Tbs.

    Algorithm

    The sea ice algorithm used for AMSR2 and AMSR-E are the same. The only substantial difference is that AMSR2 Tbs are intercalibrated to match AMSR-E Tbs, so that the algorithm coefficients can remain the same and obtain consistent geophysical estimates across both AMSR-E and AMSR2.

    The 12.5 km sea ice concentration product is generated using the NT2 algorithm described by Markus et al. 2000 and Markus et al. 2009 for both the Arctic and the Antarctic. The NT2 algorithm uses two ratios of brightness temperatures: polarization ratio (PR) and spectral gradient ratio (GR). These ratios are calculated using the following two equations:

    Equation 1. Polarization Ratio
    Equation 1. Polarization Ratio

    Equation 2. Spectral Gradient Ratio

    where Tb is the brightness temperature at frequency ν for the polarized component p: vertical (V) or horizontal (H).

    The NT2 algorithm uses these ratios to identify two ice types for the Arctic and Antarctic. In the Arctic, the two ice types correspond to first-year ice and multiyear ice. First-year ice is defined as ice that has formed since the previous summer. Multiyear ice is defined as ice that has survived at least one summer melt season. In the Antarctic, there is little multiyear ice and the two ice types represent different regimes of snow-covered sea ice. The distribution of these ice types is shown in Figure 1 (top section) of the ATBD.

    The primary source of error in the original NASA team algorithm was attributed to conditions in the surface layer such as surface glaze and layering (Comiso et al. 1997), which can significantly affect the horizontally polarized 19 GHz Tb (Matzler et al. 1984), leading to increased PR(19) values and thus an underestimate of ice concentration. The use of horizontally polarized channels makes it imperative to resolve a third ice type to overcome the difficulty of these surface effects on the emissivity of the horizontally polarized component of the Tb.

    GR(89V19V) and GR(89H19H) are gradient ratios used to resolve the ambiguity between pixels with low ice concentration and pixels with significant surface effects. The difference between these two GRs (ΔGR) is used as a measure of the magnitude of surface effects. Based on this analysis, a new ice type is introduced (Type C), which represents ice having significant surface effects (see Figure 1 bottom section in the ATBD).

    The NT2 algorithm includes an atmospheric correction scheme as an inherent part of the algorithm. The response of the Tbs to different weather conditions is calculated using an atmospheric radiative transfer model (Kummerow et al. 1993). Input data for the model are the emissivities of first-year sea ice under winter conditions (Eppler et al. 1992), with modifications to achieve agreement between modeled and observed ratios.

    Atmospheric profiles are used as another independent variable in the algorithm. There are twelve profiles that include different cloud properties such as cloud base, cloud top, cloud liquid water (Fraser et al. 1975), and average atmospheric temperatures and humidity profiles for summer and winter. The profiles are combined with the radiative transfer model to develop a look-up table of Tb values corresponding to all concentrations (0%-100% in 1% intervals) of the two ice types for each of the twelve atmospheric profiles. This results in a model solution space of 101x101x12, or 122,412 possible solutions. For each grid cell, the possible solutions are iterated across to find the solution that best matches the combination of observed Tbs.

    Weather Filters

    Two additional weather filters are applied to correct for severe weather effects over open ocean. These filters are based on spectral gradient ratios and implemented using threshold values similar to those used by the original NASA Team Algorithm (Gloersen et al. 1986) and (Cavalieri et al. 1995). Figure 5 in the ATBD shows sea ice concentration maps with and without atmospheric correction.

    Ocean Climatology Mask

    This filter is based on monthly climatological sea surface temperatures (SSTs) from the National Oceanic and Atmospheric Administration (NOAA) ocean atlas. This filter is updated on a monthly basis and removes all ice outside of the SST mask, including land spillover. In the Northern Hemisphere, any pixel where the monthly SST is greater than 278 K is set to zero; whereas in the Southern Hemisphere, any pixel where the monthly SST is greater than 275 K is set to zero.

    Land Spillover Correction

    The spillover of land classified pixels and water classified pixels leads to erroneous ice concentrations along the coast lines adjacent to open water. This makes operational usage of these maps cumbersome. To overcome this difficulty, a five step pixel classification scheme is applied to delineate land pixels from water pixels. See section 3.2.1.2 and Figure 4 in the ATBD for a detailed description of the classification process and to view ice concentration maps, with and without land spillover correction.

    Processing

    1. Calculate sea ice concentration
    To calculate sea ice concentration, the NT2 algorithm is run using the intercalibrated JAXA L1R input Tbs.  Brightness temperature values are calculated for each ice concentration and weather combination and, for each of those solutions, the modeled ratios PR(19), PR(89), and ΔGR are calculated. The three observed ratios are then compared with the three modeled ratios. The indices where the differences are smallest will determine the final ice concentration combination.

    2. Distinguish new ice from existing ice with surface effects
    A gradient ratio (37V19V) with a threshold of –0.02 is used to resolve new ice from existing ice with surface effects (Type C). New ice is defined for pixels where GR(37V19V) is above this threshold and Type C ice is defined for pixels where GR(37V19V) is below this threshold.

    3. Apply weather filters
    To eliminate severe weather effects over open ocean, weather filters based on the spectral gradient ratio are applied. The threshold for the GR(37V19V) NT weather filter (Gloersen and Cavalieri 1986) is 0.05, while the threshold for the GR(22V19V) NT weather filter (Cavalieri et al. 1995) is 0.045. Where the GR values exceed these thresholds, the sea ice concentrations are set to zero.

    4. Create sea ice concentration grids
    After the algorithm is run on JAXA L1R Tbs, sea ice concentrations derived from the ascending orbit, descending orbit, and full orbit, are each mapped to separate 12.5 km polar stereographic grids. The gridding of the sea ice concentration fields is done using the same drop-in-the-bucket process as the other gridded products.

    5. Apply ocean climatology mask
    To eliminate low-latitude spurious ice concentrations, SST filters based on the NOAA monthly ocean atlas are applied.

    6. Apply land spillover mask
    A land spillover correction scheme is applied to correct for erroneous ice concentrations along coast lines.

    For a detailed description of the algorithm processing for this data set, see section 3.2.1.1 of the ATBD document.

    Derivation Techniques for Sea Ice Concentration Difference Grids

    Sea ice concentration grids derived from AMSR2 L1R Tbs using the NT2 algorithm (AU_SI12) are compared with sea ice concentration grids derived from AMSR2 L1R Tbs using the Bootstrap algorithm. The sea ice concentration difference between the NT2 algorithm and Bootstrap algorithm is calculated. The results of this calculation are used to generate sea ice concentration difference grids. The Bootstrap algorithm, as implemented for the AMSR2 JAXA sea ice concentration product is similar to the Bootstrap algorithm implementation for the AMSR2 NASA sea ice concentration product. For a detailed description of the Bootstrap algorithm see the JAXA AMSR2 Level 1R and Level 2 Algorithms document.

    Figure 9. This figure shows the source and algorithms used to create AMSR2 12.5 km sea ice concentration difference grids.

    Derivation Techniques for Snow Depth on Sea Ice

    Snow depth over sea ice is reported as a 5-day running average, which is based on the current day and the previous four days. The 12.5 km snow depth on sea ice parameter is generated using the AMSR-E snow-depth-on-sea-ice algorithm described by Markus and Cavalieri (1998) for both the Arctic and the Antarctic. This algorithm is only valid over seasonal ice.

    Algorithm

    The snow depth on sea ice is calculated using the spectral gradient ratio from the 18.7 GHz and 36.5 GHz vertical polarization channels:

    Equation 3. Snow Depth on Sea Ice

    where hs is the snow depth in meters, a1 and a2 are coefficients derived from the linear regression of in situ snow depth measurements on microwave data (a1 = 2.9, a2 = -782), and GRV(ice) is the spectral gradient ratio corrected for the sea ice concentration, C, as follows:

    Equation 4.  Gradient Ratio corrected for sea ice concentration with surface effects

    where k1=Tbow(37V)-Tbow(19V) and k2=Tbow(37V)+Tbow(19V).

    The open water brightness temperatures, Tbow, are average values from open ocean areas and are used as constants. The principal idea of the algorithm (Kelly et al. 2003) is based on the assumptions that scattering increases with increasing snow depth and that the scattering efficiency is greater at 37 GHz than at 19 GHz. For snow-free sea ice, the gradient ratio is close to zero, and it becomes more negative as the snow depth and grain size increases. The correlation of regional in situ snow depth distributions and satellite-derived snow depth distributions is 0.81. The upper limit for snow depth retrievals is 50 cm, which is a result of the limited penetration depth at 18.7 GHz and 36.5 GHz.

    The algorithm is applicable to dry snow conditions only. At the onset of melt, the emissivity of both the 18.7 GHz and the 36.5 GHz channels approach unity; and the gradient ratio approaches zero before becoming positive. Thus, snow depth is indeterminate under wet snow conditions. Snow, which is wet during the day, frequently refreezes during the night. This refreezing results in very large grain sizes (Colbeck 1982), which leads to a reduced emissivity at 36.5 GHz relative to 18.7 GHz, thereby decreasing GRV(ice) and thus leading to an overestimate of snow depth. These thaw-freeze events cause large temporal variations in the snow depth retrievals. This temporal information is used in the algorithm to flag the snow depths as unretrievable from those periods with large fluctuations.

    As in situ grain size measurements are even less frequently collected than snow depth measurements, the influence of grain size variations could not be incorporated into the algorithm. Because of the uncertainties in grain size, density variations, and sporadic weather effects, AMSR2 daily snow depth products are five-day running averages similar to the AMSR-E snow depth on land product.

    Snow depths are retrieved for the entire Southern Ocean, but only for the seasonal sea ice zones in the Arctic. This is due to the fact that Arctic snow depth is complicated by the presence of multiyear ice, which has a signature similar to snow cover on first-year ice. Both multiyear ice and deep snow on top of first-year ice results in increasingly negative values for the spectral gradient ratio. Therefore, the algorithm only retrieves snow depth in the seasonal sea ice zones and in regions where the value of GR(37V19V) is greater than -0.02. This threshold corresponds to multiyear ice concentration of less than 20%, where GR(37V19V) is less than the -0.02 threshold used by the algorithm to flag pixels as multiyear ice. Due to the higher sensitivity of snow depth retrievals to ice concentration less than 20%, the algorithm limits snow depth retrievals to ice concentration between 20% -100%. Ice concentrations less than 20% appear almost exclusively near the ice edge.

    Processing

    Figure 10 summarizes the processing sequence for the snow-depth-on-sea-ice algorithm.

    Figure 10. This figure shows the snow depth processing sequence from the NASA Team Algorithm.

    Derivation Techniques for Sea Ice Motion

    Sea ice motion is produced daily beginning with day two of the data set. The parameter is derived by comparing L3 Northern Hemisphere 36V GHz Tbs for the current processing run with L3 Northern Hemisphere 36V GHz Tbs from the the previous processing run, which are typically consecutive calendar days.

      Algorithm

      The method to estimate sea ice motion is the Maximum Cross-Correlation feature-tracking algorithm developed at the University of Colorado (Emery et al. 1995). The methodology of the algorithm is fairly simple. Two spatially coincident images, separated by some period of time, are compared. A target area, which may be defined by a pixel or a group of pixels, is chosen in the first (older) image. Then, a search area surrounding the target area is chosen in the second (newer) image. Correlations with the target area in the first image are compared with all regions in the search area of the second image. The region with the highest correlation is determined to be the location where the target moved.

      Figure 11. This figure shows the search and target areas for the correlation. The inside box is the target window and d is the maximum distance in pixels that the ice is expected to move.

      Processing

      Ice movements are calculated from two composite images using the sliding window to find the correlation peak, which determines the distance a feature has moved. The drift is then computed by dividing the distance by the time separation.  A sea ice mask is applied to retrieve ice motion where concentration is above the standard sea ice extent threshold of 15% concentration. False correlations can occur due to clouds or variability of ice surface features. To eliminate false correlations, a minimum correlation threshold of 0.7 is applied and a post-processing filter program is run to remove at least some questionable and erroneous motions. This is based on the fact that motion is spatially correlated and requires that each vector be reasonably consistent in speed and direction with at least two neighboring motion estimates.

      Figure 12. This figure includes two successive images showing the location of a sub-window in the first image (solid box) and the location of the best match in the second image (solid box). The best match is found by computing the cross correlation between the sub-window from the first image and each sub-window-sized section within the search area in the second image (dashed box) and choosing the position with highest cross-correlation value. The drift is then computed by dividing the distance by the time separation.

      Quality

      Assessment

      Each HDF-EOS5 data file contains core metadata with Quality Assessment (QA) metadata flags that are set by the operational processing code run by the AMSR SIPS prior to delivery to NSIDC. A separate metadata file in XML format is also delivered to NSIDC with the HDF-EOS file. This file contains the same quality assessment (QA) metadata flags as the core metadata contained in the HDF-EOS file. Three levels of QA are applied to AMSR2 files: automatic, operational, and science.  Please note that if a granule passes automatic QA and operational QA, the granule is forwarded to NSIDC for archive and distribution. If not, the issue is resolved and the granule is reprocessed.  Science QA is performed automatically during nominal processing but only reviewed closely after the fact in conjunction with questions that arise after processing is complete.

      Automatic QA

      Out-of-bounds Level-1R Tbs are screened out before Tbs  are interpolated to the 12.5 km grid.

      Operational QA

      AMSR2 L1R data are subject to operational QA by JAXA prior to arriving at the AMSR SIPS for processing to higher-level products. Operational QA varies by product, but it typically checks for the following criteria in 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

      In the SIPS environment, as part of the processing code, the science QA includes checking the maximum and minimum variable values, percent of missing data, and out-of-bounds data per variable value. At the Science Computing Facility (SCF), co-located with the SIPS, post-processing science QA involves reviewing the operational QA files and browse images and performing the following additional 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

      Several tools have been developed to aid in the QA process of the Level 3 AMSR2 products. The Team Lead SIPS (TLSIPS) provides the browse image software. This creates a QA browse image in Portable Network Graphics (PNG) format available for visual QA.  The Team Lead SCF (TLSCF) provides metadata and QA software specific to each product that provides the metadata files discussed above and a QA summary report in text format. The products of these tools are provided to the NSIDC DAAC along with each data granule.

      Accuracy and Precision

      Refer to the ATBD for information about data used to check the accuracy and precision of AMSR2 observations.

      Anomalies

      Refer to the AMSR2 NRT Anomalies Page for information regarding data anomalies or gaps in coverage. Updates to this page may be forthcoming.

      Instrument Description

      For a detailed a description of the AMSR2 instrument, refer to the AMSR2 Channel Specification and Products page.

      Software and Tools

      For general tools that work with HDF-EOS data, see the NSIDC HDF-EOS web page.

      Contacts and Acknowledgments

      Josefino Comiso, Thorsten Markus
      Cryospheric Sciences Laboratory
      NASA Goddard Space Flight Center

      References

      Meier, W. N., Markus, T., Comisco, J., Ivanoff, I., and J. Miller. 2017. Algorithm Theoretical Basis Document (ATBD): AMSR2 Sea Ice. AMSR2 Project, NASA Goddard Space Flight Center, Greenbelt, MD. (PDF)

      Meier, W. N. and A. Ivanoff. 2017. Intercalibration of AMSR2 NASA Team 2 algorithm sea ice concentrations with AMSR-E slow rotation data. IEEE Journal Special Topics Application Earth Observation & Remote Sensing 10(8). https://doi.org/10.1109/JSTARS.2017.2719624.

      Maeda, T., Taniguchi, Y., and K. Imaoka. 2016. GCOM-W1 AMSR2 Level 1R Product: Dataset of Brightness Temperature Modified Using the Antenna Pattern Matching Technique. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 770-782. https://doi.org/10.1109/TGRS.2015.2465170.

      Markus, T. and D. J. Cavalieri. 2009. The AMSR-E NT2 sea ice concentration algorithm: its basis and implementation. Journal of The Remote Sensing Society of Japan 29(1), 216-225. https://doi.org/10.11440/rssj.29.216.

      Markus, T. and D. J. Cavalieri. 2000. An Enhancement of the NASA Team Sea Ice Algorithm. IEEE Transactions on Geoscience and Remote Sensing 38, 1387-1398. https://doi.org/10.1109/36.843033.

      Markus, T. and D. J. Cavalieri. 1998. Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data. Antarctic Sea Ice Physical Processes, Interactions and Variability 19-39. Washington, DC. American Geophysical Union.

      Comiso, J. C. 2009. Enhanced sea ice concentration and ice extent from AMSR-E Data. Journal Remote Sensing Society of Japan 29(1), 199-215. https://doi.org/10.11440/rssj.29.199.

      Comiso, J. C., Cavalieri, D. J., and T Markus. 2003. Sea Ice Concentration, Ice Temperature, and Snow Depth using AMSR-E data. IEEE Transactions on Geoscience and Remote Sensing 41(2), 243-252. https://doi.org/10.1109/TGRS.2002.808317.

      Kelly, R. E., Chang, A. T., Tsang, L., 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. https://ieeexplore.ieee.org/document/1196041.

      Comiso, J. C., Cavalieri, D. J., Parkinson, C. L., and P. Gloersen. 1997. Passive microwave algorithms for sea ice concentration - A comparison of two techniques. Remote Sensing of Environment 60(3), 357-384. https://doi.org/10.1016/S0034-4257(96)00220-9.

      Conway, D. 2002. Advanced Microwave Scanning Radiometer - EOS Quality Assurance Plan. Huntsville, AL. Global Hydrology and Climate Center.

      Wilkin, J. L., Bowen, M. M., and W. J. Emery. 2002. Mapping mesoscale currents by optimal interpolation of satellite radiometer and altimeter data. Ocean Dynamics 52, 95103.

      Cavalieri et al. 1995. Reduction of Weather Effects in the Calculation of Sea Ice Concentration with the DMSP SSM/I. Journal of Glaciology 41(139), 455-464. https://doi.org/10.3189/S002214300003479.

      Emery, W., Fowler, C., and J. Maslanik. 1995. Satellite Remote Sensing of Ice Motion. Oceanographic Applications of Remote Sensing. Boca Raton: CRC Press.

      Kummerow, C. 1993. On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. Journal Geophysical Research 98(D2), 2757-2765. https://doi.org/10.1029/92JD02472.

      Eppler, et al. 1992. Passive Microwave Signatures of Sea Ice in Microwave Remote Sensing of Ice. Geophysical Monograph 68, 47-71. https://doi.org/10.1029/GM068p0047.

      Gloersen, P. and D. J. Cavalieri. 1986. Reduction of Weather Effects in the Calculation of Sea Ice Concentration from Microwave Radiances. Journal of Geophysical Research 91(C3), 3913-3919. https://doi.org/10.1029/JC091iC03p03913.

      Matzler, C., Ramseier, R. O., and E. Svendsen. 1984. Polarization effects in sea ice signatures. IEEE Journal. Oceanic Eng 9, 333-338.

      Fraser et al. 1975. Interaction Mechanisms Within the Atmosphere Including the Manual of Remote Sensing. American Society of Photogrammetry 181-233. Falls Church, VA.

      Backus, G. E. and J. F. Gilbert. 1967. Numerical Applications of a Formalism for Geophysical Inverse Problems. Geophysical Journal International 13(1-3), 247–276. https://doi.org/10.1111/j.1365-246X.1967.tb02159.x.

      How To

      Programmatically access data using spatial and temporal filters
      The Common Metadata Repository (CMR) is a high-performance metadata system that provides search capabilities for data at NSIDC. A synchronous REST interface was developed which utilizes the CMR API, allowing you to ... read more