Data Set ID:
SPL3SMA

SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture, Version 3

This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

This is the most recent version of these data.

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):
  • Radar > Radar Backscatter > Sigma Nought
  • Soils > Soil Moisture/Water Content > Soil Moisture
Data Format(s):
  • HDF5
Spatial Coverage:
N: 85.044, 
S: -85.044, 
E: 180, 
W: -180
Platform(s):SMAP Observatory
Spatial Resolution:
  • 3 km x 3 km
Sensor(s):SMAP L-Band Radar
Temporal Coverage:
  • 13 April 2015 to 7 July 2015
Version(s):V3
Temporal Resolution1 dayMetadata XML:View Metadata Record
Data Contributor(s):Kim, S., J. Van Zyl, R. S. Dunbar, E. G. Njoku, J. T. Johnson, M. Moghaddam, and L. Tsang.

Geographic Coverage

Other Access Options

Other Access Options

Close

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.

Kim, S., J. Van Zyl, R. S. Dunbar, E. G. Njoku, J. T. Johnson, M. Moghaddam, and L. Tsang. 2016. SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture, Version 3. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/IGQNPB6183ZX. [Date Accessed].
Created: 
7 January 2019
Last modified: 
7 November 2019

Data Description

Parameters

Surface soil moisture (0-5 cm) in cm3/cm3 derived from sigma nought measurements is output on a fixed 3 km EASE-Grid 2.0.

Sigma nought (sigma0), or the backscatter coefficient, is a measure of the strength of radar signals reflected back to the instrument from a target, and is defined as per unit area on the ground. Usually expressed in dB, it is a normalized dimensionless number, comparing the strength observed to that expected from a defined area. The SMAP L-band Radar measures sigma0 using VV, HH, and HV transmit-receive polarizations, and uses separate transmit frequencies for the H (1.26 GHz) and V (1.29 GHz) polarizations. Sigma0 measurements are derived using Synthetic-Aperture Radar (SAR) processing.

Refer to the Appendix of this document for details on all parameters.

File Information

Format

Data are in HDF5 format. For software and more information, including an HDF5 tutorial, visit the HDF Group's HDF5 Web site.

File Contents

As shown in Figure 1, each HDF5 file is organized into the following main groups, which contain additional groups and/or data sets:

Figure 1. Subset of File Contents
For a complete list of file contents for the SMAP Level-3 radar soil moisture product, refer to the Appendix. 

Data Fields

Each file contains the main data groups summarized in this section. For a complete list and description of all data fields within these groups, refer to the Appendix of this document.

All data element arrays are one-dimensional with a size N, where N is the number of valid cells from the radar swath that appear on the grid.

Ancillary Data

Includes all ancillary data, such as surface temperature and vegetation water content.

Radar Data

Includes all radar data, such as cross-polarized sigma nought (also referred to as sigma0 or σ0) data.

Soil Moisture Retrieval Data

Includes soil moisture data and quality assessment flags.

Metadata Fields

Includes all metadata that describe the full content of each file. For a description of all metadata fields for this product, refer to the Product Specification Document.

File Naming Convention

Files are named according to the following convention, which is described in Table 1:

SMAP_L3_SM_A_yyyymmdd_RLVvvv_NNN.[ext]

For example:

SMAP_L3_SM_A_20141225_R12130_002.h5

Where:

Table 1. File Naming Conventions
Variable Description
SMAP Indicates SMAP mission data
L3_SM_A Indicates specific product (L3: Level-3; SM: Soil Moisture; A: Active)
yyyymmdd 4-digit year, 2-digit month, 2-digit day; date in Universal Coordinated Time (UTC) of the first data element that appears in the product.
RLVvvv Composite Release ID, where:
R Release
L Launch Indicator (1: Post-launch standard data)
V 1-Digit Major Version Number
vvv 3-Digit Minor Version Number
Example: R12130 indicates a standard data product with a version of 2.130. Refer to the SMAP Data Versions page for version information.
NNN Number of times the file was generated under the same version for a particular date/time interval (002: 2nd time)
.[ext] File extensions include:
.h5 HDF5 data file
.qa Quality Assurance file
.xml XML Metadata file

File Size

Each file is approximately 765 MB.

File Volume

The daily data volume is approximately 765 MB.

Spatial Information

Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N and 85.044°S. The gap in coverage at both the North and South Pole, called a pole hole, has a radius of approximately 400 km. The swath width is 1000 km, enabling nearly global coverage every three days.

Resolution

The native spatial resolution of the radar footprint is 1 km. Data are then gridded using the 3 km EASE-Grid 2.0 projection.

EASE-Grid 2.0

These data are provided on the global cylindrical EASE-Grid 2.0 (Brodzik et al. 2012). Each grid cell has a nominal area of approximately 3 x 3 km2 regardless of longitude and latitude. Using this projection, all global data arrays have dimensions of 4872 rows and 11568 columns.

EASE-Grid 2.0 has a flexible formulation. By adjusting a single scaling parameter, a family of multi-resolution grids that nest within one another can be generated. The nesting can be adjusted so that smaller grid cells can be tessellated to form larger grid cells. Figure 2 shows a schematic of the nesting.

This feature of perfect nesting provides SMAP data products with a convenient common projection for both high-resolution radar observations and low-resolution radiometer observations, as well as for their derived geophysical products.

For more on EASE-Grid 2.0, refer to the EASE-Grid 2.0 Format Description.

Perfect Nesting in EASE-Grid 2.0
Figure 2. Perfect Nesting in EASE-Grid 2.0

Temporal Information

Coverage

Coverage spans from 13 April 2015 to 07 July 2015.

Note: Temporal coverage for this data set is limited due to the premature failure of the SMAP L-Band Radar. On 07 July 2015, the radar stopped transmitting due to an anomaly involving the instrument's high-power amplifier (HPA). For details, refer to the SMAP News Release issued 02 September 2015 by the Jet Propulsion Laboratory (JPL).

Satellite and Processing Events

Due to instrument maneuvers, data downlink anomalies, data quality screening, and other factors, small gaps in the SMAP time series will occur. Details of these events are maintained on two master lists:

SMAP On-Orbit Events List for Instrument Data Users
Master List of Bad and Missing Data

Resolution

Each Level-3 file is a daily composite of half-orbit files/swaths.

Data Acquisition and Processing

Background

Retrieval of soil moisture from measured backscatter data typically implies an inversion of the radar forward scattering process. Bare rough surfaces can be characterized in terms of their Root Mean Square (RMS) roughness height, correlation length, and moisture content (a surrogate for dielectric constant). The use of time-series data makes the retrieval a well-constrained estimation problem, under the assumption of a time invariant surface roughness. By taking a co-polarized ratio the soil moisture retrieval becomes insensitive to the correlation length except for very rough surfaces, which enables an accurate retrieval of soil moisture without correlation length information. This approach has been extended to the vegetated surface by introducing a vegetation axis to the lookup table (Kim et al. 2014). A one-axis representation of the vegetation effect is clearly a simplification, considering that different sets of vegetation parameters result in different backscattering coefficients. However, with SMAP’s three measurement channels—HH, VV, and HV—at most three independent parameters can be uniquely estimated, and therefore simplified forward models must be represented in terms of at most three dominant parameters. The simplification results in some errors in soil moisture retrieval, especially in heavily vegetated areas such as forests. Allometric relationships reduce the number of unknowns and may improve the retrievals. The three parameters used to simplify the scattering model are then the dielectric constant of soil, ε, soil surface roughness, s, and VWC.

The SMAP radar HV-channel measurements are reserved for possible use in correcting vegetation effects. The remaining two co-polarized (co-pol) measurements (HH and VV) are not always sufficient to determine s and ε (Kim et al. 2014). One of the main causes is the ambiguity in bare surface scattering: a wet and smooth surface may have the same backscatter as a dry and moderately rough surface. Very often the timescale of the change in s is longer than that of ε (Jackson et al. 1997). Then s may be constrained to be a constant in time, thus resolving the ambiguity (Kim et al. 2014).

The SMAP baseline approach (Kim et al. 2012; Kim et al. 2014) is a multichannel retrieval algorithm that searches for a soil moisture solution such that the difference between modeled and observed backscatter is minimized in the least squares sense. A look-up table representation, or data cube, of a complicated forward model has been demonstrated to be an accurate and fast tool for retrieval (Kim et al. 2012; Kim et al. 2014). The algorithm estimates s first and then retrieves εr using the estimated s. Vegetation effects are included by selecting the sigma0 of the forward model at the VWC level given by an ancillary source. Note that the VWC provided by ancillary information is allowed to vary throughout the time series.

For in-depth information regarding the physics involved in deriving soil moisture from backscatter, refer to the ATBD for this product, Section 2: Physics of the Scattering Problem.

Acquisition

SMAP Level-3 radar soil moisture data (SPL3SMA) are composited from SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture, Version 3 (SPL2SMA) data.

Derivation Techniques and Algorithms

This SMAP Level-3 radar soil moisture data set is a daily gridded composite of the SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture, Version 3 (SPL2SMA) data set. The derivation of soil moisture from SMAP brightness temperatures occurs in the Level-2 processing of the radar data set.

Please refer to the Derivation Techniques section in the SPL2SMA user guide for details on algorithms and ancillary data.

Processing

The SPL3SMA product is a daily global product. This product is generated by the SMAP Science Data Processing System (SDS) at the Jet Propulsion Laboratory (JPL) in Pasadena, California USA. To generate this product, the processing software ingests one day's worth of SPL2SMA files and creates individual global composites as two-dimensional arrays for each output parameter defined in the SPL2SMA product. Wherever data overlap occurs (typically at high latitudes), data whose acquisition times are closest to the 6:00 a.m. local solar times are chosen. Because the input SPL2SMA files are available only for descending 6:00 a.m. passes, the resulting SPL3SMA files are available only for descending 6:00 a.m. passes.

Quality, Errors, and Limitations

Error Sources

Anthropogenic Radio Frequency Interference (RFI), principally from ground-based surveillance radars, can contaminate both radar and radiometer measurements at L-band. Early measurements and results from ESA's Soil Moisture and Ocean Salinity (SMOS) mission indicate that in some regions RFI is present and detectable. The SMAP radar and radiometer electronics and algorithms include design features to mitigate the effects of RFI. The SMAP radar utilizes selective filters and an adjustable carrier frequency to tune to predetermined RFI-free portions of the spectrum while on orbit.

More information about error sources is provided in Section 3.5: Error Budget of the ATBD (O'Neill et al. 2018).

Quality Assessment

For in-depth details regarding the quality of these Version 3 Validated data, refer to the following reports: 
Validated Assessment Report 
Beta Assessment Report

Quality Overview

SMAP products provide multiple means to assess quality. Each product contains bit flags, uncertainty measures, and file-level metadata that provide quality information. For information regarding the specific bit flags, uncertainty measures, and file-level metadata contained in this product, refer to the Appendix and the Product Specification Document.

Each HDF5 file contains metadata with Quality Assessment (QA) metadata flags that are set by the Science Data Processing System (SDS) at the JPL prior to delivery to NSIDC. A separate metadata file with an .xml file extension is also delivered to NSIDC with the HDF5 file; it contains the same information as the HDF5 file-level metadata.

A separate QA file with a .qa file extension is also associated with each data file. QA files are ASCII text files that contain statistical information in order to help users better assess the quality of the associated data file.

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 product fails QA, it is never delivered to NSIDC DAAC.

Data Flags

Quality flags provide information as to whether the ground is frozen, snow-covered, or flooded, or whether it is actively precipitating at the time of the satellite overpass. Other flags indicate whether masks for steeply sloped topography, or for urban, heavily forested, or permanent snow/ice areas are in effect.

For a description of the data flag types and methods of flagging, refer to the Quality Flags section in the SPL2SMA user guide.

Instrumentation

Description

For a detailed description of the SMAP instrument, visit the SMAP Instrument page at the Jet Propulsion Laboratory (JPL) SMAP Web site.

Software and Tools

For tools that work with SMAP data, refer to the Tools Web page.

Version History

Version Date Version Changes
Version 2
 
November 2015 First public data release
Version 3
 
April 2016 Changes to this version include:
  • Transitioned to Validated-Stage 1
  • Uses SPL2SMA V3 Validated data as input; inherited all changes and improvements of the new version

Related Data Sets

SMAP Data at NSIDC | Overview

SMAP Radar Data at the ASF DAAC

Related Websites

SMAP at NASA JPL

Contacts and Acknowledgments

Investigators

Seungbum Kim, Jakob van Zyl, Roy S. Dunbar, Eni Njoku
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109 USA

Joel Johnson
Ohio State University
Columbus, OH 43210 USA

Mahta Moghaddam
University of Michigan
Ann Arbor, MI 48109 USA

Leung Tsang
University of Washington
Seattle, WA 98195 USA

References

Bolten, J., V. Lakshmi, and E. Njoku. 2003. Soil Moisture Retrieval Using the Passive/Active L- and S-band Radar/Radiometer. IEEE Trans. Geosci. Rem. Sens., 41:2792-2801.

Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie. 2012. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS Int. J. Geo-Inf. 1(1):32-45. http://dx.doi.org/10.3390/ijgi1010032.

Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie. 2014. Correction: Brodzik, M. J. et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS Int. J. Geo-Inf 2012. 1(1):32-45 ISPRS Int. J. Geo-Inf. 3(3):1154-1156. http://dx.doi.org/10.3390/ijgi3031154.

Dunbar, R. S., and S. B. Kim. 2016. SMAP Level 2 Active Soil Moisture (SPL2SMA) Product Specification Document:Revised Release. SMAP Project, JPL D-72550. Jet Propulsion Laboratory, Pasadena, CA. (D-72550_SMAP L3_SM_A PSD_04162016_wo-sigs.pdf, 496 KB)

Dunbar, R. S., and S. B. Kim. 2014. SMAP Level 3 Active Soil Moisture (L3_SM_A) Product Specification Document. SMAP Project, JPL D-72550. Jet Propulsion Laboratory, Pasadena, CA. (D-72550_SMAP L3_SM_A PSD_12082015_wo-sigs.pdf, 476 KB)

Entekhabi, D. et al. 2014. SMAP Handbook–Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw from Space. Pasadena, CA USA: SMAP Project, JPL CL#14-2285, Jet Propulsion Laboratory.

Jackson, T. J., H. McNairn, M. A. Weltz, B. Brisco, and R. Brown. 1997. First Order Surface Roughness Correction of Active Microwave Observations for Estimating Soil Moisture.IEEE Trans. Geosci. Remo te Sens. 35:1065-1069.

Kim, S. B., M. Moghaddam, L. Tsang, M. Burgin, X. Xu, and E. G. Njoku. 2014. Models of L-band radar backscattering coefficients over the global terrain for soil moisture retrieval.IEEE Trans. Geosci. Remote Sens., vol. 52, pp. 1381-1396, 2014.

Kim, S. B., J. Oullette, J. J. van Zyl, and J. T. Johnson, 2015 (In revision). Dual-copolarized approach to detect surface water extent using L-land radar. IEEE Trans. Geosci. Remote Sens.

Kim, S. B., L. Tsang, J. T. Johnson, S. Huang, J. J. van Zyl, and E. G. Njoku, 2012: Soil moisture retrieval using time-series radar observations over bare surfaces. IEEE Trans. Geosci. Remote Sens., 50, 1853-1863.

Kim, S. B., J. van Zyl, S. Dunbar, E. Njoku, J. Johnson, M. Moghaddam, J. Shi, and L. Tsang. 2015. SMAP Algorithm Theoretical Basis Document: L2 & L3 Radar Soil Moisture (Active) Products. SMAP Project, Jet Propulsion Laboratory, Pasadena, CA. (L2&3_SM_A_RevB_web151031.pdf, 2.1 MB)

Kim, S. B., J. van Zyl, S. Dunbar, E. Njoku, J. Johnson, M. Moghaddam, J. Shi, and L. Tsang. 2014. SMAP Algorithm Theoretical Basis Document: L2 & L3 Radar Soil Moisture (Active) Products. SMAP Project, Jet Propulsion Laboratory, Pasadena, CA. (276_L2_3_SM_A_RevA_web.pdf, 5.4 MB)

Kim, S. B., J. van Zul, T. Jackson, and A. Colliander. 2016. Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L2/3_SM_A Validated-Stage 1 Release Data Products (Version 3). SMAP Project, JPL D-93722. Jet Propulsion Laboratory, Pasadena, CA. (L2SMA_Assessment_Report160430.pdf, 964 KB)

Kim, S. B., J. van Zul, T. Jackson, and A. Colliander. 2015. Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L2/3_SM_A Beta-Release Data Products. SMAP Project, JPL D-XXXX. Jet Propulsion Laboratory, Pasadena, CA. (L2L3SMA_Assessment_Report151024.pdf, 673 KB)

Kim, Y., and J. J. van Zyl. 2009. A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data. IEEE Trans. Geosci. Remote Sens. 47:2519-2527.

Kim, Y., and J. J. van Zyl. 2000. On the Relationship Between Polarimetric Parameters. IEEE Geoscience and Remote Sensing Symposium. Hawaii, USA.

Appendix - Data Fields

This Appendix provides a description of all data fields within the SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture product. The data are grouped into four main HDF5 groups:

  • Ancillary_Data
  • Metadata
  • Radar_Data
  • Soil_Moisture_Retrieval_Data

For a description of metadata fields for this product, refer to the Product Specification Document.

Ancillary_Data

Table A1 describes the data fields of a typical SPL3SMA descending half-orbit granule. All data element arrays are one-dimensional with a size N, where N is the number of valid cells from the radar swath that appear on the grid.

Table A1. Data Fields for Ancillary_Data
Data Field Name Shape Concept Byte Signed Unit Min Max Fill/Gap Value
bare_soil_roughness_tabular LatCell_LonCell_Array real 4 N/A meters 0.0 0.1 -9999.0
faraday_rotation_angle LatCell_LonCell_Array real 4 N/A degrees -999999.9 999999.9 -9999.0
landcover_class LatCell_LonCell_Array enum 1 N/A N/A N/A N/A 254
normalized_difference_vegetation_index LatCell_LonCell_Array real 4 N/A normalized -1.0 10.0 -9999.0
static_water_body_fraction LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
surface_temperature LatCell_LonCell_Array real 4 N/A degrees Celsius -50.0 60.0 -9999.0
vegetation_water_content_NDVI LatCell_LonCell_Array real 4 N/A kg/m**3 0.0 10.0 -9999.0
vegetation_water_content_RVI LatCell_LonCell_Array real 4 N/A kg/m**3 0.0 10.0 -9999.0

Radar_Data 

All data element arrays are one-dimensional with a size N, where N is the number of valid cells from the radar swath that appear on the grid (Table A2).

Table A2. Data Fields for Radar_Data
Data Field Name Shape Concept Byte Signed Unit Min Max Fill/Gap Value
altitude_std_dev LatCell_LonCell_Array real 4 N/A meters 0.0 1000.0 -9999.0
cell_radar_mode_flag LatCell_LonCell_Array bit flag 2 N/A N/A N/A N/A 65534
earth_boresight_azimuth_aft LatCell_LonCell_Array real 4 N/A degrees 0.0 360.0 -9999.0
earth_boresight_azimuth_fore LatCell_LonCell_Array real 4 N/A degrees 0.0 360.0 -9999.0
kp_hh LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_hh_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_hh_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_vv LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_vv_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_vv_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_xpol LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_xpol_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
kp_xpol_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 1.0 -9999.0
sigma0_hh_mean LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_hh_mean_aft LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_hh_mean_fore LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_hh_std_dev LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_hh_std_dev_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_hh_std_dev_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_vv_mean LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_vv_mean_aft LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_vv_mean_fore LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_vv_std_dev LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_vv_std_dev_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_vv_std_dev_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_xpol_mean LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_xpol_mean_aft LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_xpol_mean_fore LatCell_LonCell_Array real 4 N/A normalized -0.01 10.0 -9999.0
sigma0_xpol_std_dev LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_xpol_std_dev_aft LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0
sigma0_xpol_std_dev_fore LatCell_LonCell_Array real 4 N/A normalized 0.0 5.0 -9999.0

Soil_Moisture_Retrieval_Data

All data element arrays are one-dimensional with a size N, where N is the number of valid cells from the radar swath that appear on the grid (Table A3).

Table A3. Data Fields for Soil_Moisture_Retrieval_Data
Data Field Name Shape Concept Byte Signed Unit Min Max Fill/Gap Value
EASE_column_index LatCell_LonCell_Array integer 2 false count 0 65535 65534
EASE_row_index LatCell_LonCell_Array integer 2 false count 0 65535 65534
bare_soil_roughness_retrieved LatCell_LonCell_Array real 4 N/A meters 0.0 0.05 -9999.0
distance_from_nadir LatCell_LonCell_Array real 4 N/A meters 0.0 500000.0 -9999.0
latitude LatCell_LonCell_Array real 4 N/A degrees_north -90.0 90.0 N/A
longitude LatCell_LonCell_Array real 4 N/A degrees_east -180.0 180.0 N/A
num_input_sigma0s_hh LatCell_LonCell_Array integer 2 false count 0 100 65534
num_input_sigma0s_vv LatCell_LonCell_Array integer 2 false count 0 100 65534
num_input_sigma0s_xpol LatCell_LonCell_Array integer 2 false count 0 100 65534
num_time_series LatCell_LonCell_Array integer 1 false count 0 255 254
radar_vegetation_index LatCell_LonCell_Array real 4 N/A normalized -999999.9 999999.9 TBD
radar_water_flag3 LatCell_LonCell_Array integer 1 false count 0 255 254
retrieval_qual_flag LatCell_LonCell_Array bit flag 2 N/A N/A N/A N/A 65534
retrieval_qual_flag_change_index EASEGridCell_Array bit flag 2 N/A N/A N/A N/A 65534
retrieval_qual_flag_kvz LatCell_LonCell_Array bit flag 2 N/A N/A N/A N/A 65534
sigma0_qual_flag_hh LatCell_LonCell_Array bit flag 4 N/A N/A N/A N/A 4294967294
sigma0_qual_flag_vv LatCell_LonCell_Array bit flag 4 N/A N/A N/A N/A 4294967294
sigma0_qual_flag_xpol LatCell_LonCell_Array bit flag 4 N/A N/A N/A N/A 4294967294
soil_moisture LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
soil_moisture_change_index LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
soil_moisture_error LatCell_LonCell_Array real 4 N/A cm3/cm3 0.0 0.2 -9999.0
soil_moisture_kvz LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
soil_moisture_snapshot1 LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
soil_moisture_snapshot_DVZ1 LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
soil_moisture_snapshot_shi1 LatCell_LonCell_Array real 4 N/A cm3/cm3 0.02 0.5 -9999.0
spacecraft_overpass_time_seconds LatCell_LonCell_Array real 8 N/A seconds 0.00000 999999.9 -9999.0
spacecraft_overpass_time_utc LatCell_LonCell_Array string 24 N/A N/A N/A N/A N/A
surface_flag LatCell_LonCell_Array bit flag 2 N/A N/A N/A N/A 65534
1Algorithms that are not currently being used for these data include Snapshot, Dubois/van Zyl (DVZ), and Shi. Thus, the data fields corresponding to these algorithms contain a fill value of -9999.0.

Data Field Definitions

bare_soil_roughness_tabular

Measure of soil roughness from tabular source.

faraday_rotation_angle

Faraday rotation angle.

landcover_class

An enumerated type that specifies the predominant surface vegetation found in the grid cell. See Table A4 for landcover classification values.

Table A4. Landcover Classification Values
Value Description
0 Water
1 Evergreen needleleaf forest
2 Evergreen broadleaf forest
3 Deciduous needleleaf forest
4 Deciduous broadleaf forest
5 Mixed forest
6 Closed shrubland
7 Open shrubland
8 Woody savanna
9 Savanna
10 Grassland
11 Mixed forest
12 Closed shrubland
13 Open shrubland
14 Woody savanna
15 Savanna
16 Grassland
>16 TBD
normalized_difference_vegetation_index

Normalized difference vegetation index. A measure of the green character of vegetation. (IR-Red)/(IR+Red).

static_water_body_fraction

The fraction of the area of the 3 km grid cell that is covered by static water based on a Digital Elevation Map.

surface_temperature

Temperature at land surface based on ancillary data.

vegetation_water_content_NDVI

Representative measure of water in the vegetation within the 3 km grid cell based on the normalized difference vegetation index.

vegetation_water_content_RVI

Representative measure of water in the vegetation within the 3 km grid cell based on the radar vegetation index.

altitude_std_dev

The standard deviation of the Earth surface elevation within the 3km cell.

cell_radar_mode_flag

Bit flags that specify modes or conditions of radar instrument operation that impact the data represented in the Level 2 SM A Product. See Table A5 for more infromation.

Table A5. Bit Values and Interpretation
Bit Position Bit Value and Interpretation
0 0 = Radar is operating in transmit-receive mode
1 = Radar is operating in receive only mode
1 Always clear (This bit is used to designate the nadir region in Level 1. It is redundant in Level 2.)
2 0 = Cross polarized data are v-pol transmitted, h-pol received.
1 = Cross polarized data are h-pol transmitted, v-pol received.
3 0 = Cross polarized data are consistent within this EASE grid cell.
1 = Cross polarized data are in transition, some are v-pol transmitted, h-pol received, others are h-pol transmitted, v-pol received.
4-15 Always clear (Bits 5 through 7 are reserved for Radar Level 1C use. Bits 8 through 15 are reserved for Level 2 use.)
earth_boresight_azimuth_aft

Mean direction of the projection of the antenna boresight vector on the Earth's surface relative to North for aft looking sigma0s within 3 km cell. Level 1C azimuth is based on instrument coordinate system, not geographical North.

earth_boresight_azimuth_fore

Mean direction of the projection of the antenna boresight vector on the Earth's surface relative to North for forward looking sigma0s within 3 km cell. Level 1C azimuth is based on instrument coordinate system, not geographical North.

kp_hh

Overall error measure for HH-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_hh_aft

Overall error measure for aft-looking HH-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_hh_fore

Overall error measure for fore-looking HH-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_vv

Overall error measure for VV-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_vv_aft

Overall error measure for aft-looking VV-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_vv_fore

Overall error measure for fore-looking VV-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_xpol

Overall error measure for cross-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_xpol_aft

Overall error measure for aft-looking cross-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

kp_xpol_fore

Overall error measure for fore-looking cross-pol σ0 within the 3 km cell based on Level 1C kp values, includes calibration, RFI and contamination effects.

sigma0_hh_mean

Mean of 1 km instrument resolution HH-pol σ0 in the 3 km Earth grid cell.

sigma0_hh_mean_aft

Mean of 1 km instrument resolution aft-looking HH-pol σ0 in the 3 km Earth grid cell.

sigma0_hh_mean_fore

Mean of 1 km instrument resolution fore-looking HH-pol σ0 in the 3 km Earth grid cell.

sigma0_hh_std_dev

Standard deviation of 1 km instrument resolution HH-pol σ0 in the 3 km Earth grid cell.

sigma0_hh_std_dev_aft

Standard deviation of 1 km instrument resolution aft-looking HH-pol σ0 in the 3 km Earth grid cell.

sigma0_hh_std_dev_fore

Standard deviation of 1 km instrument resolution fore-looking HH-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_mean

Mean of 1 km instrument resolution VV-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_mean_aft

Mean of 1 km instrument resolution aft-looking VV-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_mean_fore

Mean of 1 km instrument resolution fore-looking VV-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_std_dev

Standard deviation of 1 km instrument resolution VV-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_std_dev_aft

Standard deviation of 1 km instrument resolution aft-looking VV-pol σ0 in the 3 km Earth grid cell.

sigma0_vv_std_dev_fore

Standard deviation of 1 km instrument resolution fore-looking VV-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_mean

Mean of 1 km instrument resolution cross-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_mean_aft

Mean of 1 km instrument resolution aft-looking cross-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_mean_fore

Mean of 1 km instrument resolution fore-looking cross-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_std_dev

Standard deviation of 1 km instrument resolution cross-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_std_dev_aft

Standard deviation of 1 km instrument resolution aft-looking cross-pol σ0 in the 3 km Earth grid cell.

sigma0_xpol_std_dev_fore

Standard deviation of 1 km instrument resolution fore-looking cross-pol σ0 in the 3 km Earth grid cell.

EASE_column_index

EASE grid column index of cell on world grid in longitude direction.

EASE_row_index

EASE grid row index of cell on world grid in latitude direction.

bare_soil_roughness_retrieved

Bare soil roughness measure retrieved using the active soil moisture algorithm.

distance_from_nadir

The distance from the center of the 3 km EASE grid cell to the spacecraft's sub-nadir track on the Earth's surface.

latitude

Latitude of the center of the Earth based grid cell.

longitude

Longitude of the center of the Earth based grid cell.

num_input_sigma0s_hh

Total number of horizontal polarization sigma0s from the Level 1C product that were used for retrievals in an EASE grid cell.

num_input_sigma0s_vv

Total number of vertical polarization sigma0s from the Level 1C product that were used for retrievals in an EASE grid cell.

num_input_sigma0s_xpol

Total number of cross-polarization sigma0s from the Level 1C product that were used for retrievals in an EASE grid cell.

num_time_series

The number of time-series data used to retrieve soil moisture in the corresponding grid cell.

radar_vegetation_index

Vegetation index derived from radar backscatter.

radar_water_flag3

Radar waterbody flag at 3km.

retrieval_qual_flag

Bit flags that record the conditions and the quality of the soil moisture and freeze-thaw retrieval for the grid cell. See Table A6.

Table A6. Quality Bit Flag Definitions
Name Bit Position Interpretation of Values (0:off, 1:on)
Retrieval recommended flag 0 0: Use of the soil moisture value retrieved for this pixel is recommended.
1: Use of soil moisture value retrieved for this pixel is not recommended.
Retrieval attempted flag 1 0: The algorithm attempted to retrieve soil moisture for this grid cell.
1: The algorithm did not attempt to retrieve soil moisture for this grid cell.
Retrieval success flag 2 0: Retrieval for this algorithm was successfully executed or the algorithm was not attempted.
1: The retrieval for this algorithm was attempted but failed.
Radar water body detection success flag 3 0: Radar water body detection ran successfully.
1: Unable to detect water bodies using retrieval techniques based on radar.
Freeze-thaw retrieval success flag 4 0: Freeze-thaw retrieval ran succesfully.
1: Unable to ascertain freeze-thaw conditons.
Radar vegetation index retrieval success flag 5 0: Radar vegetation index retrieval ran successfully.
1: Radar vegetation index retrieval unsuccessful.
retrieval_qual_flag_change_index

Bit flags that record the conditions and the quality of the soil moisture and freeze-thaw retrieval for the grid cell. See Table A5.

retrieval_qual_flag_cube

Bit flags that record the conditions and the quality of the soil moisture and freeze-thaw retrieval for the grid cell. See Table A5.

retrieval_qual_flag_kvz

Bit flags that record the conditions and the quality of the soil moisture and freeze-thaw retrieval for the grid cell. See Table A5.

sigma0_qual_flag_hh

Representative quality flags of horizontal polarization sigma0 measures in the grid cell. See Table A7.

Table A7. Sigma Quality Bit Flag Definitions
Name Bit Position Description of Values (0: off, 1: on)
Mean horizontal polarization quality flag 0 0: The mean of the forward looking and aft looking horizontal polarization sigma0s has acceptable quality.
1: The mean of the forward looking and aft looking horizontal polarization sigma0s does not have acceptable quality.
Forward looking horizontal polarization quality flag 1 0: The forward looking horizontal polarization sigma0 has acceptable quality.
1: The forward looking horizontal polarization sigma0 has questionable or poor quality.
Aft looking horizontal polarization quality flag 2 0: The aft looking horizontal polarization sigma0 has acceptable quality.
1: The aft looking horizontal polarization sigma0 has questionable or poor quality.
Mean horizontal polarization range flag 3 0: The mean of the forward looking and aft looking horizontal polarization sigma0s falls within the expected range.
1: The mean of the forward looking and aft looking horizontal polarization sigma0s is out of range.
Forward looking horizontal polarization range flag 4 0: The forward looking horizontal polarization sigma0 falls within the expected range.
1: The forward looking horizontal polarization sigma0 is out of range.
Aft looking horizontal polarization range flag 5 0: The aft looking horizontal polarization sigma0 falls within the expected range.
1: The aft looking horizontal polarization sigma0 is out of range.
Mean horizontal polarization RFI clean flag 6 0: Insignificant RFI detected in the mean of the forward looking and aft looking horizontal polarization sigma0s.
1: RFI level is unsuitably high for the mean of the forward looking and aft looking horizontal polarization sigma0s.
Mean horizontal polarization RFI repair flag 7 0: Some components of the mean of the forward looking and aft looking horizontal polarization sigma0s are based on repairs for RFI contamination.
1: Unable to repair the mean of the forward looking and aft looking horizontal polarization sigma0s for RFI contamination.
Forward looking horizontal polarization RFI clean flag 8 0: Insignificant RFI detected in the forward looking horizontal polarization sigma0s.
1: RFI level is unsuitably high for the forward looking horizontal polarization sigma0s.
Forward looking horizontal polarization RFI repair flag 9 0: At least one of the input forward looking horizontal polarization sigma0s is based on repairs for RFI contamination.
1: Unable to repair the forward looking horizontal polarization sigma0s for RFI contamination.
Aft looking horizontal polarization RFI clean flag 10 0: Insignificant RFI detected in the aft looking horizontal polarization sigma0s.
1: RFI level is unsuitably high for the aft looking horizontal polarization sigma0s.
Aft looking horizontal polarization RFI repair flag 11 0: At least one of the input aft looking horizontal polarization sigma0s is based on repairs for RFI contamination.
1: Unable to repair the aft looking horizontal polarization sigma0s for RFI contamination.
Mean horizontal polarization Faraday Rotation Flag 12 0: Faraday Rotation has little or no impact on the mean of the forward looking and aft looking horizontal polarization sigma0s.
1: Faraday Rotation has significant impact on the mean of the forward looking and aft looking horizontal polarization sigma0s.
Forward looking horizontal polarization Faraday Rotation Flag 13 0: Faraday Rotation has little or no impact on the forward looking horizontal polarization sigma0.
1: Faraday Rotation has significant impact on the forward looking horizontal polarization sigma0.
Aft looking horizontal polarization Faraday Rotation Flag 14 0: Faraday Rotation has little or no impact on the aft looking horizontal polarization sigma0.
1: Faraday Rotation has significant impact on the aft looking horizontal polarization sigma0.
Mean horizontal polarization Kp flag 15 0: Kp for the mean of the forward and aft looking horizontal polarization sigma0s is acceptably low.
1: Kp for the mean of forward and aft looking horizontal polarization sigma0s is unacceptably high.
Forward looking horizontal polarization Kp flag 16 0: Kp for the forward looking horizontal polarization sigma0 is acceptably low.
1: Kp for the forward looking horizontal polarization sigma0 is unacceptably high.
Aft looking horizontal polarization Kp flag 17 0: Kp for the aft looking horizontal polarization sigma0 is acceptably low.
1: Kp for the aft looking horizontal polarization sigma0 is unacceptably high.
sigma0_qual_flag_vv

Representative quality flags of vertical polarization sigma0 measures in the grid cell. See Table A7.

sigma0_qual_flag_xpol

Representative quality flags of cross polarization sigma0 measures in the grid cell. See Table A7.

soil_moisture

Retrieved soil moisture for the Earth based grid cell retrieved using the time series algorithm.

soil_moisture_change_index

Retrieved normalized change in soil moisture.

soil_moisture_error

Net uncertainty measure of soil moisture measure for the Earth based grid cell. Calculation method is TBD. May be replaced by other quality indicators.

soil_moisture_kvz

Retrieved soil moisture for the Earth based grid cell retrieved using the Kim/van Zyl time series algorithm.

soil_moisture_snapshot

Representative soil moisture measurement for the Earth based grid cell, retrieved using the snapshot algiorithm.

soil_moisture_snapshot_DVZ

Retrieved soil moisture for the Earth based grid cell, retrieved using the Dubois/van Zyl snapshot algorithm.

soil_moisture_snapshot_shi

Retrieved soil moisture for the Earth based grid cell, retrieved using the Shi snapshot algorithm.

soil_moisture_time_series

Retrieved soil moisture for the Earth based grid cell retrieved using the time series algorithm.

spacecraft_overpass_time_seconds

Number of seconds since a specified epoch (J2000) that represents the spacecraft overpass relative to ground swath.

spacecraft_overpass_time_utc

Time of spacecraft overpass relative to ground swath in UTC.

surface_flag

Bit flags that record ambient surface conditions for the grid cell. See Table A8.

Table A8. Surface Quality Bit Flag Definitions
Name Bit Position Description of Values (0: off, 1: on)
3 km static water body flag 0 0: The fraction of the 3 km grid cell area that is over a permanent water body is less than metadata element PermanentWaterBodyThreshold. Determined by DEM.
1: The fraction of the 3 km grid cell area that is over a permanent water body is greater than or equal to metadata element PermanentWaterBodyThreshold. Determined by DEM.
3 km radar water body detection flag 1 0: Radar retrieval algorithm did not detect significant surface water within the 3 km grid cell.
1: Radar retrieval algorithm detected significant surface water within the 3 km grid cell.
3 km urban area flag 2 0: The fraction of the 3 km grid cell area that is over urban development is less than metadata element UrbanAreaThreshold.
1: The fraction of the 3 km grid cell area that is over urban development is greater than or equal to metadata element UrbanAreaThreshold.
3 km precipitation flag 3 0: No precipitation detected within the 3 km grid cell when data were being acquired.
1: Precipitation detected within the 3 km grid cell when data were being acquired
3 km snow or ice flag 4 0: No snow or ice detected within the 3 km grid cell.
1: Snow and/or ice were detected within the 3 km grid cell.
3 km permanent snow or ice flag 5 0: The fraction of the 3 km grid cell area that is over permanent snow or ice is less than a specified algorithmic threshold.
1: The fraction of the 3 km grid cell area that is over permanent snow or ice is greater than or equal to a specified algorithmic threshold.
3 km frozen ground flag 6 0: No frozen ground detected within the 3 km grid cell.
1: Frozen ground detected within the 3 km grid cell.
3 km mountainous terrain flag 7 0: The variability of land elevation in the 3 km grid cell is less than metadata element MountainousTerrainThreshold.
1: The variability of land elevation in the 3 km grid cell is greater than or equal to metadata element MountainousTerrainThreshold.
3 km dense vegetation flag 8 0: The vegetation density within the 3 km grid cell is less than metadata element DenseVegetationThreshold.
1: The vegetation density within the 3 km grid cell area is greater than or equal to metadata element DenseVegetationThreshold.
3 km nadir region flag 9 0: Data within the the grid cell were not acquired in the nadir region of the swath where sigma0s may not meet the 3 km resolution requirement.
1: A significant fraction (TBD) of the 3 km grid cell data were acquired within the nadir region of the swath where sigma0s may not meet the 3 km resolution requirement.
3 km coastal mask flag 10 0: Data within the the grid cell were not acquired in the coastal region of the large water bodies where especially brightness temperature on land may get severly contaminated due to presense of water.
1: Data within the the grid cell were acquired in the coastal region of the large water bodies where especially brightness temperature on land may get severly contaminated due to presense of water.

Fill/Gap Values 

SMAP data products employ fill and gap values to indicate when no valid data appear in a particular data element. Fill values ensure that data elements retain the correct shape. Gap values locate portions of a data stream that do not appear in the output data file.

Fill values appear in the SMAP SPL3SMA Product when the SPL3SMA SPS can process some, but not all, of the input data for a particular swath grid cell. Fill data may appear in the product in any of the following circumstances:

  • One of Science Production Software (SPS) executables that generate the SMAP SPL3SMA Product is unable to calculate a particular science or engineering data value. The algorithm encounters an error. The error disables generation of valid output. The SPS reports a fill value instead.
  • Some of the required science or engineering algorithmic input are missing. Data over the region that contributes to particular grid cell may appear in only some of the input data streams. Since data are valuable, the SPL3SMA Product records any outcome that can be calculated with the available input. Missing data appear as fill values.
  • Non-essential information is missing from the input data stream. The lack of non-essential information does not impair the algorithm from generating needed output. The missing data appear as fill values.
  • Fill values appear in the input radiometer L1B_TB product. If only some of the input that contributes to a particular grid cell is fill data, the Level SPL3SMA SPS will most likely be able to generate some output. However, some portion of the SPL3SMA output for that grid cell may appear as fill values.

SMAP data products employ a specific set of data values to connote that an element is fill. The selected values that represent fill are dependent on the data type.

No valid value in the SPL3SMA product is equal to the values that represent fill. If any exceptions should exist in the future, the SPL3SMA content will provide a means for users to discern between elements that contain fill and elements that contain genuine data values. This document will also contain a description of the method used to ascertain which elements are fill and which elements are genuine.

The SPL3SMA product records gaps when entire frames within the time span of a particular data granule do not appear. Gaps can occur under one of two conditions:

  • One or more complete frames of data are missing from all data streams.
  • The subset of input data that is available for a particular frame is not sufficient to process any frame output.

The Level 3_SM_A Product records gaps in the product level metadata. The following conditions will indicate that no gaps appear in the data product:

  • Only one instance of the attributes Extent/rangeBeginningDateTime and Extent/rangeEndingDateTime will appear in the product metadata.
  • The character string stored in metadata element Extent/rangeBeginningDateTime will match the character string stored in metadata element OrbitMeasuredLocation/halfOrbitStartDateTime.
  • The character string stored in metadata element Extent/rangeEndingDateTime will match the character string stored in metadata element OrbitMeasuredLocation/halfOrbitStopDateTime.

One of two conditions will indicate that gaps appear in the data product:

  • The time period covered between Extent/rangeBeginningDateTime and Extent/RangeEndingDateTime does not cover the entire half orbit as specified in OrbitMeasuredLocation/halfOrbitStartDateTime and OrbitMeasuredLocation/halfOrbitStartDateTime.
  • More than one pair of Extent/rangeBeginningDateTime and Extent/rangeEndingDateTime appears in the data product. Time periods within the time span of the half orbit that do not fall within the sets of Extent/rangeBeginningDateTime and Extent/rangeEndingDateTime constitute data gaps.

Notations

Table A9 lists the notations used in this document.

Table A9. Notation Definitions
Notation Definition
Int8 8-bit (1-byte) signed integer
Int16 16-bit (2-byte) signed integer
Int32 32-bit (4-byte) signed integer
Uint8 8-bit (1-byte) unsigned integer
Uint16 16-bit (2-byte) unsigned integer
Float32 32-bit (4-byte) floating-point integer
Float64 64-bit (8-byte) floating-point integer
Char 8-bit character
H-pol Horizontally polarized
V-pol Vertically polarized

How To

Programmatically access to data with services such as subsetting, reformatting, and reprojection
This article provides a step-by-step getting started guide to utilizing an Application Programming Interface, or API, for programmatic access to data from the NSIDC Distributed Active Archive Center (DAAC) based on spatial and temporal filters, as well as subsetting, reformatting, and... read more
How to import and geolocate SMAP Level-3 and Level-4 data in ENVI
The following are instructions on how to import and geolocate SMAP Level-3 Radiometer Soil Moisture HDF5 data in ENVI. Testing notes Software: ENVI Software version: 5.3 Platform: Windows 7 Data set: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil... read more
How do I visualize SMAP data in Worldview?
This video tutorial provides step-by-step instructions on how to visualize SMAP data in Worldview (http://worldview.earthdata.nasa.gov/). NASA Worldview is a map-based application... read more
How do I search, order, and customize SMAP data using Earthdata Search?
In this step-by-step tutorial, we will demonstrate how to search, order, and customize NASA Soil Moisture Active Passive, or SMAP data using the NASA Earthdata Search application. NASA Earthdata search provides an interactive map-based search environment where you can filter your results based on... read more
How to Import SMAP HDF Data Into ArcGIS
Selected SMAP L4, Version 4 HDF data (SPL4SMAU, SPL4SMGP, & SPL4SMLM) can be added to ArcGIS with a simple drag/drop or using the 'Add Data' function. These data can be imported and visualized but not geolocated. In order to import, project, and scale these data and other SMAP L3 and L4 HDF... read more
How do I access data using OPeNDAP?
Data can be programmatically accessed using NSIDC’s OPeNDAP Hyrax server, allowing you to reformat and subset data based on parameter and array index. For more information on OPeNDAP, including supported data sets and known issues, please see our OPeNDAP documentation: ... read more
How to learn more about SMAP ancillary data
SMAP Ancillary data sets are used to produce SMAP Level-1, -2, -3, and -4 standard data products. Several of these ancillary data sets are produced by external organizations, such as NOAA, the NASA Global Modeling and Assimilation... read more
How to visualize SMAP WMS layers with ArcGIS and Google Earth
NASA's Global Imagery Browse Services (GIBS) provides up to date, full resolution imagery for selected SMAP data sets. Adding GIBS layers via OGC methods, such as Web Map Service (WMS), Web Map Tile Service (WMTS) and Tiled Web Map Service (TWMS) provides an easy way to visualize the entire time... read more

FAQ

What data subsetting, reformatting, and reprojection services are available for SMAP data?
The following table describes the data subsetting, reformatting, and reprojection services that are currently available for SMAP data via the NASA Earthdata Search tool and a Data Subscription... read more