Data Set ID:

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

  • 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
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


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: [Date Accessed].
7 January 2019
Last modified: 
21 February 2019

Data Description


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 Data Fields document for details on all parameters.

File Information


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 Data Fields page. 

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 Data Fields 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 Metadata Fields document.

File Naming Convention

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


For example:



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 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.


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 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


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

Data Acquisition and Processing


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.


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.


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 Data Fields and Metadata Fields documents.

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.



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

Document Creation Date

October 2015

Document Revision Date

April 2016

Related Data Sets

SMAP Data at NSIDC | Overview

SMAP Radar Data at the ASF DAAC

Related Websites


Contacts and Acknowledgments


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


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.

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.

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.

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