Data Description

Parameters

Surface soil moisture (0-5 cm) in m³/m³ derived from brightness temperatures (TBs) is output on a fixed global 36 km EASE-Grid 2.0. Also included are brightness temperatures in kelvin representing the weighted average of Level-1B brightness temperatures whose boresights fall within a 36 km EASE-Grid 2.0 cell.

Refer to the Product Specification 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 website.

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:

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 Product Specification Document.

Soil Moisture Retrieval Data AM

Includes soil moisture data, ancillary data, and quality assessment flags for each descending half-orbit pass of the satellite [where the satellite moves from North to South and 6:00 a.m. is the Local Solar Time (LST) at the equator].

Soil Moisture Retrieval Data PM

Includes soil moisture data, ancillary data, and quality assessment flags for each ascending half-orbit pass of the satellite (where the satellite moves from South to North and 6:00 p.m. is the LST at the equator.

Corrected brightness temperatures are also provided for each AM and PM group. For these brightness temperatures (such as tb_3_corrected), an additional procedure has been applied to correct for anomalous water and land values. Further details are provided in the Water/Land Contamination Correction section of the SPL2SMP user guide. 

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_P_E_yyyymmdd_RLVvvv_NNN.[ext]

For example:

SMAP_L3_SM_P_E_20170117_R14010_001.h5

Where:

File Size

Each file is approximately 271 MB.

File Volume

The daily data volume is approximately 271 MB.

Spatial Information

Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N and 85.044°S for the global EASE-Grid 2.0 projection. 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 approximately 1000 km, enabling nearly global coverage every two to three days.

Resolution

The native spatial resolution of the radiometer footprint is 36 km. Data are then interpolated using the Backus-Gilbert optimal interpolation algorithm into the global cylindrical EASE-Grid 2.0 projection with 9 km spacing.

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 36 x 36 km² regardless of longitude and latitude. Using this projection, all global data arrays have dimensions of 406 rows and 964 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 Grids website.

Temporal Information

Coverage

Coverage spans from 31 March 2015 to 12 August 2019.

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

A significant gap in coverage occurred between 19 June and 23 July 2019 after the SMAP satellite went into Safe Mode. A brief description of the event and its impact on data quality is available in the SMAP Post-Recovery Notice.

Latencies

FAQ: What are the latencies for SMAP radiometer data sets?

Resolution

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

Data Acquisition and Processing

This section has been adapted from O'Neill et al. (2016) and O'Neill et al. (2018).

Background

The microwave portion of the electromagnetic spectrum, which includes wavelengths from a few centimeters to a meter, has long held the most promise for estimating surface soil moisture remotely. Passive microwave sensors measure the natural thermal emission emanating from the soil surface. The variation in the intensity of this radiation depends on the dielectric properties and temperature of the target medium, which for the near-surface soil layer is a function of the amount of moisture present. Low microwave frequencies, at L-band or approximately 1 GHz, offer the following additional advantages:

• The atmosphere is almost completely transparent, providing all-weather sensing
• Transmission of signals from the underlying soil is possible through sparse and moderate vegetation layers (up to at least 5 kg/m² of vegetation water content)
• Measurement is independent of solar illumination which allows for day and night observations

For an in-depth description of the theory of these measurements, refer to Passive Remote Sensing of Soil Moisture in the Algorithm Theoretical Basis Document (ATBD) for this product, O'Neill et al. 2018.

Acquisition

SMAP enhanced Level-3 radiometer soil moisture data (SPL3SMP_E) are composited from SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 2 (SPL2SMP_E).

Derivation Techniques and Algorithms

The SMAP enhanced Level-3 radiometer soil moisture product (SPL3SMP_E) is a daily composite of the SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 2 (SPL2SMP_E). The derivation of soil moisture from SMAP brightness temperatures occurs in the Level-2 processing.

For information regarding the Backus-Gilbert optimal interpolation algorithm used to enhance these data, refer to the SPL1CTB_E user guide.

Refer to the Derivation Techniques section in the SPL2SMP_E user guide for details on algorithms and ancillary data.

Processing

The SPL3SMP_E product is a daily global product. To generate the product, individual SPL2SMP_E half-orbit files acquired over one day are composited to produce a daily multi-orbit global map of retrieved soil moisture.

The SPL2SMP_E swaths overlap poleward of approximately +/- 65° latitude. Where overlap occurs, three options were considered for compositing multiple data points at a given grid cell:

1. Use the most recent (or last-in) data point
2. Take the average of all data points within the grid cell
3. Choose the data points observed closest to 6:00 a.m. Local Solar Time (LST) for observations derived from SMAP descending passes and closest to 6:00 p.m. LST for observations derived from SMAP descending passes

The current approach for the SPL3SMP_E product is to use the nearest 6:00 a.m. LST and nearest 6:00 p.m. LST criteria to perform Level-3 compositing separately for descending and ascending passes, respectively. According to these criteria, for a given grid cell, an L2 data point acquired closest to 6:00 a.m. LST or closest to 6:00 p.m. LST will make its way to the final enhanced Level-3 file; other late-coming L2 data points falling into the same grid cell will be ignored. For a given L2 half-orbit granule whose time stamp (yyyymmddThhmmss) is expressed in UTC, only the hhmmss part is converted into local solar time. (O'Neill et al. 2016)

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. The SMAP radiometer electronics and algorithms include design features to mitigate the effects of RFI. The SMAP radiometer implements a combination of time and frequency diversity, kurtosis detection, and use of T4 thresholds to detect and, where possible, mitigate RFI.

Radiometer enhanced L3 data can contain bit errors caused by noise in communication links and memory storage devices. The CCSDS packets include error-detecting Cyclic Redundancy Checks (CRCs), which the L1A processor uses to flag errors.

More information about error sources is provided in Section 4.6: Algorithm Error Performance of the ATBD, O'Neill et al. 2016.

Quality Assessment

For in-depth details regarding the quality of these data, refer to the Validated 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 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 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

Bit flags generated from input SMAP data and ancillary data are also employed to help determine the quality of the retrievals. Ancillary data help determine either specific aspects of the processing (such as corrections for transient water) or the quality of the retrievals (e.g. precipitation flag). These flags will 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 will 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 Data Flags section in the SPL2SMP_E user guide. All flags in SPL2SMP_E are carried over into the SPL3SMP_E product.

Instrumentation

Description

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

Software and Tools

For tools that work with SMAP data, see the Tools web page.

Version History

Document Creation Date

December 2016

Document Revision Date

November 2019

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

Peggy O'Neill
NASA Goddard Space Flight Center
Global Modeling and Assimilation Office
Mail Code 610.1
8800 Greenbelt Rd.
Greenbelt, MD 20771 USA

Steven Chan
Jet Propulsion Laboratory
4800 Oak Grove Drive 
Pasadena, CA 91109 USA

Tom Jackson
United States Department of Agriculture/Agricultural Research Service (USDA/ARS) 
Hydrology and Remote Sensing Laboratory
104 Bldg. 007, BARC-West 
Beltsville, MD 20705 USA

Rajat Bindlish
NASA Goddard Space Flight Center
Hydrological Sciences Laboratory
8800 Greenbelt Rd.
Greenbelt, MD 20771 USA

References

References

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. https://dx.doi.org/10.3390/ijgi3031154.

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. https://dx.doi.org/10.3390/ijgi1010032.

Chan, S., R. Bindlish, P. O'Neill, E. Njoku, T. Jackson, A. Colliander, F. Chen, M. Mariko, S. Dunbar, J. Piepmeier, S. Yueh, D. Entekhabi, M. Cosh, T. Caldwell, J. Walker, X. Wu, A. Berg, T. Rowlandson, A. Pacheco, H. McNairn, M. Thibeault, J. Martinez-Fernandez, A. Gonzalez-Zamora, M. Seyfried, D. Bosch, P. Starks, D. Goodrich, J. Prueger, M. Palecki, E. Small, M. Zreda, J. Calvet, W. Crow, and Y. Kerr. 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54(8):4994-5007. https://dx.doi.org/10.1109/TGRS.2016.2561938.

Chan, S. K., R. Bindlish, P. O'Neill, T. Jackson, E. Njoku, S. Dunbar, J. Chaubell, J. Piepmeier, S. Yueh, D. Entekhabi, A. Colliander, F. Chen, M. H. Cosh, T. Caldwell, J. Walker, A. Berg, H. McNairn, M. Thibeault, J. Martiinez-Fernandez, F. Uldall, M. Seyfried, D. Bosch, P. Starks, C. Holifield Collins, J. Prueger, R. van der Velde, J. Asanuma, M. Palecki, E. E. Small, M. Zreda, J. Calvet, W. T. Crow, Y. Kerr, 2018. Development and assessment of the SMAP enhanced passive soil moisture product, Remote Sensing of Environment, 204: 931-941. https://doi.org/10.1016/j.rse.2017.08.025.

Jackson, T. , P. O’Neill, S. Chan, R. Bindlish, A. Colliander, F. Chen , S. Dunbar , J. Piepmeier , S. Misra , M. Cosh , T. Caldwell, J. Walker, X. Wu, A. Berg, T. Rowlandson, A. Pacheco, H. McNairn, M. Thibeault, J. Martínez-Fernández, Á. González-Zamora, E. Lopez-Baeza, F. Udall, M. Seyfried, D. Bosch, P. Starks, C. Holifield, J. Prueger, Z. Su, R. van der Velde, J. Asanuma, M. Palecki, E. Small, M. Zreda, J. Calvet, W. Crow, Y. Kerr, S. Yueh, and D. Entekhabi. 2018. Calibration and Validation for the L2/3_SM_P Version 5 and L2/3_SM_P_E Version 2 Data Products. SMAP Project, JPL D-56297, Jet Propulsion Laboratory. Pasadena, CA. (PDF, 1.1 MB; see Technical References)

Jet Propulsion Laboratory (JPL). "SMAP Instrument." JPL SMAP Soil Moisture Active Passive. https://smap.jpl.nasa.gov/observatory/instrument/ [20 August 2015].

NASA EOSDIS Land Processes DAAC. 2015. Land Water Mask Derived from MODIS and SRTM L3 Global 250m SIN Grid. Version 005. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD. (https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod44w), [20 August 2015].

O'Neill, P. E., E. G. Njoku, T. J. Jackson, S. Chan, and R. Bindlish. 2018. SMAP Algorithm Theoretical Basis Document: Level 2 & 3 Soil Moisture (Passive) Data Products, Revision D. SMAP Project, JPL D-66480, Jet Propulsion Laboratory, Pasadena, CA. (PDF, 1.7 MB; see Technical References)

O'Neill, P. E., E. G. Njoku, T. J. Jackson, S. Chan, and R. Bindlish. 2016. SMAP Algorithm Theoretical Basis Document: Level 2 & 3 Soil Moisture (Passive) Data Products. SMAP Project, JPL D-66480, Jet Propulsion Laboratory, Pasadena, CA. (PDF, 3.3 MB; see Technical References) 

Owe, M., De Jeu, R. A. M., and Walker, J. P. 2015. A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index. IEEE Transactions on Geoscience and Remote Sensing, 39(8):1643–1654, 2001.

Piepmeier, J. R., D. G. Long, and E. G. Njoku. 2008. Stokes Antenna Temperatures. IEEE Trans. Geosci. Remote Sens. 46(2):516-527.

Technical References

For additional references, such as ATBDs, refer to the Technical References tab at the top of this user guide.