Data Description

Parameters

Surface soil moisture (0-5 cm) in m³/m³ derived from brightness temperatures (TBs) is output on a fixed global 9 km EASE-Grid 2.0. Also included are brightness temperatures in kelvin representing Level-1B brightness temperatures interpolated at a 9 km EASE-Grid 2.0 cell.

Refer to the Data Fields 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 group summarized in this section. For a complete list and description of all data fields within these groups, refer to the Data Fieldsdocument.

Soil Moisture Retrieval Data 

Includes soil moisture data, ancillary data, and quality assessment flags for either a descending/6:00 a.m. or ascending/6:00 p.m. half-orbit pass of the satellite.

Corrected brightness temperatures are also provided. For these brightness temperatures (such as tb_3_corrected), an additional procedure has been applied to correct for anomalous water and land values. A summary of the Water/Land Contamination Correction is provided in the Derivation Techniques and Algorithms section of this user guide.

Note: As described in the file naming convention section, file names indicate whether they contain ascending or descending data with either an A or D. For example, the SMAP_L2_SM_P_E_10508_A_20170119T005350_R14010_001.h5 file contains ascending data.

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:

SMAP_L2_SM_P_E_[Orbit#]_[A/D]_yyyymmddThhmmss_RLVvvv_NNN.[ext]

For example:

SMAP_L2_SM_P_E_10508_A_20170119T005350_R14010_001.h5

Where:

Table 1. File Naming Conventions

Variable	Description
SMAP	Indicates SMAP mission data
L2_SM_P_E	Indicates specific product (L2: Level-2; SM: Soil Moisture; P: Passive; E: Enhanced)
[Orbit#]	5-digit sequential number of the orbit flown by the SMAP spacecraft when data were acquired. Orbit 00000 began at launch. Orbit numbers increment each time the spacecraft flies over the southernmost point in the orbit path.
A/D	A: Ascending half-orbit pass of the satellite (where satellite moves from South to North, and 6:00 p.m. is the local solar time at the equator) D: Descending half-orbit pass of the satellite (where satellite moves from North to South, and 6:00 a.m. is the local solar time at the equator)
yyyymmddThhmmss	Date/time in Universal Coordinated Time (UTC) of the first data element that appears in the product, where: yyyymmdd 4-digit year, 2-digit month, 2-digit day T Time (delineates the date from the time, i.e. yyyymmddThhmmss) hhmmss 2-digit hour, 2-digit month, 2-digit second
RLVvvv	Composite Release ID (CRID), where: R Release L Launch Indicator (1: post-launch standard data) V 1-Digit Major CRID Version Number vvv 3-Digit Minor CRID Version Number 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 half-orbit file ranges from approximately 8 to 14 MB.

File Volume

The daily data volume is approximately 300 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.

Spatial Coverage Map

Figure 2 shows the spatial coverage of the SMAP L-Band Radiometer for one descending half orbit, which comprises one file of this data set.

Resolution

The native spatial resolution of the radiometer footprint is 36 km. Data are then interpolated using the Backus Gilbert optimal interpolation algorithm on the global cylindrical, and Northern and Southern Hemisphere azimuthal EASE-Grid 2.0 projections, all at a 9 km grid resolution. Soil moisture retrieval (based on water-corrected brightness temperatures), ancillary data, and quality flags are posted on the 9 km global cylindrical 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 9 x 9 km² regardless of longitude and latitude.

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 3 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.

Temporal Information

Coverage

Coverage spans from 31 March 2015 to present.

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

Latencies

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

Resolution

Each Level-2 half-orbit file spans approximately 49 minutes.

Data Acquisition and Processing

Background

The microwave portion of the electromagnetic spectrum, which includes wavelengths from a few centimeters to a meter, has long held 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, especially 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. (O'Neill et al. 2016)

For an in-depth description of the theory of these measurements, refer to Section 2: Passive Remote Sensing of Soil Moisture in the Algorithm Theoretical Basis Document (ATBD) for the SMAP baseline Level-2 soil moisture product, SPL2SMP.

Acquisition

SMAP enhanced Level-2 radiometer soil moisture data (SPL2SMP_E) are derived from SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 2 (SPL1CTB_E).

Derivation Techniques and Algorithms

SMAP enhanced Level-2 radiometer soil moisture data (SPL2SMP_E) are derived from SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 2 (SPL1CTB_E). Utilizing a baseline plus four optional soil moisture algorithms discussed below, SMAP brightness temperatures are converted into an estimate of the 0-5 cm surface soil moisture in units of m³/m³.

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

The following information has been adapted from O'Neill et al. (2016).

Algorithm Inputs and Outputs

The main input to the processing algorithm for this product is the water-corrected brightness temperatures in the SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 2 (SPL1CTB_E) product that contains the time-ordered, geolocated, calibrated SMAP enhanced Level-1B radiometer brightness temperatures (L1B_TB_E) that have been resampled to the fixed 9 km EASE-Grid 2.0. In addition to general geolocation and calibration, the enhanced Level-1B brightness temperature data have also been corrected for atmospheric effects, Faraday rotation, and low-level RFI effects prior to regridding. If the RFI encountered is too large to be corrected, the brightness temperature data are flagged accordingly and no soil moisture retrieval is attempted. Refer to the SPL1CTB_E ATBD for additional details.

In addition to brightness temperature observations, the SPL2SMP_E algorithm also requires ancillary data sets for the soil moisture retrieval. The specific parameters and sources of these and other externally provided ancillary data are listed in Section 6: Ancillary Datasets of the baseline SPL2SMP ATBD. In order for soil moisture to be retrieved accurately, a variety of global static and dynamic ancillary data are required. Static ancillary data are data which do not change during the mission, while dynamic ancillary data require periodic updates in time frames ranging from seasonally to daily. Static data include parameters such as permanent masks (land, water, forest, urban, mountain, etc.), the grid cell average elevation and slope derived from a Digital Elevation Model (DEM), permanent open water fraction, and soils information (primarily sand and clay fraction). Dynamic ancillary data include land cover, surface roughness, precipitation, vegetation parameters, and effective soil temperatures.

Ancillary data are also employed to set flags that help to determine either specific aspects of the processing, such as corrections for open water and frozen ground, or the quality of the retrievals, such as the precipitation flag. Refer to the Data Flags Section below. All input data to the SPL2SMP_E process are pre-mapped using the 9 km EASE-Grid 2.0 projection and are then aggregated at a spatial extent that is approximately the same as the native resolution (approximately 36 km) of the SMAP radiometer. Other parameters used by the SPL2SMP_E algorithm include a freeze/thaw flag, an open water fraction, and a vegetation index.

The primary contents of output SPL2SMP_E data are the retrieved soil moisture and associated Quality Assessment (QA) flags, as well as the values of the ancillary parameters needed to retrieve the output soil moisture for that grid cell.

Soil Moisture Algorithms

Decades of research by the passive microwave soil moisture community have resulted in a number of viable soil moisture retrieval algorithms that can be used with SMAP brightness temperature data. The European Space Agency (ESA) Soil Moisture and Ocean Salinity Mission (SMOS) mission currently flies an aperture synthesis L-band radiometer which produces brightness temperature data at multiple incidence angles over the same ground location. The baseline SMOS retrieval algorithm is based on the tau-omega model described in the SPL2SMP ATBD, Section 2.1: Physics of the Problem, but utilizes the SMOS multiple-incidence-angle capability to retrieve soil moisture. SMAP retrievals are also based on the tau-omega model, but use the constant-incidence-angle brightness temperature data produced by the SMAP conically-scanning radiometer. Other necessary parameters in the retrieval are obtained as ancillary data.

Prior to implementing the actual soil moisture retrieval, a preliminary step in the processing is to perform a Water/Land Contamination Correction to the input brightness temperature data for cases where a significant percentage of the grid cell contains a mix of land and open water. This procedure corrects for anomalous soil moisture values seen near coastlines in the previous version and should result in less rejected data due to waterbody contamination. In addition, a Soil Depth Correction is performed in order to reduce the dry bias seen when comparing SMAP data to in situ data from the core validation sites.

The Version 2 SPL2SMP_E product contains soil moisture retrieval fields produced by a baseline and several optional algorithms (Refer to Table 2). Inside an SPL2SMP_E file, the soil_moisture field is the one that links to the retrieval result produced by the currently-designated baseline algorithm, the Single Channel Algorithm V-pol (SCA-V). At present, the operational SPL2SMP_E Science Production Software (SPS) produces and stores soil moisture retrieval results from the following five algorithms:

Table 2. Soil Moisture Algorithm Options

Algorithm Options	Corresponding Data Field
Single Channel Algorithm H-pol (SCA-H)	soil_moisture_option1
Single Channel Algorithm V-pol (SCA-V) – Current Baseline	soil_moisture_option2 (Internally linked to the soil_moisturefield)
Dual Channel Algorithm (DCA)	soil_moisture_option3
Microwave Polarization Ratio Algorithm (MPRA)	soil_moisture_option4
Extended Dual Channel Algorithm (E-DCA)	soil_moisture_option5

Given results from SPL2SMP_E Calibration/Validation (Cal/Val) analyses, the SCA-V algorithm seems to deliver better performance than the SCA-H algorithm, which was designated as the pre-launch baseline retrieval algorithm. For this reason, the SCA-V is designated as the current baseline algorithm for the validated release of SPL2SMP_E. However, all five algorithms will be continuously assessed; the choice of the operational algorithm for the validated release of the product will be evaluated on a regular basis as analyses of new observations and Cal/Val data become available, and algorithm parameters are tuned based on a longer SMAP radiometer brightness temperature time series record.

All five algorithms operate on the same zeroth-order microwave emission model commonly known as the tau-omega model. In essence, this model relates brightness temperatures (SMAP L1 observations) to soil moisture (SMAP L2 retrievals) through ancillary information (e.g. soil texture, soil temperature, and vegetation water content) and a soil dielectric model. The algorithms differ in their approaches to solve for soil moisture from the model under different constraints and assumptions. The following sections provide concise descriptions of each algorithm. Further details are provided in O'Neill et al. 2016.

Baseline Single Channel Algorithm V-pol (SCA-V)

In the SCA-V, the vertically polarized brightness temperature (TB) observations are converted to emissivity using a surrogate for the physical temperature of the emitting layer. The derived emissivity is corrected for vegetation and surface roughness to obtain the soil emissivity. The Fresnel equation is then used to determine the dielectric constant from the soil emissivity. Finally, a dielectric mixing model is used to solve for the soil moisture given knowledge of the soil texture. Analytically, SCA-V attempts to solve for one unknown variable (soil moisture) from one equation that relates the vertically polarized TB to soil moisture. Vegetation information is provided by a 13-year climatological data base of global Normalized Difference Vegetation Index (NDVI) and a table of parameters based on land cover types.

Single Channel Algorithm H-pol (SCA-H)

The SCA-H is similar to SCA-V, in that the horizontally polarized TB observations are converted to emissivity using a surrogate for the physical temperature of the emitting layer. The derived emissivity is corrected for vegetation and surface roughness to obtain the soil emissivity. The Fresnel equation is then used to determine the dielectric constant. Finally, a dielectric mixing model is used to obtain the soil moisture given knowledge of the soil texture. Analytically, SCA-H attempts to solve for one unknown variable (soil moisture) from one equation that relates the horizontally polarized TB to soil moisture. Vegetation information is provided by a 13-year climatological data base of global NDVI and a table of parameters based on land cover.

Dual Channel Algorithm (DCA)

In the DCA, both the vertically and horizontally polarized TB observations are used to solve for soil moisture and vegetation optical depth. The algorithm iteratively minimizes a cost function (Φ2) that consists of the sum of squares of the differences between the observed TBs (TB^obs) and estimated TBs (TB^est):

   (Equation 1)

In each iteration step, the soil moisture and vegetation opacity are adjusted simultaneously until the cost function attains a minimum in a least square sense. Similar to SCA-V and SCA-H, ancillary information such as effective soil temperature, surface roughness, and vegetation single scattering albedo must be known a priori before the inversion process. DCA permits polarization dependence of coefficients in the forward modeling of TB observations. As currently implemented for SPL2SMP_E, the H and V coefficients are set the same.

Microwave Polarization Ratio Algorithm (MPRA)

The MPRA is based on the Land Parameter Retrieval Model (Owe 2015) and was first applied to multi-frequency satellites such as AMSR-E. Like DCA, MPRA attempts to solve for soil moisture and vegetation optical depth using the vertically and horizontally polarized TB observations. However, it does so under the assumptions that (1) the soil and canopy temperatures are considered equal, and (2) vegetation transmissivity and the single-scattering albedo are the same for both H and V polarizations. When these assumptions are satisfied, it can be shown that the soil moisture and vegetation optical depth can be solved analytically in closed form, resulting in the same solutions as would be obtained iteratively using DCA. Similarly to DCA, ancillary information such as effective soil temperature, surface roughness, and vegetation single scattering albedo must be known a priori before the inversion process.

Extended Dual Channel Algorithm (E-DCA)

The E-DCA is a variant of DCA. Like DCA, E-DCA uses both the vertically and horizontally polarized TB observations to solve for soil moisture and vegetation optical depth. In E-DCA, however, the cost function (Φ2) is formulated in a way different from that of DCA. Instead of minimizing the sum of squares of the differences between the observed and estimated TBs as in DCA (see Equation 1), the E-DCA attempts to minimize the sum of squares of the difference between the observed and estimated normalized polarization differences (expressed in natural logarithm) and the difference between the observed and estimated TBs (also expressed in natural logarithm) as follows:

   (Equation 2)

In each iteration step, soil moisture and vegetation opacity are adjusted simultaneously until the cost function attains a minimum in a least square sense. It is clear that when both Φ_DCA^2 and Φ_(E-DCA)^2 attain their theoretical minimum value (i.e. zero) in the absence of uncertainties of modeling, observations, and ancillary data, T_(B,v)^obs = T_(B,v)^est and T_(B,h)^obs = T_(B,h)^est, implying that DCA and E-DCA converge to the same solutions. The advantage of E-DCA over DCA, however, is apparent when in reality there is finite uncertainty (e.g., a dry bias associated with the ancillary soil temperature data); this uncertainty will be cancelled from the numerator and denominator in the calculation of the normalized polarization difference in Φ_(E-DCA)^2, leaving such uncertainty affecting only one component of the cost function instead of two components as in Φ_DCA^2. This reduction in the impact of soil temperature uncertainty on soil moisture retrieval should make E-DCA more tolerant of soil temperature uncertainty, resulting in fewer instances of retrieval failure than DCA. 

Processing

This product is generated by the SMAP Science Data Processing System (SDS) at the Jet Propulsion Laboratory (JPL). To generate this product, the processing software ingests the 6:00 a.m. descending and 6:00 p.m. ascending half-orbit files of the SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 2 (SPL1CTB_E) product. The ingested data are then inspected for retrievability criteria according to input data quality, ancillary data availability, and land cover conditions. When retrievability criteria are met, the software invokes the baseline and optional algorithms to generate soil moisture retrieval. Only cells that are covered by the actual swath for a given projection are written in the product.

Quality, Errors, and Limitations

Error Sources

Anthropogenic 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 thresholds to detect and, where possible, mitigate RFI.

Level-2 radiometer data can also contain bit errors caused by noise in communication links and memory storage devices. Consultative Committee on Space Data Systems (CCSDS) packets include error-detecting Cyclic Redundancy Checks (CRCs), which are used to flag errors.

More information about error sources is provided in Section 4.6: Algorithm Error Performance of the ATBD. (O'Neill et al. 2018).

Quality Assessment

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

Quality Overview

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 the National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). 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 the HDF5 file; it contains useful statistics such as the percentage of elements having various quality conditions. 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.

The information in the following sections has been adapted from O'Neill et al. (2016).

6:00 p.m. Ascending Half Orbits

Data from both 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes are used as input for soil moisture derivation in this Version 2 Validated product. However, the radiometer soil moisture algorithm assumes that the air, vegetation, and near-surface soil are in thermal equilibrium in the early morning hours; thus, retrievals from 6:00 p.m. ascending half-orbit passes may show a slight degradation in quality. Nonetheless, ubRMSE (unbiased root mean square error) and correlation of the p.m. and a.m. retrievals are relatively close.

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, such as the precipitation flag. These flags provide information as to whether the ground is frozen, covered with snow, 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. Unless otherwise stated, all areal fractions defined below refer to 33 x 33 km² inversion domain.

The flags described below, for example, are used in the surface_flag. Refer to Table 5 of the Data Fields page for more details.

Open Water Flag

Open water fraction can be determined from SMAP high-resolution radar and/or a priori information on permanent open freshwater from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD44W database. The SPL2SMP_E Version 2 product uses the MOD44W database due to the maturity of the SMAP radar open-water algorithm and availability of radar measurements, and are reported in Bit 0 and 1 in the surface_flag of the SPL2SMP_E product. This information serves as a flag to affect soil moisture retrieval processing in the following ways:

• If areal water fraction is 0.00–0.05, then retrieve soil moisture, but flag for recommended quality.
• If areal water fraction is 0.05–0.50, then flag for uncertain quality and retrieve soil moisture.
• If areal water fraction is above 0.50, then flag, but do not retrieve soil moisture.

Precipitation Flag 

The SMAP precipitation flag is set based on either forecasts of precipitation or using data from the Global Precipitation Mission (GPM). It is a binary precipitation/no precipitation flag which indicates the presence or absence of precipitation within a 33 km inversion domain at the time of the SMAP overpass. The presence of liquid in precipitation incident on the ground at the time of the SMAP overpass can adversely bias the retrieved soil moisture due to its large impact on SMAP brightness temperatures, as precipitation in the atmosphere is part of the atmospheric correction done in Level-1B brightness temperature processing. Unlike other flags, soil moisture retrieval will always be attempted even if precipitation is flagged. However, this flag serves as a warning to the user to view the retrieved soil moisture with some skepticism if precipitation is present.

• If precipitation is 0–1 mm/hr, then retrieve soil moisture, but flag for recommended quality.
• If precipitation is 1–25.4 mm/hr, then flag for uncertain quality and retrieve soil moisture.
• If precipitation is above 25.4 mm/hr, then flag, but do not retrieve soil moisture.

Snow Flag

Although the SMAP L-Band Radiometer can theoretically see through dry snow with its low dielectric to the soil underneath a snowpack, the snow flag is set based on the snow fraction as reported in the National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) database. The snow flag affects soil moisture retrieval processing in the following ways: 

• If snow areal fraction is 0.00–0.05, then retrieve soil moisture, but flag for recommended quality.
• If snow areal fraction is 0.05–0.50, then flag for uncertain quality and retrieve soil moisture.
• If snow areal fraction is above 0.50, then flag, but do not retrieve soil moisture.

Frozen Ground Flag 

The frozen ground flag is set from either 1) the flag passed through from the SMAP radiometer freeze/thaw algorithm, or 2) from modeled surface temperature information (Tsurf) from the Global Modeling and Assimilation Office (GMAO). These sources for the frozen ground flag are contained in the SPL2SMP_E product and are reflected in bits 7 and 8 of the surface_flag (bit 7: SMAP radiometer-derived freeze/thaw state; bit 8: GMAO Tsurf). For this SPL2SMP_E Version 2 product, GMAO Tsurf (bit 8) is used to determine frozen ground condition.

The frozen soil flag affects soil moisture retrieval processing in the following ways: 

• If frozen ground areal fraction is 0.00–0.05, then retrieve soil moisture, but flag for recommended quality.
• If frozen ground areal fraction is 0.05–0.50, then flag for uncertain quality and retrieve soil moisture.
• If frozen ground areal fraction is above 0.50, then flag, but do not retrieve soil moisture.

Note: SMAP radiometer freeze/thaw flags are presently validated only for all land regions north of 45°N latitude. While the SPL2SMP_E product contains global SMAP freeze/thaw flags, uncertainty in the flags is higher south of 45°N latitude due to small differences in the SMAP radiometer-derived reference freeze and thaw states upon which the freeze/thaw algorithm is based. Further information is available in the SMAP Level-3 Freeze/Thaw (SPL3FTP) Validated Assessment Report.

Urban Area Flag 

Since the brightness temperature of man-made, impervious, and urban areas cannot be estimated theoretically, the presence of urban areas in the 9 km Level-2 soil moisture grid cell cannot be corrected for during soil moisture retrieval. Thus, the presence of even a small amount of urban area in the radiometer footprint is likely to adversely bias the retrieved soil moisture. The SMAP urban flag is set based on the Columbia University Global Rural-Urban Mapping Project (GRUMP) data set (O'Neill et al. 2016). The urban fraction affects soil moisture retrieval processing in the following ways: 

• If urban areal fraction is 0.00–0.25, then retrieve soil moisture, but flag for recommended quality.
• If urban areal fraction is 0.25–1.00, then flag for uncertain quality and retrieve soil moisture.
• If urban areal fraction is above 1.00, then flag, but do not retrieve soil moisture.

Mountainous Area Flag 

Large and highly variable slopes present in the radiometer footprint will adversely affect the retrieved soil moisture. The SMAP mountainous area flag is derived from a combination of high elevation information from the DEM coupled with a statistical threshold based on the slope and slope variability within each 9 km grid cell. 

• If slope standard deviation is 0.0–3.0°, then retrieve soil moisture, but flag for recommended quality.
• If slope standard deviation is 3.0°–6.0°, then flag for uncertain quality and retrieve soil moisture.
• If slope standard deviation is above 6.0°, then flag, but do not retrieve soil moisture.

For in-depth details on all data flags, refer to the Data Fields document.

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

June 2018

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
California Institute of Technology (Caltech)
4800 Oak Grove Drive 
Pasadena, CA 91109 USA

Tom Jackson
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, Revision C. SMAP Project, JPL D-66480, Jet Propulsion Laboratory, Pasadena, CA. (PDF, 4.9 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.

Poe, G. A. 1990. Optimum Interpolation of Imaging Microwave Radiometer Data. IEEE Transactions on Geoscience and Remote Sensing 28(5):800-810.

Technical References

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