On 23 July 2019 at 22:28 UTC, the SMAP satellite was returned to science mode and resumed collecting data. Those data are now available. The first ~25 seconds of resumed data have been flagged for quality concerns, as described in this advisory note. A more detailed quality assessment and a description of what caused the SMAP outage will be provided at a later date. NSIDC will continue to post updates here: https://nsidc.org/data/smap/news
Data Set ID:
SPL2SMP_E

SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 3

This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to a 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: a global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.

This is the most recent version of these data.

Version Summary:

Changes to this version include:

  • The Dual Channel Algorithm (DCA) has been replaced by the Modified Dual Channel Algorithm (MDCA). MDCA achieves better retrieval performance through the modeling of polarization mixing between the vertically and horizontally polarized brightness temperature channels, as well as new estimates of single-scattering albedo and roughness coefficients. MDCA supersedes optional algorithms MPRA (option 4) and E-DCA (option 5).
  • As part of the option algorithm changes, the following data fields were removed: soil_moisture_option4, vegetation_opacity_option4, retrieval_qual_flag_option4, soil_moisture_option5, vegetation_opacity_option5, retrieval_qual_flag_option5.
  • As part of the option algorithm changes, the following data fields were added: albedo_option3, roughness_coefficient_option3, bulk_density, clay_fraction.
  • The baseline algorithm (SCA-V) remains unchanged.
  • Improved aggregation of values in input ancillary data, e.g. roughness, soil texture, NDVI. The fix has negligible impacts on retrievals estimated to be of recommended quality.

COMPREHENSIVE Level of Service

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

Documentation: Key metadata and comprehensive user guide available

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

See All Level of Service Details

Parameter(s):
  • MICROWAVE > BRIGHTNESS TEMPERATURE
  • 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
Spatial Resolution:
  • 9 km x 9 km
Sensor(s):SMAP L-BAND RADAR
Temporal Coverage:
  • 31 March 2015
Version(s):V3
Temporal Resolution49 minuteMetadata XML:View Metadata Record
Data Contributor(s):O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell.

Geographic Coverage

Other Access Options

Other Access Options

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As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell. 2019. SMAP Enhanced L2 Radiometer Half-Orbit 9 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/10.5067/017XZSKMLTT2. [Date Accessed].
Created: 
3 January 2019
Last modified: 
15 August 2019

Data Description

Parameters

The main output of this data set is 0-5 cm surface soil moisture (cm3/cm3) presented on the 9 km EASE-Grid 2.0 projection. Also included are Brightness Temperature (Tb) measurements (K), representing SMAP Level-1B brightness temperatures.

Refer to the Data Fields document for details on all parameters.

File Information

Format

Data are in HDF5 format. For compatible software and more information of HDF5 files, including an HDF5 tutorial, visit the HDF Group's HDF5 website.

File Contents

As shown in Figure 1, each HDF5 file is organized into two main groups, Metadata and Soil Moisture Retrieval Data, each of which contains sub-groups and/or data sets. 

Figure 1. Subset of File Contents
For a complete list of file contents, refer to the Data Fields page.

The Soil Moisture Retrieval Data group contains 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.

The Metadata Fields group includes all the 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 and as 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

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 10 to 15 MB.

File Volume

The daily data volume is approximately 300 MB.

Spatial Information

Coverage

Data set 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.

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. The swath width is approximately 1000 km, enabling nearly global coverage every two to three days.

Figure 2. Spatial coverage map displaying one descending half orbit of the SMAP L-Band Radiometer. 

Resolution

The native spatial resolution of the radiometer footprint is 36 km, but soil moisture retrieval, ancillary data, and quality flags are all interpolated, using the Backus Gilbert optimal interpolation algorithm, to a 9 km grid resolution. 

Geolocation

Data are gridded using the 9 km EASE-Grid 2.0 projection.The following tables provide information for geolocating this data set. For more on EASE-Grid 2.0, refer to the  EASE Grids website.

Table 2. Geolocation Details for the Global EASE-Grid
Geographic coordinate system
WGS 84
Projected coordinate system
EASE-Grid 2.0 Global
Longitude of true origin
0
Standard Parallel
30
Scale factor at longitude of true origin
N/A
Datum
WGS 84
Ellipsoid/spheroid
WGS 84
Units
meter
False easting
0
False northing
0
EPSG code
6933
PROJ4 string
+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
Reference

Table 3. Grid Details for the Global EASE-Grid
Grid cell size (x, y pixel dimensions) 9,024.31 projected meters (x)
9,024.31 projected meters (y)
Number of columns 3856
Number of rows 1624
Geolocated lower left point in grid 85.044° S, 180.000 ° W
Nominal gridded resolution 36 km by 36 km
Grid rotation N/A
ulxmap – x-axis map coordinate of the outer edge of the upper-left pixel -17367530.45 m
ulymap – y-axis map coordinate of the outer edge of the upper-left pixel 7314540.83 m

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

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 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 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 more details, refer to "Section 2: Passive Remote Sensing of Soil Moisture" of the Algorithm Theoretical Basis Document (ATBD) for the SMAP L2 & L3 Products (O'Neill et al. 2018).

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)  and generated by the SMAP Science Data Processing System (SDS) at the Jet Propulsion Laboratory (JPL).

Processing

SDS 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 retrieval algorithm, plus two optional soil moisture algorithms, to generate soil moisture retrieval; all algorithms convert SMAP brightness temperatures into an estimates of the 0-5 cm surface soil moisture (m3/m3). Only cells that are covered by the actual swath for a given projection are included in this data set. The three soil moisture retrieval algorithms are described briefly below.

For information regarding the Backus-Gilbert optimal interpolation algorithm used to enhance these data, refer to the SPL1CTB_E user guide. For more information on the soil moisture retrieval algorithms, users should refer to the O'Neill et al. 2016 and O'Neill et al. 2018 for more details.

Algorithm Inputs and Outputs

The main input to the processing algorithm is the SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 2 (SPL1CTB_E) data set. This product contains the time-ordered, geolocated, and calibrated SMAP enhanced Level-1B radiometer Brightness Temperatures (Tb) that have been resampled to the fixed 9 km EASE-Grid 2.0. In addition to general geolocation and calibration, the enhanced Level-1B Tdata 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 Tb data are flagged accordingly and no soil moisture retrieval is attempted. Refer to the SPL1CTB_E ATBD for additional details.

The input enhanced Level-1C Tdata (SPL1CTB_E) have been corrected for cases where a significant percentage of the grid cell contains a mix of land and open water (Water/Land Contamination Correction). This procedure corrects for anomalous soil moisture values seen near coastlines in previous versions and should result in less rejected data due to waterbody contamination. The correction is performed in the SPL1CTB_E product at the footprint level using the SMAP radiometer antenna gain pattern. When the antenna-gain-weighted water fraction within the antenna field of view (FOV) is less than or equal to 0.9, and when the antenna boresight falls on a land location as indicated by a static high-resolution land/water mask, the correction is applied. Conversely, when the antenna boresight falls on a water location, and when the water fraction within the antenna field of view (FOV) is greater than or equal to 0.1, the correction is applied. Over land, the resulting brightness temperatures will become warmer upon the removal of the contribution of water compared to the original uncorrected observations. Further details are provided in the Water/Land Contamination Correction section of the SPL1BTB user guide.

In addition to brightness temperature observations, the SPL2SMP_E algorithm also requires ancillary data sets for the soil moisture retrieval. 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. The specific parameters and sources of ancillary data are listed in "Section 6: Ancillary Data Sets" of the SMAP L2 & L3 Products ATBD (O'Neill et al. 2018).

Ancillary data are also employed to set flags that help 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. Other parameters used by the SPL2SMP_E algorithm include a freeze/thaw flag, an open water fraction, and a vegetation index. Refer to the Data Flags section below for more details. 

Note: all input data to the SPL2SMP_E process are pre-mapped using the 9 km EASE-Grid 2.0 projection and then aggregated at a spatial extent that is approximately the same as the native resolution (approximately 36 km) of the SMAP radiometer.

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 Tb data at multiple incidence angles over the same ground location. The baseline SMOS retrieval algorithm is based on the tau-omega model described in "Section 2.1: Physics of the Problem" of this data product's ATBD (O'Neill et al. 2018); SMAP retrievals are also based on the tau-omega model. In essence, this model relates Tb (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. 

Prior to implementing the soil moisture retrieval, Tb estimates are corrected for water/land contamination (described above and in the SPL1BTB user guide). This product also includes an improved depth correction scheme for the effective soil temperature (i.e. the surface_temperature field), which is a critical parameter in passive soil moisture retrieval. This correction reduces the dry bias seen when comparing SMAP data to in situ data from the core validation sites. At L-band frequency, the contributing soil depth of microwave emission (or penetration depth) may be slightly different from the pre-defined discrete soil depths at which the soil temperatures are available from a land surface model. The resulting discrepancy will lead to dry bias of retrieved soil moisture (i.e. retrievals lower than in situ soil moisture) if the model-based effective soil temperature is colder than the soil temperature sensed by the radiometer. Conversely, wet bias of retrieved soil moisture will occur if the model-based effective soil temperature is warmer than the soil temperature sensed by the radiometer.

The Version 3 SPL2SMP_E product contains soil moisture retrieval fields produced by the baseline algorithm and two other optional algorithms (refer to Table 4). The operational SPL2SMP Science Production Software (SPS) produces and stores soil moisture retrieval results from all three algorithms. Within an SPL2SMP file, the soil_moisture field is linked to the retrieval result produced by the current baseline algorithm, the Single Channel Algorithm V-pol (SCA-V). 

Table 4. 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_moisture field)
Modified Dual Channel Algorithm (MDCA) soil_moisture_option3

Among the three algorithms, SCA-V delivers the best performance according to SPL2SMP_E Calibration/Validation (Cal/Val) analyses. For this reason, SCA-V is designated as the current baseline algorithm. However, all three algorithms will be continuously assessed; the choice of the operational algorithm for the release of the product will be evaluated on a regular basis as analyses of new observations and Cal/Val data become available and as algorithm parameters are tuned using a longer SMAP record.

All three algorithms operate on the same zeroth-order microwave emission model, commonly known as the tau-omega model and described briefly above. However, the algorithms differ in their approaches and solve for soil moisture under different constraints and assumptions. A brief description of each algorithm is provided below. Users should refer to O'Neill et al. 2016 for more details.

Single Channel Algorithm (SCA) 

In SCA, horizontally (SCA-H) or vertically (SCA-V) 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 soil moisture given knowledge of the soil texture.

Analytically, SCA 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 and polarization.

Though SCA can apply to H-pol or V-pol Tb measurements, SCA-V is the current baseline soil moisture algorithm because it performs better when compared against SMAP Cal/Val data. The SCA-H algorithm was the pre-launch baseline retrieval algorithm.

Modified Dual Channel Algorithm (MDCA)

The Modified Dual Channel Algorithm (MDCA) is an extension of the SCA and replaces the Dual Channel Algorithm implemented in previous releases. Like DCA, MDCA uses both the vertically and horizontally polarized Tb observations to solve for soil moisture and vegetation optical depth. The algorithm iteratively minimizes the cost function (Φ2) shown in Equation 1:

   (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 before the inversion process. MDCA permits polarization mixing of the vertically and horizontally polarized Tb observations prior to cost function minimization.

The MDCA implemented in Version 6 of this data set performs better than the Dual Channel Algorithm (DCA), Microwave Polarization Ratio Algorithm (MPRA), and Extended Dual Channel Algorithm (EDCA) used in earlier releases. While it still does not perform as well as SCA-V, the MDCA retrieves tau, the vegetation opacity, in addition to soil moisture. There is currently no independent verification of the accuracy of these tau retrievals.

Quality, Errors, and Limitations

Error Sources

Anthropogenic RFI, principally from ground-based surveillance radars, can contaminate both radar and radiometer measurements at L-band frequencies. The SMAP radar and 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 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 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. For in-depth details regarding the quality of these data, refer to the Validated Assessment Report.

6:00 p.m. Ascending / Descending 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 beginning with Version 4 of this 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 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.

A brief description of data flags are provided below. For more details on all data flags, users should refer to the Data Fields page or the Product Specification Document.

  • Open Water Flag
    Open water fraction is determined by a priori information on permanent open freshwater from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD44W database. Open water are reported in Bits 0 and 1 in the surface_flag field of the SPL2SMP_E product. This information serves as a flag to affect soil moisture retrieval processing in the following ways:
    • If water fraction is 0.00–0.05, then retrieve soil moisture, but flag for recommended quality.
    • If water fraction is 0.05–0.50, then retrieve soil moisture, and flag for uncertain quality.
    • If water fraction is 0.50–1.00, 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 precipitation at the time of the SMAP overpass can adversely bias the retrieved soil moisture due to its large impact on Tb; corrections for participation are part of the Level-1B Tb processing. Unlike other flags, soil moisture retrieval will always be attempted even if precipitation is flagged. However, this flag serves as a warning to users 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 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), which is used to determine frozen ground conditions. These two flag sources 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 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 0.50–1.00, 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. More informaiton is available in the SMAP Level-3 Freeze/Thaw (SPL3FTP) Validated Assessment Report.

  • Urban Area Flag 
    ​Since the Tb of man-made, impervious, and urban areas cannot be estimated theoretically, the presence of urban areas in the 36 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 high elevation information from a DEM coupled with a statistical threshold based on the 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.

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

Table 5. Summary of Version Changes

Version

Date

Description of Changes

V3 August 2019

Changes to this version include:

  • The Dual Channel Algorithm (DCA) has been replaced by the Modified Dual Channel Algorithm (MDCA). MDCA achieves better retrieval performance through the modeling of polarization mixing between the vertically and horizontally polarized brightness temperature channels, as well as new estimates of single-scattering albedo and roughness coefficients. MDCA supersedes optional algorithms MPRA (option 4) and E-DCA (option 5). 
    • As part of the option algorithm changes, the following data fields were removed: soil_moisture_option4, vegetation_opacity_option4, retrieval_qual_flag_option4,  soil_moisture_option5, vegetation_opacity_option5, retrieval_qual_flag_option5.
    • As part of the option algorithm changes, the following data fields were added: albedo_option3, roughness_coefficient_option3, bulk_density, clay_fraction.
  • The baseline algorithm (SCA-V) remains unchanged.
  • Improved aggregation of values in input ancillary data, e.g. roughness, soil texture, NDVI. The fix has negligible impacts on retrievals estimated to be of recommended quality.

V2

June 2018

Changes to this version include:

  • Level-1B water-corrected brightness temperatures are used in passive soil moisture retrieval. 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. Five new data fields accommodate this correction: grid_surface_status, surface_water_fraction_mb_h, surface_water_fraction_mb_v, tb_h_uncorrected, and tb_v_uncorrected.
  • Improved depth correction for effective soil temperature used in passive soil moisture retrieval; new results are captured in the surface_temperature data field. This correction reduces the dry bias seen when comparing SMAP data to in situ data from the core validation sites.
  • Frozen ground flag updated to reflect improved freeze/thaw detection algorithm, providing better accuracy; new results are captured in bit 7 of the surface_flag.

V1

December 2016

First public release 

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

M. Julian Chaubell
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA

References

References

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

Carroll, M.L., C. M. DiMiceli, M. R. Wooten, A. B. Hubbard, R.A. Sohlberg, and J. R. G. Townshend. 2017. MOD44W MODIS/Terra Land Water Mask Derived from MODIS and SRTM L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD. doi: 10.5067/MODIS/MOD44W.006

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)

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 the Common Metadata Repository (CMR) API. CMR is a metadata system that provides search capabilities for data at NSIDC. A synchronous REST interface utilizes the ... read more
How to import and geolocate SMAP Level-1C and Level-2 data in ENVI
The following are instructions on how to import and geolocate SMAP Level-1C HDF5 data in ENVI. Testing notes Software: ENVI Software version: 5.3 and above. If using version 5.3, service pack 5.3.1 is needed.  Platform: Windows 7 Data set: SMAP L1C... 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 do I interpret the surface and quality flag information in the Level-2 and -3 passive soil moisture products?
SMAP data files contain rich quality information that can be useful for many data users. The retrieval quality flag and surface flag bit values and interpretations are documented in the respective product Data Fields pages: Level-2 soil moisture product (SPL2SMP)... 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 are the latencies for SMAP radiometer data sets?
The following table describes both the required and actual latencies for the different SMAP radiometer data sets. Latency is defined as the time (# days, hh:mm:ss) from data acquisition to product generation. Short name Title Latency Required Actual (mean1) SPL1AP SMAP L1A... read more
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. Short name Title Subsetting... read more
Why don't the SMAP enhanced soil moisture products include landcover class?
While the standard SMAP Level-2 and -3 radiometer soil moisture products* contain landcover_class and landcover_class_fraction in the data files, the enhanced soil moisture products** do not. This is because the landcover class ancillary data are not available at the 9 km grid posting that the... read more
How are the enhanced SMAP radiometer products generated and what are the benefits of using these products?
There is considerable overlap of the SMAP radiometer footprints, or Instantaneous Fields of View (IFOVs), which are defined by the contours where the sensitivity of the antenna has fallen by 3db from its maximum. The IFOVs are spaced about 11 km apart in the along scan direction with scan lines... read more