Data Set ID:
SPL4SMAU

SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4

SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:

For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.

Version Summary:

Changes to this version include:

  • The land surface modeling system was revised in the following ways:
    • Improved input parameter data sets for land cover, topography, and vegetation height are based on more recent data sets. Land cover inputs were updated to the GlobCover2009 product, resulting in a slightly different land mask between Version 3 and Version 4. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne lidar measurements.
    • The model background precipitation forcing is rescaled to match the climatology of the Global Precipitation Climatology Project (v2.2), which results in substantial changes in the precipitation and soil moisture climatology in Africa and the high latitudes, where the gauge-based Climate Prediction Center Unified precipitation is not used.
    • SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements from the Soil Climate Analysis Network and U.S. Climate Reference Network were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the model’s root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the model’s surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture.
    • Additional model changes include revisions to the parameters and parameterizations of the surface energy balance and the snow depletion curve.
  • The Version 4 brightness temperature scaling parameters are based on eight years of SMOS observations and three years of SMAP observations where the SMOS climatology is unavailable due to radio frequency interference. Note that the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4.
  • Analysis increments are no longer computed for the “catchment deficit” model prognostic variable in the Ensemble Kalman filter update step.
  • Minor bug fixes.
  • Added x and y coordinate variables [including arrays of EASE-Grid 2.0 coordinate values, Climate and Forecast (CF)-compliant metadata, and HDF-5 dimension scales] as well as an EASE-Grid 2.0 projection grid mapping variable. This augmentation of L4 soil moisture data files improves interoperability and user workflow via ArcGIS/QGIS, OPeNDAP, and programmatic access. Three new data fields accommodate this change: EASE2_global_projection, x, and y.

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Parameter(s):
  • Soils > Soil Moisture/Water Content > Root Zone Soil Moisture
  • Soils > Soil Moisture/Water Content > Surface Soil Moisture
Spatial Coverage:
N: 85.044, 
S: -85.044, 
E: 180, 
W: -180
Spatial Resolution:
  • 9 km x 9 km
Temporal Coverage:
  • 31 March 2015
Temporal Resolution: 3 hour
Data Format(s):
  • HDF5
Platform(s) SMAP Observatory
Sensor(s): SMAP L-BAND RADIOMETER
Version: V4
Data Contributor(s): Reichle, R., G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu.
Metadata XML: View Metadata Record

Data Citation

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.

Reichle, R., G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu. 2018. SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/60HB8VIP2T8W. [Date Accessed].

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Detailed Data Description

Parameter Description

SMAP Level-4 soil moisture data include the following parameters:

  • Surface soil moisture (0-5 cm vertical average)
  • Root zone soil moisture (0-100 cm vertical average)
  • Additional research products (not validated), including surface meteorological forcing variables, soil temperature, evapotranspiration, net radiation, and error estimates for select output fields that are produced internally by the SMAP Level-4 soil moisture algorithm

Soil moisture is output in volumetric units, in wetness (or relative saturation) units, and in percentile units (except surface soil moisture). 

Refer to the Data Fields document for details on all parameters. Parameters are further described in the Algorithm Theoretical Basis Document (ATBD) for this product, Reichle et al. 2014, under Section 3: Physics of the Problem.

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File Contents

SMAP Level-4 soil moisture data consists of three main products:

  • Geophysical Data
  • Analysis Update Data
  • Land Model Constants

For each 3-hour interval, there are two files: one geophysical (gph) file and one analysis update (aup) file. Land model constants (lmc) are provided in a single file per Science Version. Science Version IDs (such as Vv3030) are included in all file names, and are defined in the File Naming Convention section.

Geophysical Data

The Geophysical Data (gph) product includes a series of 3-hourly time-averaged geophysical data fields from the assimilation system, such as surface and root zone soil moisture. Figure 1 shows a subset of the gph file contents.

Image of File Contents
Figure 1. Subset of the Geophysical Data File Contents
For a complete list of file contents for the SMAP Level-4 soil moisture product, refer to the Data Fields page. 

Analysis Update Data

The Analysis Update Data (aup) product includes a series of 3-hourly instantaneous/snapshot files that contain the following:

  • Analysis Data: Soil moisture and temperature analysis estimates, including error estimates
  • Forecast Data: Land model predictions of brightness temperature, soil moisture, and soil temperature
  • Observations Data: Assimilated SMAP brightness temperature observations and data assimilation diagnostics

Figure 2 shows a subset of the aup file contents.

Image of File Contents
Figure 2. Subset of the Analysis Update Data File Contents
For a complete list of file contents for the SMAP Level-4 soil moisture product, refer to the Data Fields page. 

Land Model Constants

The Land Model Constants (lmc) product includes static land surface model constants that provide further interpretation of the geophysical land surface fields. Figure 3 shows a subset of the lmc file contents.

Image of File Contents
Figure 3. Subset of the Land Model Constants File Contents
For a complete list of file contents for the SMAP Level-4 soil moisture product, refer to the Data Fields page. 


Data Fields

Each file contains the main data groups summarized above. For a complete list and description of all data fields within these groups, refer to the Data Fields document.

All global data arrays are two dimensional with 1624 rows and 3856 columns (6,262,144 pixels per layer).

Metadata Fields

Each product also contains 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.

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Format

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

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File Naming Convention

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

SMAP_L4_SM_pid_yyyymmddThhmmss_VLMmmm_NNN.[ext]

For example:

SMAP_L4_SM_gph_20151015T133000_Vv3030_001.h5

Where:

Table 1. File Naming Conventions
Variable Description
SMAP Indicates SMAP mission data
L4_SM Indicates specific product (L4: Level-4; SM: Soil Moisture)
pid

Product ID (PID), where:

Variable Description of Data Description of Date/Time for Product
gph Geophysical Data The date/time corresponds to the center point of the 3-hourly time averaging interval. For example, T013000 corresponds to the time average from 00:00:00 UTC to 03:00:00 UTC on a given day.
aup Analysis Update Data The date/time indicates the time of the analysis update. For example, T030000 indicates an analysis for 03:00:00 UTC on a given day. This analysis would typically assimilate all SMAP data observed between 01:30:00 UTC and 04:30:00 UTC.
lmc Land Surface Model Constants For the LMC product (time-invariant constants), which consists of only one file per Science Version, the date/time is 00000000T000000.
yyyymmddThhmmss Date/time in Universal Coordinated Time (UTC) of the data file, 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 minute, 2-digit second
VLMmmm

Science Version ID, where:

Variable Description
V Version (Not a variable; leading character will always be V)
L Launch Indicator (v: Validated Data)
M 1-Digit Major Version Number
mmm 3-Digit Minor Version Number

Example: Vv3030 indicates a Validated-quality product with a version of 3.030. Refer to the SMAP Data Versions page for version information.
NNN Product counter indicating the number of times the file was generated under the same Science Version ID 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
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File Size and Volume

Table 2 provides file sizes and daily volume estimates for each product.

Table 2. Approximate File Sizes and Total Volume for SMAP L4 Soil Moisture Products
Product File Size Total Volume
gph 138 MB 1.1 GB (Daily)
aup 85 MB 0.7 GB (Daily)
lmc 35 MB 35 MB*
* Not a daily product. LMC data are provided in a single file per Science Version.
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Spatial Coverage

Coverage spans from 180°W to 180°E, and from approximately 85.044°N to 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. Coverage is for the global land surface excluding inland water and permanently frozen areas.

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Spatial Resolution

The native spatial resolution of the radiometer footprint is approximately 40 km. Data are then assimilated into a land surface model that is gridded using the 9 km global EASE-Grid 2.0 projection.

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Projection and Grid Description

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 81 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 2 shows a schematic of the nesting to a resolution of 3 km (4872 rows x 11568 columns on global coverage), 9 km (1624 rows x 3856 columns on global coverage) and 36 km (406 rows x 964 columns on global coverage).

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.

Figure 2. Perfect Nesting in EASE-Grid 2.0

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

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Temporal Coverage

Coverage is continuous and spans from 31 March 2015 to present.

SMAP 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

However, gaps in the SMAP time series do not affect this product. While some temporal coverage gaps exist in the input SPL1CTB data, the SPL4SM product is processed continuously and does not have temporal coverage gaps. When SPL1CTB gaps occur, SPL4SM data are processed using information from SMAP observations assimilated prior to each gap in the input SPL1CTB data, as well as information from the land surface model.

Latencies

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

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Temporal Resolution

Three basic time steps are involved in the generation of the Level-4 soil moisture products, including:

  1. The land model computational time step (7.5 minutes)
  2. The Ensemble Kalman Filter (EnKF) analysis update time step (3 hours)
  3. The reporting/output time step for the instantaneous and time-average geophysical fields that are stored in the data products (3 hours)

SMAP observations are assimilated in an EnKF analysis update step at the nearest 3-hourly analysis time such as 0z, 3z, ..., and 21z (where z indicates Zulu). A broad variety of geophysical parameters are provided as 3-hourly averages between these update times. Moreover, instantaneous forecast and analysis soil moisture and temperature estimates are provided along with the assimilated observations. These snapshots are nominally for 0z, 3z,…, or 21z.

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Software and Tools

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

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Data Acquisition and Processing

This section has been adapted from Reichle et al. 2014, the ATBD for this product. Additional documentation of the algorithm is provided by Reichle et al. 2017a,b.

Sensor or Instrument Description

For a detailed description of the SMAP instrument, visit the SMAP Instrument page at the JPL SMAP website.

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Data Sources

SMAP Level-4 soil moisture products are derived from the following data sets: 

In addition, ancillary data sources used as input to calculating the SMAP Level-4 soil moisture products are obtained from the GMAO; these sources are listed in the ATBD, Section 4.1.3: Ancillary Data Requirements. Precipitation observations that are used to correct the GMAO precipitation estimates are obtained from the NOAA Climate Prediction Center (Reichle et al. 2017a,b).

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Theory of Measurements

The primary SMAP measurements, land surface microwave emission at 1.41 GHz and radar backscatter at 1.26 GHz and 1.29 GHz, are directly related to surface soil moisture (in the top 5 cm of the soil column). Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (defined here as soil moisture in the top 1 m of the soil column), which is not directly linked to SMAP observations. The foremost objective of the SMAP Level-4 Surface and Root Zone Soil Moisture (SPL4SM) product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a soil moisture data assimilation system.

The land surface model component of the assimilation system is driven with observation-based surface meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. Finally, the assimilation system uses the land model to interpolate and extrapolate SMAP observations in time and in space. The SPL4SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on SMAP observations and complementary information from a variety of sources. The assimilation algorithm considers the respective uncertainties of each component and, if properly calibrated, yields a product that is superior to both satellite and land model data. Error estimates for the SPL4SM product are generated as a by-product of the data assimilation system.

The ATBD for this product provides a detailed description of the SPL4SM product, its algorithm, and how the product is validated.

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Derivation Techniques and Algorithms

SMAP Level-4 soil moisture products are generated from a variety of data sources. Utilizing the baseline data assimilation algorithm discussed below, input data sources are used with the SMAP Level-4 soil moisture model to provide enhanced estimates of surface soil moisture, root zone soil moisture, and related geophysical variables.

Baseline Algorithm

The SPL4SM science algorithm consists of two key processing elements:

  1. GEOS-5 Catchment Land Surface and Microwave Radiative Transfer Model
  2. GEOS-5 Ensemble-Based Land Data Assimilation Algorithm

The GEOS-5 Catchment Land Surface and Microwave Radiative Transfer Model is a numerical description of the water and energy transport processes at the land-atmosphere interface, augmented with a model that describes the land surface microwave radiative transfer (refer to section 4.1.1 of the ATBD: Reichle et al. 2014). The GEOS-5 Ensemble-Based Land Data Assimilation System is the tool used to merge SMAP observations with estimates from the land model as it is driven with observation-based surface meteorological forcing data.

The SMAP Level-4 soil moisture baseline algorithm, described in detail in the ATBD, includes a soil moisture analysis based on the ensemble Kalman filter and a rule-based freeze/thaw analysis. However, data users should note that for Validated Version 4 data, the algorithm ingests only the SPL1CTB radiometer brightness temperatures, contrary to the planned use of downscaled brightness temperatures from the SPL2SMAP product and of landscape freeze-thaw state retrievals from the SPL2SMA product. The latter two products—SPL2SMAP and SPL2SMA—are based on radar observations and are only available for the period from 13 April 2015 through 07 July 2015 due to an anomaly that caused the premature failure of the SMAP L-band radar. Neither of these two radar-based products is assimilated in the SMAP Level-4 soil moisture algorithm.

More information about error sources is provided in the ATBD under Section 4.1.2: Mathematical Description of the Algorithm. For more information on data product accuracy, refer to Reichle et al. 2017a,b and the Validated Assessment Report from Reichle et al. 2018.

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Processing Steps

SMAP Level-4 soil moisture data are generated by the GMAO located at the NASA Goddard Space Flight Center (GSFC), using the High-End Computing Facilities at the NASA Center for Climate Simulation (NCCS), also located at GSFC in Greenbelt, Maryland.

SMAP SPL1CTB data are required for the baseline algorithm. Aside from SMAP observations, the data assimilation system requires initialization, parameter, and forcing inputs for the Catchment land surface model, as well as input error parameters for the ensemble-based data assimilation system. These ancillary data requirements are described in detail in the ATBD, Section 4.1.3: Ancillary Data Requirements. The precipitation observations used to correct the GMAO precipitation estimates are obtained from the NOAA Climate Prediction Center (Reichle et al. 2017a,b). Note that for this version, the model background precipitation forcing is rescaled to match the climatology of the Global Precipitation Climatology Project (v2.2), which results in substantial changes in the precipitation and soil moisture climatology in Africa and the high latitudes, where the gauge-based Climate Prediction Center Unified precipitation is not used.

For more information on each portion of the algorithm processing flow, refer to the ATBD.

Land Surface Modeling System and SMAP Nature Run 

Note that for Version 4 SPL4SM products an improved version of the land surface modeling system is used. The corresponding model-only Nature Run (NRv7.2) simulation is used to derive brightness temperature scaling parameters, model soil moisture initial conditions, and the soil moisture climatology. For this release, the land surface modeling system was revised in the following ways:

  • Improved input parameter data sets for land cover, topography, and vegetation height are based on more recent data sets. Land cover inputs were updated to the GlobCover2009 product, resulting in a slightly different land mask between Version 3 and Version 4. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne lidar measurements.  
  • The model background precipitation forcing is rescaled to match the climatology of the Global Precipitation Climatology Project (v2.2), which results in substantial changes in the precipitation and soil moisture climatology in Africa and the high latitudes, where the gauge-based Climate Prediction Center Unified precipitation is not used.
  • SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements from the Soil Climate Analysis Network and U.S. Climate Reference Network were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the model’s root-zone excess reservoir into the surface excess reservoir. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brings the model’s surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture.  
  • Additional model changes include revisions to the parameters and parameterizations of the surface energy balance and the snow depletion curve.
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Error Sources

The data assimilation system weighs the relative errors of the assimilated lower-level product (such as radiance or retrieval) and the land model forecast. Estimates of the error of the assimilation product are dynamically determined as a by-product of this calculation. How useful these error estimates are depends on the accuracy of the input error parameters and needs to continue to be determined through validation; refer to the ATBD, Section 4.2.4. The target accuracy of the assimilated brightness temperatures is discussed in the SPL1CTB product documentation. Error estimates of the land surface model and required input error parameters are discussed in the ATBD for this product.

Each instantaneous land model field is accompanied with a corresponding instantaneous error field which is provided for select variables. The relevant outputs are listed in the Data Fields document for the SPL4SMAU product. Specifically, the error estimates are derived from the ensemble standard deviation of the analyzed fields. For soil moisture, the ensemble standard deviation is computed from the analysis ensemble in volumetric units (m3 m-3). For temperatures, the ensemble standard deviation is provided in units of kelvin. These error estimates will vary in space and time.

More information about error sources is provided in the ATBD under Section 4.1.2: Mathematical Description of the Algorithm. For more information on data product accuracy, refer to Reichle et al. 2017a,b and the Validated Assessment Report from Reichle et al. 2018.

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Quality Assessment

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

Quality Overview

SMAP products provide multiple means to assess quality. Uncertainty measures and file-level metadata that provide quality information are provided within each product. For details, refer to the Data Fields and Metadata Fields documents.

Each HDF5 file contains file-level metadata. A separate metadata file with an .xml file extension is available from the NSIDC DAAC with every HDF5 file; it contains essentially the same information as the file-level metadata. In addition, a Quality Assessment (QA) file with a .qa file extension is provided for every HDF5 file. QA files contain spatial statistics across the SMAP Level-4 soil moisture products, such as the global minimum, mean, and maximum of each data field.

Level-4 surface and root zone soil moisture estimates are validated to a Root Mean Square Error (RMSE) requirement of 0.04 m3 m-3 after removal of the long-term mean bias. This accuracy requirement is identical to Level-2 soil moisture product validation and excludes regions with snow and ice cover, frozen ground, mountainous topography, open water, urban areas, and vegetation with water content greater than 5 kg m-2. Research outputs (not validated) include the surface meteorological forcing fields, land surface fluxes, soil temperature and snow states, runoff, and error estimates that are derived from the ensemble.

Quality Control

Quality control is also an integral part of the soil moisture assimilation system. Two kinds of quality control (QC) measures are applied. The first set of QC steps is based on the flags that are provided with the SMAP observations. Only SMAP brightness temperature data that have favorable flags for soil moisture estimation are assimilated, such as acceptably low vegetation density, no rain, no snow cover, no frozen ground, no RFI, sufficient distance from open water, etc.

The second set of QC steps are additional rules that exclude SMAP observations from assimilation in the EnKF soil moisture update whenever the land surface model indicates that (1) heavy rain is falling, (2) the soil is frozen, or (3) the ground is fully or partly covered with snow. The assimilation system will typically provide some weight to the model background and thus buffers the impact of anomalous observations that are not caught in the flagging process.

Note: Brightness temperature observations from Version 4 SPL1CTB granules that have known deficiencies were excluded from assimilation in the Version 4 SPL4SM algorithm.

For more quality control information, refer to the Data Fields document.

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References and Related Publications

References

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.

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.

Crow, W. T., F. Chen, R. H. Reichle, and Q. Liu. 2017. L Band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting. Geophysical Research Letters. 44:5495-5503. https://dx.doi.org/10.1002/2017GL073642.

Crow, W. T., F. Chen, R. H. Reichle, Y. Xia, and Q. Liu. 2018. Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models. Geophysical Research Letters. 45, in press, https://doi.org/10.1029/2018GL077193.

De Lannoy, G. J. M., and R. H. Reichle. 2016. Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model. Hydrology and Earth System Sciences. 20:4895-4911. Hydrol. Earth Syst. Sci., 20:4895-4911. https://dx.doi.org/10.5194/hess-20-4895-2016.

De Lannoy, G. J. M., and R. H. Reichle. 2016. Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation. Journal of Hydrometeorology, 17:669-691. https://dx.doi.org/10.1175/JHM-D-15-0037.1.

Entekhabi, D., R. H. Reichle, R. D. Koster, and W. T. Crow. 2010. Performance Metrics for Soil Moisture Retrievals and Application Requirements. Journal of Hydrometeorology. 11:832–840. https://dx.doi.org/10.1175/2010JHM1223.1.

Koster, R. D., Q. Liu, S. P. P. Mahanama, and R. H. Reichle. 2018. Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation. Journal of Hydrometeorology. In press. https://dx.doi.org/10.1175/JHM-D-17-0228.1.

Reichle, R. H., and Q. Liu. 2014. Observation-Corrected Precipitation Estimates in GEOS-5. SMAP Project, Global Modeling and Assimilation Office, Goddard Space Flight Center, Greenbelt, MD, USA. NASA/TM–2014-104606, Vol. 35. (https://gmao.gsfc.nasa.gov/pubs/docs/Reichle734.pdf, 495 KB)

Reichle, R. et al. 2014. SMAP Algorithm Theoretical Basis Document: L4 Surface and Root-Zone Soil Moisture Product. SMAP Project, JPL D-66483, Jet Propulsion Laboratory, Pasadena, CA, USA. (PDF, 1.4 MB; see Technical References)

Reichle, R., G. J. M. De Lannoy, Q. Liu, J. V. Ardizzone, F. Chen, A. Colliander, A. Conaty, W. Crow, T. Jackson, J. Kimball, R. D. Koster, and E. Brent Smith. 2016. Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release). GMAO Office Note No. 12 (Version 1.0), 55 pp, NASA Goddard Space Flight Center, Greenbelt, MD, USA. (PDF, 3.2 MB; see Technical References)

Reichle, R. H., et al.  2017a. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements. Journal of Hydrometeorology 18:2621-2645. http://dx.doi.org/doi:10.1175/JHM-D-17-0063.1. 

Reichle, R. H., G. J. De Lannoy, Q. Liu, R. D. Koster, J. S. Kimball, W. T. Crow, J. V. Ardizzone, P. Chakraborty, D. W. Collins, A. L. Conaty, M. Girotto, L. A. Jones, J. Kolassa, H. Lievens, R. A. Lucchesi, and E. B. Smith. 2017b. Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics. Journal of Hydrometeorology, accepted. https://doi.org/10.1175/JHM-D-17-0130.1.

Reichle, R. H., Q. Liu, R. D. Koster, J. Ardizzone, A. Colliander, W. Crow, G. J. M. De Lannoy, and J. Kimball. Soil Moisture Active Passive (SMAP) Project Assessment Report for Version 4 of the L4_SM Data Product. National Aeronautics and Space Administration: Technical Report Series on Global Modeling and Data Assimilation, Volume 52. (PDF, 2.6 MB; see Technical References)

Technical References

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

Contacts and Acknowledgments

Investigators

Rolf H. Reichle, Randal Koster, Qing Liu
NASA Goddard Space Flight Center
Global Modeling and Assimilation Office
Mail Code 610.1
8800 Greenbelt Rd
Greenbelt, MD 20771 USA

Gabrielle De Lannoy
KU Leuven
Department of Earth and Environmental Sciences
Celestijnenlaan 200 E-box 2411
B-3001 Heverlee
Belgium

Wade Crow
Hydrology and Remote Sensing Lab
US Department of Agriculture/Agricultural Research Service (USDA ARS)
Beltsville, MD 20705 USA

John Kimball
Numerical Terradynamic Simulation Group (NTSG)
College of Forestry & Conservation
The University of Montana
Missoula, MT 59812-1049 USA

Document Information

Document Creation Date

October 2015

Document Revision Date

June 2018

FAQ

Why does the root zone soil moisture in the SMAP Level-4 soil moisture products vary in such close unison with the surface soil moisture?
The surface and root zone soil moisture estimates in the SMAP Level-4 soil moisture products are the outputs of a land surface model into which SMAP observations of brightness temperature have been assimilated. The coupling between the surface layer and the root zone layer is known to be very... read more
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
Why do the soil moisture values in the SMAP Level-4 data vary from what I expect in a particular region?
There are a few reasons that the soil moisture data values in SMAP Level-4 data products may vary from what you expect in a particular region. The first step a data user should take in investigating apparently anomalous values is to look at the rich quality information and other data flags... 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

How To

How do I programmatically request data services such as subsetting, reformatting, and reprojection using an API?
On 31 October 2018, system upgrades lead to a change in our API domain. The URL changed from https://n5eil01u.ecs.nsidc.org to https://n5eil02u.ecs.nsidc.org. We will have a redirect in place for several weeks, but we advise you to manually change the URL to avoid errors. If you have questions or... read more
How to import and geolocate SMAP Level-3 and Level-4 data in ENVI
The following are instructions on how to import and geolocate SMAP Level-3 Radiometer Soil Moisture HDF5 data in ENVI. Testing notes Software: ENVI Software version: 5.3 Platform: Windows 7 Data set: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil... read more
How do I visualize SMAP data in Worldview?
This video tutorial provides step-by-step instructions on how to visualize SMAP data in Worldview (http://worldview.earthdata.nasa.gov/). NASA Worldview is a map-based application... read more
How do I search, order, and customize SMAP data using Earthdata Search?
This video tutorial and attached PDF document provide step-by-step instructions on how to search, order, and customize SMAP data using Earthdata Search (https://search.earthdata.nasa.... read more
How to Import SMAP HDF Data Into ArcGIS
Selected SMAP L4, Version 4 HDF data (SPL4SMAU, SPL4SMGP, & SPL4SMLM) can be added to ArcGIS with a simple drag/drop or using the 'Add Data' function. These data can be imported and visualized but not geolocated. In order to import, project, and scale these data and other SMAP L3 and L4 HDF... read more
How do I access data using OPeNDAP?
Data can be programmatically accessed using NSIDC’s OPeNDAP Hyrax server, allowing you to reformat and subset data based on parameter and array index. For more information on OPeNDAP, including supported data sets and known issues, please see our OPeNDAP documentation: ... read more
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Questions? Please contact:
NSIDC User Services
Phone: 1 303 492-6199
Email: nsidc@nsidc.org