On Wednesday, October 16th between 8 am and 2 pm MDT, the following data collections will not be available due to planned system maintenance: AMSR-E, Aquarius, ASO, High Mountain Asia, IceBridge, ICESat/GLAS, ICESat-2, MEaSUREs, MODIS, NISE, SMAP, SnowEx, and VIIRS. 
Data Set ID:
SPL4SMGP

SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, 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.

This is the most recent version of these data.

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.

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):
  • Soils > Soil Moisture/Water Content > Root Zone Soil Moisture
  • Soils > Soil Moisture/Water Content > Surface Soil Moisture
Data Format(s):
  • HDF5
Spatial Coverage:
N: 85.044, 
S: -85.044, 
E: 180, 
W: -180
Platform(s):SMAP Observatory
Spatial Resolution:
  • 9 km x 9 km
Sensor(s):SMAP L-BAND RADIOMETER
Temporal Coverage:
  • 31 March 2015
Version(s):V4
Temporal Resolution3 hourMetadata XML:View Metadata Record
Data Contributor(s):Reichle, R., G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu.

Geographic Coverage

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.

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 Geophysical Data, 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/KPJNN2GI1DQR. [Date Accessed].
Created: 
7 January 2019
Last modified: 
10 October 2019

Data Description

Parameters

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

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

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 of this user guide.

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 Appendix. 
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 Appendix of this document. 
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 Appendix. 


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 Appendix of this 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 Product Specification Document.

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

File Size

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.

Spatial Information

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.

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.

Projection and Grid Information

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 4 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 4. Perfect Nesting in EASE-Grid 2.0

For more on EASE-Grid 2.0, refer to the EASE Grids website.

Temporal Information

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:

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.

For the period between 19 June and 23 July 2019, an extended gap occurred in the L1 - L3 SMAP products. During this period, the L4 Soil Mositure data sets were not informed by SMAP data. For more information on this SMAP outage, users should refer to the SMAP Post-Recovery Notice

Latencies

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

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.

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, Reichle et al. 2017b, and Reichle et al. 2019.

Background

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.

Acquisition

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, Reichle et al. 2017b, Reichle et al. 2019).

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, Reichle et al. 2017b, Reichle et al. 2019, and the Validated Assessment Report from Reichle et al. 2018.

Processing

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, Reichle et al. 2017b, Reichle et al. 2019). 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.

Quality, Errors, and Limitations

Quality Assessments

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

Quality Overview

SMAP products provide multiple means to assess quality. Uncertainty measures and file-level metadata that provide quality information are provided within each product. For details, refer to the Appendix of this document and the Product Specification 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 Appendix of this doocument.

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, Reichle et al. 2017b, Reichle et al. 2019, and the Validated Assessment Report from Reichle et al. 2018.

Instrumentation

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

Software and Tools

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

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

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

References

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. 2018. 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)

Reichle, R. H., Q. Liu, R. D. Koster, W. T. Crow, G. J. M. De Lannoy, J. S. Kimball, J. V. Ardizzone, ... and J. P. Walker. 2019. Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product. J. of Advances in Modeling Earth Systems, in press. https://doi.org/10.1029/2019MS001729.

Appendix - Data Fields

This appendix provides a description of all data fields within the SMAP L4 Global 3-hourly 9 km Surface and Rootzone Soil Moisture Geophysical DataSMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, and SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants products. The data are grouped into the following main HDF5 groups: 

  • Geophysical Data (gph)*
  • Analysis Data (aup)
  • Forecast_Data (aup)
  • Observations_Data (aup)
  • Land-Model-Constants Data (lmc)
  • Metadata

For a description of metadata fields for this product, refer to the Product Specification Document.

* As reflected in the file names, gphaup, and lmc indicate three different file collections: geophysical, analysis, and land-model-constants data, respectively. Note that analysis, forecast, and observations data are contained in the aup collection.

Geophysical_Data

Table A1 describes the data fields in the Geophysical_Data group stored in the gph file collection. This group contains fields that specify time-average geophysical data (including soil moisture, soil temperature, and land surface fluxes). Time and space coordinate information is stored in the HDF5 root data group.

Table A1. Data Fields for Geophysical_Data
Data Field Name Type Shape Unit Valid Min Valid Max Fill Value
EASE2_global_projection String /cell_lat /cell_lon N/A N/A N/A N/A
baseflow_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.01 -9999.0
heat_flux_ground Float32 /cell_lat /cell_lon W m-2 -1000.0 1000.0 -9999.0
heat_flux_latent Float32 /cell_lat /cell_lon W m-2 -2500.0 3000.0 -9999.0
heat_flux_sensible Float32 /cell_lat /cell_lon W m-2 -2500.0 3000.0 -9999.0
height_lowatmmodlay Float32 /cell_lat /cell_lon m 40.0 80.0 -9999.0
land_evapotranspiration_flux Float32 /cell_lat /cell_lon kg m-2 s-1 -0.001 0.001 -9999.0
land_fraction_saturated Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
land_fraction_snow_covered Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
land_fraction_unsaturated Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
land_fraction_wilting Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
leaf_area_index Float32 /cell_lat /cell_lon m2 m-2 0.0 10.0 -9999.0
net_downward_longwave_flux Float32 /cell_lat /cell_lon W m-2 -1000.0 200.0 -9999.0
net_downward_shortwave_flux Float32 /cell_lat /cell_lon W m-2 0.0 1365.0 -9999.0
overland_runoff_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.05 -9999.0
precipitation_total_surface_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.05 -9999.0
radiation_longwave_absorbed_flux Float32 /cell_lat /cell_lon W m-2 35.0 800.0 -9999.0
radiation_shortwave_downward_flux Float32 /cell_lat /cell_lon W m-2 0.0 1500.0 -9999.0
sm_profile Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_profile_pctl Float32 /cell_lat /cell_lon percent 0.0 100.0 -9999.0
sm_profile_wetness Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
sm_rootzone Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_rootzone_pctl Float32 /cell_lat /cell_lon percent 0.0 100.0 -9999.0
sm_rootzone_wetness Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
sm_surface Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_surface_wetness Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
snow_depth Float32 /cell_lat /cell_lon m 0.0 50.0 -9999.0
snow_mass Float32 /cell_lat /cell_lon kg m-2 0.0 10000.0 -9999.0
snow_melt_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.05 -9999.0
snowfall_surface_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.05 -9999.0
soil_temp_layer1 Float32 /cell_lat /cell_lon K 210.0 340.0 -9999.0
soil_temp_layer2 Float32 /cell_lat /cell_lon K 210.0 330.0 -9999.0
soil_temp_layer3 Float32 /cell_lat /cell_lon K 215.0 325.0 -9999.0
soil_temp_layer4 Float32 /cell_lat /cell_lon K 220.0 325.0 -9999.0
soil_temp_layer5 Float32 /cell_lat /cell_lon K 225.0 325.0 -9999.0
soil_temp_layer6 Float32 /cell_lat /cell_lon K 230.0 320.0 -9999.0
soil_water_infiltration_flux Float32 /cell_lat /cell_lon kg m-2 s-1 0.0 0.05 -9999.0
specific_humidity_lowatmmodlay Float32 /cell_lat /cell_lon kg kg-1 0.0 0.4 -9999.0
surface_pressure Float32 /cell_lat /cell_lon Pa 40000.0 110000.0 -9999.0
surface_temp Float32 /cell_lat /cell_lon K 180.0 350.0 -9999.0
temp_lowatmmodlay Float32 /cell_lat /cell_lon K 180.0 350.0 -9999.0
vegetation_greenness_fraction Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
windspeed_lowatmmodlay Float32 /cell_lat /cell_lon m s-1 -60.0 60.0 -9999.0
cell_column Unsigned32 /cell_lat /cell_lon dimensionless 0 3855 4294967294
cell_lat Float32 /cell_lat /cell_lon degrees -90.0 90.0 -9999.0
cell_lon Float32 /cell_lat /cell_lon degrees -180.0 179.999 -9999.0
cell_row Unsigned32 /cell_lat /cell_lon dimensionless 0 1623 4294967294
time Float64 /cell_lat /cell_lon seconds since 2000-01-01 11:58:55.816 N/A N/A N/A
x Float64 /cell_lat /cell_lon m -17367531 17367531 0.0
y Float64 /cell_lat /cell_lon m -7342231 7342231 0.0

Analysis_Data

Table A2 describes the data fields in the Analysis_Data group stored in the aup file collection. This group contains soil moisture and temperature estimates after the ensemble Kalman filter analysis update, along with their corresponding uncertainty estimates. Soil moisture and temperature values are snapshots/instantaneous data. Time and space coordinate information is stored in the HDF5 root data group.

Table A2. Data Fields for Analysis_Data group
Data Field Name Type Shape Unit Valid Min Valid Max Fill Value
EASE2_global_projection String N/A N/A N/A N/A N/A
sm_profile_analysis Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_profile_analysis_ensstd Float32 /cell_lat /cell_lon m3 m-3 0.0 1.0 -9999.0
sm_rootzone_analysis Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_rootzone_analysis_ensstd Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_surface_analysis Float32 /cell_lat /cell_lon m3 m-3 0.0 1.0 -9999.0
sm_surface_analysis_ensstd Float32 /cell_lat /cell_lon m3 m-3 0.0 1.0 -9999.0
soil_temp_layer1_analysis Float32 /cell_lat /cell_lon K 210.0 340.0 -9999.0
soil_temp_layer1_analysis_ensstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
surface_temp_analysis Float32 /cell_lat /cell_lon K 180.0 350.0 -9999.0
surface_temp_analysis_ensstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
cell_column Unsigned32 /cell_lat /cell_lon dimensionless 0 3855 4294967294
cell_lat Float32 /cell_lat /cell_lon degrees -90.0 90.0 -9999.0
cell_lon Float32 /cell_lat /cell_lon degrees -180.0 179.999 -9999.0
cell_row Unsigned32 /cell_lat /cell_lon dimensionless 0 1623 4294967294
time Float64 /cell_lat /cell_lon seconds since 2000-01-01 11:58:55.816 N/A N/A N/A
x Float64 /cell_lat /cell_lon m -17367531 17367531 0.0
y Float64 /cell_lat /cell_lon m -7342231 7342231 0.0

Forecast_Data

Table A3 describes the data fields in the Forecast_Data group stored in the aup file collection. This group is the land model equivalent of the Observations_Data group; it provides the land surface model’s predictions of the assimilated observations. These forecasts, or observation predictions, are based on propagating the land surface model forward in time from the previous analysis time step. The Forecast_Data group does not contain a medium-range (5-day) forecast of land surface conditions. Soil moisture and temperature values are snapshots/instantaneous data. Time and space coordinate information is stored in the HDF5 root data group.

Table A3. Data Fields for Forecast_Data group
Data Field Name Type Shape Unit Valid Min Valid Max Fill Value
EASE2_global_projection String N/A N/A N/A N/A N/A
sm_profile_forecast Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_rootzone_forecast Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
sm_surface_forecast Float32 /cell_lat /cell_lon m3 m-3 0.0 0.9 -9999.0
soil_temp_layer1_forecast Float32 /cell_lat /cell_lon K 210.0 340.0 -9999.0
surface_temp_forecast Float32 /cell_lat /cell_lon K 180.0 350.0 -9999.0
tb_h_forecast Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_h_forecast_ensstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
tb_v_forecast Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_v_forecast_ensstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
cell_column Unsigned32 /cell_lat /cell_lon dimensionless 0 3855 4294967294
cell_lat Float32 /cell_lat /cell_lon degrees -90.0 90.0 -9999.0
cell_lon Float32 /cell_lat /cell_lon degrees -180.0 179.999 -9999.0
cell_row Unsigned32 /cell_lat /cell_lon dimensionless 0 1623 4294967294
time Float64 /cell_lat /cell_lon seconds since 2000-01-01 11:58:55.816 N/A N/A N/A
x Float64 /cell_lat /cell_lon m -17367531 17367531 0.0
y Float64 /cell_lat /cell_lon m -7342231 7342231 0.0

Observations_Data

Table 4 describes the data fields in the Observations_Data group stored in the aup file collection. This group provides information about the assimilated SMAP observations. Time and space coordinate information is stored in the HDF5 root data group.

Table A4. Data Fields for Observations_Data group
Data Field Name Type Shape Unit Valid Min Valid Max Fill Value
EASE2_global_projection String N/A N/A N/A N/A N/A
tb_h_obs Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_h_obs_assim Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_h_obs_errstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
tb_h_obs_time_sec Float64 /cell_lat /cell_lon seconds 4.65156E8 9.46E8 -9999.0
tb_h_orbit_flag Unsigned32 /cell_lat /cell_lon dimensionless 0 2 4294967294
tb_h_resolution_flag Unsigned32 /cell_lat /cell_lon dimensionless 1 2 4294967294
tb_v_obs Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_v_obs_assim Float32 /cell_lat /cell_lon K 100.0 350.0 -9999.0
tb_v_obs_errstd Float32 /cell_lat /cell_lon K 0.0 50.0 -9999.0
tb_v_obs_time_sec Float64 /cell_lat /cell_lon seconds 4.65156E8 9.46E8 -9999.0
tb_v_orbit_flag Unsigned32 /cell_lat /cell_lon dimensionless 0 2 4294967294
tb_v_resolution_flag Unsigned32 /cell_lat /cell_lon dimensionless 1 2 4294967294
cell_column Unsigned32 /cell_lat /cell_lon dimensionless 0 3855 4294967294
cell_lat Float32 /cell_lat /cell_lon degrees -90.0 90.0 -9999.0
cell_lon Float32 /cell_lat /cell_lon degrees -180.0 179.999 -9999.0
cell_row Unsigned32 /cell_lat /cell_lon dimensionless 0 1623 4294967294
time Float64 /cell_lat /cell_lon seconds since 2000-01-01 11:58:55.816 N/A N/A N/A
x Float64 /cell_lat /cell_lon m -17367531 17367531 0.0
y Float64 /cell_lat /cell_lon m -7342231 7342231 0.0

Land-Model-Constants_Data

Table A5 describes the data fields in the Land-Model-Constants_Data group stored in the lmc file collection. This group contains fields that specify static/time-invariant parameters (or constants) of the Catchment Land Surface Model (CLSM) and its associated L-band Microwave Radiative Transfer Model (MWRTM). Time and space coordinate information is stored in the HDF5 root data group.

Note: Due to the time-invariant nature of the file contents, the lmc file collection consists of only one granule per data product version (as identified by a distinct Science Version ID).

Table A5. Data Fields for Land-Model-Constants_Data group
Data Field Name Type Shape Unit Valid Min Valid Max Fill Value
EASE2_global_projection String N/A N/A N/A N/A N/A
cell_elevation Float32 /cell_lat /cell_lon m -500.0 6000.0 -9999.0
cell_land_fraction Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
clsm_cdcr1 Float32 /cell_lat /cell_lon kg m-2 30.0 3000.0 -9999.0
clsm_cdcr2 Float32 /cell_lat /cell_lon kg m-2 200.0 6000.0 -9999.0
clsm_dzgt1 Float32 /cell_lat /cell_lon m 0.0988 0.0988 -9999.0
clsm_dzgt2 Float32 /cell_lat /cell_lon m 0.1952 0.1952 -9999.0
clsm_dzgt3 Float32 /cell_lat /cell_lon m 0.3859 0.3859 -9999.0
clsm_dzgt4 Float32 /cell_lat /cell_lon m 0.7626 0.7626 -9999.0
clsm_dzgt5 Float32 /cell_lat /cell_lon m 1.5071 1.5071 -9999.0
clsm_dzgt6 Float32 /cell_lat /cell_lon m 10.0 10.0 -9999.0
clsm_dzpr Float32 /cell_lat /cell_lon m 1.33 10.0 -9999.0
clsm_dzrz Float32 /cell_lat /cell_lon m 1.0 1.0 -9999.0
clsm_dzsf Float32 /cell_lat /cell_lon m 0.05 0.05 -9999.0
clsm_dztsurf Float32 /cell_lat /cell_lon m 0.0 0.05 -9999.0
clsm_poros Float32 /cell_lat /cell_lon m3 m-3 0.3 0.9 -9999.0
clsm_veghght Float 32 /cell_lat/cell_lon m 0.0 60.0 -9999.0
clsm_wp Float32 /cell_lat /cell_lon m3 m-3 0.001 0.3 -9999.0
mwrtm_bh Float32 /cell_lat /cell_lon dimensionless 0.0 0.7 -9999.0
mwrtm_bv Float32 /cell_lat /cell_lon dimensionless -0.15 0.85 -9999.0
mwrtm_clay Float32 /cell_lat/cell_lon dimensionless 0.0 1.0 -9999.0
mwrtm_lewt Float32 /cell_lat /cell_lon kg m-2 0.0 1.0 -9999.0
mwrtm_omega Float32 /cell_lat /cell_lon dimensionless 0.0 0.3 -9999.0
mwrtm_poros Float32 /cell_lat /cell_lon m3 m-3 0.3 0.9 -9999.0
mwrtm_rghhmax Float32 /cell_lat /cell_lon dimensionless 0.0 3.0 -9999.0
mwrtm_rghhmin Float32 /cell_lat /cell_lon dimensionless 0.0 2.0 -9999.0
mwrtm_rghnrh Float32 /cell_lat /cell_lon dimensionless 0.0 1.75 -9999.0
mwrtm_rghnrv Float32 /cell_lat /cell_lon dimensionless -1.0 2.0 -9999.0
mwrtm_rghpolmix Float32 /cell_lat /cell_lon dimensionless 0.0 0.0 -9999.0
mwrtm_rghwmax Float32 /cell_lat /cell_lon m3 m-3 0.3 0.9 -9999.0
mwrtm_rghwmin Float32 /cell_lat /cell_lon m3 m-3 0.1 0.4 -9999.0
mwrtm_sand Float32 /cell_lat /cell_lon dimensionless 0.0 1.0 -9999.0
mwrtm_soilcls Unsigned32 /cell_lat /cell_lon dimensionless 1 253 4294967294
mwrtm_vegcls Unsigned32 /cell_lat /cell_lon dimensionless 1 16 4294967294
mwrtm_wangwp Float32 /cell_lat /cell_lon m3 m-3 0.0 0.4 -9999.0
mwrtm_wangwt Float32 /cell_lat /cell_lon m3 m-3 0.1 0.4 -9999.0
cell_column Unsigned32 /cell_lat /cell_lon dimensionless 0 3855 4294967294
cell_lat Float32 /cell_lat /cell_lon degrees -90.0 90.0 -9999.0
cell_lon Float32 /cell_lat /cell_lon degrees -180.0 179.999 -9999.0
cell_row Unsigned32 /cell_lat /cell_lon dimensionless 0 1623 4294967294
time Float64 /cell_lat /cell_lon seconds since 2000-01-01 11:58:55.816 N/A N/A N/A
x Float64 /cell_lat /cell_lon m -17367531 17367531 0.0
y Float64 /cell_lat /cell_lon m -7342231 7342231 0.0

Data Field Definitions 

Table A6 lists all Level-4 soil moisture data fields and their definitions. All fields are two-dimensional and Float32 unless otherwise indicated in the description.

Table A6. Description of Data Fields for SPL4SMP
Data Field Name GEOS-5 Name Data group Description

baseflow_flux

BASEFLOW Geophysical Baseflow

cell_column

CELL_COLUMN_INDEX [All Data groups]1 The column index of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Type is Unsigned32.

cell_elevation

CELL_ELEVATION LandModel Constants Mean elevation above sea Level-of land within each grid cell.

cell_land_fraction

FRLAND LandModel Constants Area fraction of land within each grid cell.

cell_lat

LATITUDE [All Data groups]1 The geodetic latitude of the center of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Zero latitude represents the Equator. Positive latitudes represent locations North of the Equator. Negative latitudes represent locations South of the Equator.

cell_lon

LONGITUDE [All Data groups]1 The geodetic longitude of the center of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Zero longitude represents the Prime Meridian. Positive longitudes represent locations to the East of the Prime Meridian. Negative longitudes represent locations to the West of the Prime Meridian.

cell_row

CELL_ROW_INDEX [All Data groups]1 The row index of each cell in the cylindrical 9 km Earth-fixed EASE-Grid 2.0. Type is Unsigned32.

clsm_cdcr1

CLSM_cdcr1 LandModel Constants Catchment model: Catchment deficit at which baseflow ceases

clsm_cdcr2

CLSM_cdcr2 LandModel Constants Catchment model: Maximum water holding capacity of land field

clsm_dzgt1

CLSM_dzgt1 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 1

clsm_dzgt2

CLSM_dzgt2 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 2

clsm_dzgt3

CLSM_dzgt3 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 3

clsm_dzgt4

CLSM_dzgt4 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 4

clsm_dzgt5

CLSM_dzgt5 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 5

clsm_dzgt6

CLSM_dzgt6 LandModel Constants Catchment model: Thickness of soil heat diffusion model layer 6

clsm_dzpr

CLSM_dzpr LandModel Constants Catchment model: Thickness of profile soil moisture layer (“depth-to-bedrock” in the Catchment model)

clsm_dzrz

CLSM_dzrz LandModel Constants Catchment model: Thickness of root zone soil moisture layer

clsm_dzsf

CLSM_dzsf LandModel Constants Catchment model: Thickness of surface soil moisture layer

clsm_dztsurf

CLSM_DZTSURF LandModel Constants Catchment model: Thickness of soil layer associated with surface_temp

clsm_poros

CLSM_poros LandModel Constants Catchment model: Soil porosity
clsm_veghght CLSM_veghght LandModel Constants Catchment model: Vegetation canopy height

clsm_wp

CLSM_WP LandModel Constants Catchment model: Soil wilting point

EASE2_global_projection

grid_mapping [All Data groups]1 Defines the parameters of the cylindrical 9 km Earth-fixed EASE-Grid 2.0 projection and the mapping from latitude/longitude to grid-native coordinates

heat_flux_ground

GHLAND Geophysical Downward ground heat flux into layer 1 of soil heat diffusion model

heat_flux_latent

LHLAND Geophysical Latent heat flux from land2

heat_flux_sensible

SHLAND Geophysical Sensible heat flux from land2

height_lowatmmodlay

HLML Geophysical Center height of lowest atmospheric model layer

land_evapotranspiration_flux

EVLAND Geophysical Evapotranspiration from land2

land_fraction_saturated

FRSAT Geophysical Fractional land area that is saturated and snow-free2

land_fraction_snow_covered

FRSNO Geophysical Fractional land area that is snow-covered2

land_fraction_unsaturated

FRUNSAT Geophysical Fractional land area that is unsaturated (but non-wilting) and snow-free2

land_fraction_wilting

FRWLT Geophysical Fractional land area that is wilting and snow-free2

leaf_area_index

LAI Geophysical Vegetation leaf area index

mwrtm_bh

MWRTM_BH LandModel Constants Microwave radiative transfer model: H-pol. Vegetation b parameter

mwrtm_bv

MWRTM_BV LandModel Constants Microwave radiative transfer model: V-pol. Vegetation b parameter

mwrtm_clay

MWRTM_CLAY LandModel Constants Microwave radiative transfer model: Clay fraction

mwrtm_lewt

MWRTM_LEWT LandModel Constants Microwave radiative transfer model: Parameter to transform leaf area index into vegetation water content

mwrtm_omega

MWRTM_OMEGA LandModel Constants Microwave radiative transfer model: Scattering albedo

mwrtm_poros

MWRTM_POROS LandModel Constants Microwave radiative transfer model: Porosity

mwrtm_rghhmax

MWRTM_RGHHMAX LandModel Constants Microwave radiative transfer model: Maximum microwave roughness parameter

mwrtm_rghhmin

MWRTM_RGHHMIN LandModel Constants Microwave radiative transfer model: Minimum microwave roughness parameter

mwrtm_rghwmax

MWRTM_RGHWMAX LandModel Constants Microwave radiative transfer model: Soil moisture value above which minimum microwave roughness parameter is used

mwrtm_rghwmin

MWRTM_RGHWMIN LandModel Constants Microwave radiative transfer model: Soil moisture value below which maximum microwave roughness parameter is used

mwrtm_rghnrh

MWRTM_RGHNRH LandModel Constants Microwave radiative transfer model: H-pol. Exponent for rough reflectivity parameterization

mwrtm_rghnrv

MWRTM_RGHNRV LandModel Constants Microwave radiative transfer model: V-pol. Exponent for rough reflectivity parameterization

mwrtm_rghpolmix

MWRTM_RGHPOLMIX LandModel Constants Microwave radiative transfer model: Polarization mixing parameter

mwrtm_sand

MWRTM_SAND LandModel Constants Microwave radiative transfer model: Sand fraction

mwrtm_soilcls

MWRTM_SOILCLS LandModel Constants Microwave radiative transfer model: Soil class. Type is Unsigned32.

mwrtm_vegcls

MWRTM_VEGCLS LandModel Constants Microwave radiative transfer model: Vegetation class. Type is Unsigned32.

mwrtm_wangwp

MWRTM_WANGWP LandModel Constants Microwave radiative transfer model: Wang dielectric model wilting point soil moisture

mwrtm_wangwt

MWRTM_WANGWT LandModel Constants Microwave radiative transfer model: Wang dielectric model transition soil moisture

net_downward_longwave_flux

LWLAND Geophysical Net downward longwave flux over land2

net_downward_shortwave_flux

SWLAND Geophysical Net downward shortwave flux over land2

overland_runoff_flux

RUNOFF Geophysical Overland (surface) runoff (including throughflow)2

precipitation_total_surface_flux

PRECTOT Geophysical Total surface precipitation (incl. snow fall)

radiation_longwave_absorbed_flux

LWGAB Geophysical Absorbed (downward) longwave radiation at the surface

radiation_shortwave_downward_flux

SWGDN Geophysical Downward shortwave flux incident on the surface

sm_profile

PRMC Geophysical Total profile soil moisture (0 cm to model bedrock depth)

sm_profile_pctl

PRMC_PRCNTL Geophysical

Total profile soil moisture (0 cm to model bedrock depth; percentile units)

Note: There are known shortcomings in the underlying climatology, and the soil moisture fields in percentile units have not been validated.

sm_profile_wetness

GWETPROF Geophysical Total profile soil wetness (0 cm to model bedrock depth; wetness units5)

sm_profile_wetness_analysis

GWETPROF_ANA Analysis Analysis total profile soil moisture (0 cm to model bedrock depth ; wetness units5)

sm_profile_wetness_analysis_ensstd

GWETPROF_ANA_ENSSTD Analysis Uncertainty of analysis total profile soil moisture (0 cm to model bedrock depth; ensemble std-dev; wetness units5)

sm_profile_wetness_forecast

GWETPROF_FCST Forecast Catchment model forecast total profile soil moisture (0 cm to model bedrock depth; wetness units5)

sm_rootzone

RZMC Geophysical Root zone soil moisture (0-100 cm)

sm_rootzone_pctl

RZMC_PRCNTL Geophysical

Root zone soil moisture (0-100 cm; percentile units)

Note: There are known shortcomings in the underlying climatology, and the soil moisture fields in percentile units have not been validated.

sm_rootzone_wetness

GWETROOT Geophysical Root zone soil wetness (0-100 cm; wetness units5)

sm_rootzone_wetness_analysis

GWETROOT_ANA Analysis Analysis root zone soil moisture (0-100 cm; wetness units5)

sm_rootzone_wetness_analysis_ensstd

GWETROOT_ANA_ENSSTD Analysis Uncertainty of analysis root zone soil moisture (0-100 cm; ensemble std-dev; wetness units5)

sm_rootzone_wetness_forecast

GWETROOT_FCST Forecast Catchment model forecast root zone soil moisture (0-100 cm; wetness units5)

sm_surface

SFMC Geophysical Top layer soil moisture (0-5 cm)

sm_surface_wetness

GWETTOP Geophysical Top layer soil wetness (0-5 cm; wetness units5)

sm_surface_wetness_analysis

GWETTOP_ANA Analysis Analysis surface soil moisture (0-5 cm; wetness units5)

sm_surface_wetness_analysis_ensstd

GWETTOP_ANA_ENSSTD Analysis Uncertainty of analysis surface soil moisture (0-5 cm; ensemble std-dev; wetness units5)

sm_surface_wetness_forecast

GWETTOP_FCST Forecast Catchment model forecast surface soil moisture (0-5 cm; wetness units5)

snow_depth

SNODP Geophysical Snow depth within snow-covered land fraction of grid cell2

snow_mass

SNOMAS Geophysical Average snow mass (or snow water equivalent) over land fraction of grid cell2

snow_melt_flux

SNOMLT Geophysical Snowmelt2

snowfall_surface_flux

PRECSNO Geophysical Surface snow fall

soil_temp_layer1

TSOIL1 Geophysical Soil temperature in layer 1 of soil heat diffusion model

soil_temp_layer1_analysis

TSOIL1_ANA Analysis Analysis soil temperature in layer 1 of soil heat diffusion model

soil_temp_layer1_analysis_ensstd

TSOIL1_ANA_ENSSTD Analysis Uncertainty of analysis soil temperature in layer 1 of soil heat diffusion model (ensemble std-dev)

soil_temp_layer1_forecast

TSOIL1_FCST Forecast Catchment model forecast soil temperature in layer 1 of soil heat diffusion model

soil_temp_layer2

TSOIL2 Geophysical Soil temperature in layer 2 of soil heat diffusion model

soil_temp_layer3

TSOIL3 Geophysical Soil temperature in layer 3 of soil heat diffusion model

soil_temp_layer4

TSOIL4 Geophysical Soil temperature in layer 4 of soil heat diffusion model

soil_temp_layer5

TSOIL5 Geophysical Soil temperature in layer 5 of soil heat diffusion model

soil_temp_layer6

TSOIL6 Geophysical Soil temperature in layer 6 of soil heat diffusion model

soil_water_infiltration_flux

QINFIL Geophysical Soil water infiltration rate

specific_humidity_lowatmmodlay

QLML Geophysical Air specific humidity at center height of lowest atmospheric model layer

surface_pressure

PS Geophysical Surface pressure

surface_temp

TSURF Geophysical Mean land surface temperature (incl. snow-covered land area)2

surface_temp_analysis

TSURF_ANA Analysis Analysis surface temperature

surface_temp_analysis_ensstd

TSURF_ANA_ENSSTD Analysis Uncertainty of analysis surface temperature (ensemble std-dev)

surface_temp_forecast

TSURF_FCST Forecast Catchment model forecast surface temperature

tb_h_forecast

TBHCOMP_FCST Forecast Composite resolution Catchment model forecast 1.41 GHz H-pol brightness temperature4

tb_h_forecast_ensstd

TBHCOMP_FCST_ ENSSTD Forecast Uncertainty (ensemble std-dev) of tb_h_forecast4

tb_h_obs

TBHCOMP_OBS Observations

Composite resolution observed SPL1CTB H-pol brightness temperature, represented as the average of fore and aft observations from the SMAP antenna3

Note: These brightness temperature observations passed all quality control steps but could not be assimilated for lack of brightness temperature scaling parameters. For such observations, the variables tb_h_obs_assim and tb_v_obs_assim are equal to no-data values.

tb_h_obs_assim

TBHCOMP_OBS_ASSIM Observations

Assimilated value after model-based quality control and climatological adjustment (scaling) tb_h_obs3 for consistency with the land model’s seasonally varying mean brightness temperature climatology

Output for this field is only stored at times and locations for which input SMAP Level-1 or Level-2 data are assimilated. If more than one overpass occurs for a given grid cell within the assimilation window, the Level-1 or Level-2 observations from all overpasses within the analysis update time window are averaged.

Note: These brightness temperature observations passed all quality control steps but could not be assimilated for lack of brightness temperature scaling parameters. For such observations, the variables tb_h_obs_assim and tb_v_obs_assim are equal to no-data values.

tb_h_obs_errstd

TBHCOMP_OBS_ERRSTD Observations Observation error std-dev for tb_h_obs_scaled3

tb_h_obs_time_sec

TBHCOMP_OBS_TIME_SEC Observations

Time values as counts of International System (SI) seconds based on the J2000 epoch in Ephemeris Time (ET). The J2000 epoch starting point is January 1, 2000 at 12:00 ET, which translates to January 1, 2000 at 11:58:55.816 Universal Coordinated Time (UTC). Type is Float64.

Time stamps for H-polarization and V-polarization observations are provided in the fields tb_h_obs_time_sec and tb_v_obs_time_sec, respectively. If observations from more than one overpass time at the same location (grid cell) are assimilated, the observation time stamps reflect the average over the spacecraft overpass times. Furthermore, the fields tb_h_orbit_flag and tb_v_orbit_flag indicate whether the observation is exclusively from ascending orbits (orbit_flag=1), exclusively from descending orbits (orbit_flag=2), or from an average over ascending and descending orbits (orbit_flag=0). The latter case may occur at very high latitudes.

tb_h_orbit_flag

TBHCOMP_ORBFLAG Observations

Flag indicating the orbit direction of H-pol brightness temperature composite fields (tb_h_obs, tb_h_forecast, etc.): 0=average over ascending and descending orbits, 1=ascending orbits only, 2=descending orbits only, Type is Unsigned32.

Time stamps for H-polarization and V-polarization observations are provided in the fields tb_h_obs_time_sec and tb_v_obs_time_sec, respectively. If observations from more than one overpass time at the same location (grid cell) are assimilated, the observation time stamps reflect the average over the spacecraft overpass times. Furthermore, the fields tb_h_orbit_flag and tb_v_orbit_flag indicate whether the observation is exclusively from ascending orbits (orbit_flag=1), exclusively from descending orbits (orbit_flag=2), or from an average over ascending and descending orbits (orbit_flag=0). The latter case may occur at very high latitudes.

tb_h_resolution_flag

TBHCOMP_RESFLAG Observations

Flag indicating the effective resolution of H-pol brightness temperature composite fields (tb_h_obs, tb_h_forecast, etc.): 1=36 km, 2=9 km. Type is Unsigned32.

The fields tb_h_resolution_flag and tb_v_resolution_flag indicate whether the model forecast brightness temperature for a given grid cell corresponds to a 36 km observation from the SPL1CTB product. Model forecast brightness temperatures that correspond to 36 km observations from the SPL1CTB product are aggregated from 9 km to 36 km and then posted at 9 km for convenience. Brightness temperature output is only stored at times and locations for which input SPL1CTB brightness temperature data are assimilated. If more than one overpass occurs for a given grid cell within the assimilation window, the latest overpass time prevails.

tb_v_forecast

TBVCOMP_FCST Forecast Composite resolution Catchment model forecast 1.41 GHz V-pol brightness temperature4

tb_v_forecast_ensstd

TBVCOMP_FCST_ENSSTD Forecast Uncertainty (ensemble std-dev) of tb_v_forecast4

tb_v_obs

TBVCOMP_OBS Observations

Composite resolution observed SPL1CTB V-pol brightness temperature, represented as the average of fore and aft observations from the SMAP antenna3

Output for this field is only stored at times and locations for which input SMAP Level-1 or Level-2 data are assimilated. If more than one overpass occurs for a given grid cell within the assimilation window, the Level-1 or Level-2 observations from all overpasses within the analysis update time window are averaged.

Note: These brightness temperature observations passed all quality control steps but could not be assimilated for lack of brightness temperature scaling parameters. For such observations, the variables tb_h_obs_assim and tb_v_obs_assim are equal to no-data values.

tb_v_obs_assim

TBVCOMP_OBS_ASSIM Observations

Assimilated value after model-based quality control and climatological adjustment (scaling) of tb_v_obs3 for consistency with the land model’s seasonally varying mean brightness temperature climatology

Note: These brightness temperature observations passed all quality control steps but could not be assimilated for lack of brightness temperature scaling parameters. For such observations, the variables tb_h_obs_assim and tb_v_obs_assim are equal to no-data values.

tb_v_obs_errstd

TBVCOMP_OBS_ERRSTD Observations Observation error std-dev for tb_v_obs_scaled3

tb_v_obs_time_sec

TBVCOMP_OBS_TIME_SEC Observations

Time values as counts of International System (SI) seconds based on the J2000 epoch in Ephemeris Time (ET). The J2000 epoch starting point is January 1, 2000 at 12:00 ET, which translates to January 1, 2000 at 11:58:55.816 Universal Coordinated Time (UTC). Type is Float64.

Time stamps for H-polarization and V-polarization observations are provided in the fields tb_h_obs_time_sec and tb_v_obs_time_sec, respectively. If observations from more than one overpass time at the same location (grid cell) are assimilated, the observation time stamps reflect the average over the spacecraft overpass times. Furthermore, the fields tb_h_orbit_flag and tb_v_orbit_flag indicate whether the observation is exclusively from ascending orbits (orbit_flag=1), exclusively from descending orbits (orbit_flag=2), or from an average over ascending and descending orbits (orbit_flag=0). The latter case may occur at very high latitudes.

tb_v_orbit_flag

TBVCOMP_ORBFLAG Observations

Flag indicating the orbit direction of V-pol brightness temperature composite fields (tb_v_obstb_v_forecast, etc.): 0=average over ascending and descending orbits, 1=ascending orbits only, 2=descending orbits only. Type is Unsigned32.

Time stamps for H-polarization and V-polarization observations are provided in the fields tb_h_obs_time_sec and tb_v_obs_time_sec, respectively. If observations from more than one overpass time at the same location (grid cell) are assimilated, the observation time stamps reflect the average over the spacecraft overpass times. Furthermore, the fields tb_h_orbit_flag and tb_v_orbit_flag indicate whether the observation is exclusively from ascending orbits (orbit_flag=1), exclusively from descending orbits (orbit_flag=2), or from an average over ascending and descending orbits (orbit_flag=0). The latter case may occur at very high latitudes.

tb_v_resolution_flag

TBVCOMP_RESFLAG Observations

Flag indicating the effective resolution of V-pol brightness temperature composite fields (tb_v_obstb_v_forecast, etc..): 1=36 km, 2=9 km. Type is Unsigned32. The fields tb_h_resolution_flag and tb_v_resolution_flag indicate whether the model forecast brightness temperature for a given grid cell corresponds to a 36 km observation from the SPL1CTB product. Model forecast brightness temperatures that correspond to 36 km observations from the SPL1CTB product are aggregated from 9 km to 36 km and then posted at 9 km for convenience. Brightness temperature output is only stored at times and locations for which input SPL1CTB brightness temperature data are assimilated. If more than one overpass occurs for a given grid cell within the assimilation window, the latest overpass time prevails.

temp_lowatmmodlay

TLML Geophysical Air temperature at center height of lowest atmospheric model layer
time TIME [All Data groups]1

Time accrued since 2000-01-01 11:58:55.816. Type is 64-bit floating-point and array is one dimensional.

vegetation_greenness_fraction

GRN Geophysical Vegetation “greenness” or fraction of transpiring leaves averaged over the land area2 of the grid cell.

windspeed_lowatmmodlay

SPEEDLML Geophysical Surface wind speed at center height of lowest atmospheric model layer
x projection_x_coordinate [All Data groups]1 The x coordinate values from the cylindrical 9 km Earth-fixed EASE-Grid 2.0 projection
y projection_y_coordinate [All Data groups]1 The y coordinate values from the cylindrical 9 km Earth-fixed EASE-Grid 2.0 projection

1 The time and space coordinate data sets are stored in the HDF5 root data group, not in any particular group (i.e Geophysical_Data). 

2 Excluding areas of open water and permanent ice. Output is only stored at times and locations for which input SMAP Level-1 or Level-2 data are assimilated. If more than one overpass occurs for a given grid cell within the assimilation window, output represents average over all overpass times.

3 Observed brightness temperatures that originate from 36 km SPL1CTB files are posted at 9 km here for convenience (as average over fore and aft brightness temperature if stored separately in SPL1CTB product).

4 Model forecast brightness temperatures that correspond to 36 km observations from the SPL1CTB product are aggregated from 9 km to 36 km and then posted at 9 km for convenience.

5 Soil wetness units (dimensionless) vary between 0 and 1, indicating relative saturation between completely dry conditions and completely saturated conditions, respectively.
Soil moisture output in the Geophysical Data (gph) group is provided in three different units:

  • m3/m3 (or volumetric percent): the volume of water / total volume of soil including solids, water, and air
  • dimensionless wetness units (or relative saturation): volume of water / volume of pore space
  • percentile units: root zone and profile soil moisture only (Note: There are known shortcomings in the underlying climatology, and the soil moisture fields in percentile units have not been validated).

Soil moisture output in the Analysis Update (aup) group is provided only in m3/m3 (volumetric percent); for applications, the gph output is likely more appropriate. For more details, refer to Appendix D (page 81) of the Product Specification Document (Reichle et al. 2015a).

Two-Dimensional Arrays

All SPL4SM HDF5 data fields have /cell_lat /cell_lon shape. This shape is a two-dimensional array, where each data field represents a specific grid cell in the 9 km global cylindrical EASE-Grid 2.0 as specified by the cell_lat and cell_lon arrays. For example, the field surface_temp (234,789) represents the land surface temperature of the grid cell located at cell_lat (234,789) and cell_lon (234,789), where cell_row (234,789)=234 and cell_column (234,789)=789.

Fill/Gap Values 

SMAP data products employ fill and gap values to indicate when no valid data appear in a particular data field and ensure that data fields retain the correct shape. Gap values locate portions of a data stream that do not appear in the output data file. Yet because the SPL4SM data product is partially based on modeling, gaps are not expected to occur in the SPL4SM data stream. Note, however, that there might well be 3-hour intervals for which no SMAP data were assimilated. This situation would be reflected in the aup collection when the total number of assimilated observations for the time interval in question is zero.

Fill values appear in the SPL4SM data product over ocean and water surfaces or for variables that are not meaningful (such as snow temperatures in the absence of snow). Fill values are also used, for example, in the aup file collection for all grid cells for which SMAP observations were not assimilated. The latter may occur for any of the following circumstances:

  • There was no SMAP overpass for the grid cell in question during the assimilation time window.
  • The SMAP observations were not available due to quality control, missing science or engineering input data, or any other reason in the Level-1, -2, or -3 processing algorithms.
  • The SMAP observations were rejected for assimilation due to quality control by the SPL4SM algorithm.

SMAP data products employ a specific set of data values to connote that a field is fill. The selected values that represent fill are dependent on the data type.

No valid value in the SPL4SM data product is equal to the values that represent fill. If any exceptions should exist in the future, the SPL4SM content will provide a means for users to discern between fields that contain fill and fields that contain genuine data values.

Notations 

Table A7 lists the acronyms and abbreviations used in this document.

Table A7. Acronyms and Abbreviations
Abbreviation Definition
Char 8-bit character
IGBP International Geosphere-Biosphere Programme
Int8 8-bit (1-byte) signed integer
Int16 16-bit (2-byte) signed integer
Int32 32-bit (4-byte) signed integer
Float32 32-bit (4-byte) floating-point integer
Float64 64-bit (8-byte) floating-point integer
H-pol Horizontally polarized
N/A Not Applicable
NF Number of frozen ground pixels
NL Number of water pixels
NT Number of thawed land pixels
NW Number of land pixels
SI International System of Units
SPS Science Production Software
T1, T2 Threshold 1, Threshold 2
TB Brightness Temperature
Uint8 8-bit (1-byte) unsigned integer
Uint16 16-bit (2-byte) unsigned integer
UTC Universal Coordinated Time
V-pol Vertically polarized
VWC Vegetation Water Content

How To

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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
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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
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How to Import SMAP HDF Data Into ArcGIS
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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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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
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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