SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM)
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
Earthdata Search: This application allows you to search, visualize, and access data across thousands of Earth science data sets. Additional customization services are available for select data sets, including subsetting, reformatting, and reprojection.
Subscription Service: Subscribe to have new data automatically delivered to you as they become available at NSIDC. Subscriptions apply only to future data as they are delivered to NSIDC; they cannot be used to receive data already in NSIDC's archive.
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 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants, 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/KGLC3UH4TMAQ. [Date Accessed].
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.
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.
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.
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.
Three basic time steps are involved in the generation of the Level-4 soil moisture products, including:
The land model computational time step (7.5 minutes)
The Ensemble Kalman Filter (EnKF) analysis update time step (3 hours)
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:
GEOS-5 Catchment Land Surface and Microwave Radiative Transfer Model
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.
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.
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.
Version History
Table 3. Summary of Version Changes
Version
Date
Version Changes
V1
October 2015
First public data release
V2
April 2016
Changes to this version include:
Transitioned to Validated-Stage 2
Using updated SPL1CTB V3 Validated data as input
Minor bug fixes
V3
July 2017
Changes to this version include:
SMAP observations are now assimilated in Eastern Europe, the Middle East, and East Asia due to expanded coverage of the brightness temperature scaling parameters. The latter are based on two years of SMAP Version 3 brightness temperature observations where the SMOS climatology is unavailable due to RFI.
An improved version of the model-only Nature Run (NRv4.1) simulation is used to derive the brightness temperature scaling parameters, the model soil moisture initial conditions, and the soil moisture climatology.
Minor bug fixes.
V4
June 2018
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.
Rolf H. Reichle, Randal Koster, Qing Liu
NASA Goddard Space Flight Center
Greenbelt, MD
Gabrielle De Lannoy
KU Leuven
Department of Earth and Environmental Sciences
Heverlee, Belgium
Wade Crow
Hydrology and Remote Sensing Lab
US Department of Agriculture/Agricultural Research Service (USDA ARS)
Beltsville, MD
John Kimball
Numerical Terradynamic Simulation Group
University of Montana
Missoula, MT
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 Data, SMAP 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, gph, aup, 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 theaup collection.
Geophysical_Data
Table A1 describes the data fields in the Geophysical_Data group stored in thegph 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 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 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 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 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
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
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_obs, tb_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_obs, tb_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.
Programmatic access to data with services such as subsetting, reformatting, and reprojection
This article provides a step-by-step getting started guide to utilizing an Application Programming Interface, or API, for programmatic access to data from the NSIDC Distributed Active Archive Center (DAAC) based on spatial and temporal filters, as well as subsetting, reformatting, and... 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 search, order, and customize SMAP data using Earthdata Search?
In this step-by-step tutorial, we will demonstrate how to search, order, and customize NASA Soil Moisture Active Passive, or SMAP data using the NASA Earthdata Search application. NASA Earthdata search provides an interactive map-based search environment where you can filter your results based on... read more
How 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
How to learn more about SMAP ancillary data
SMAP Ancillary data sets are used to produce SMAP Level-1, -2, -3, and -4 standard data products. Several of these ancillary data sets are produced by external organizations, such as NOAA, the NASA Global Modeling and Assimilation... read more
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?