Data Set ID:

SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State, Version 3

This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

This is the most recent version of these data.

Version Summary:

Changes to this version include:

  • Transitioned to Validated-Stage 2
  • Uses SPL1CS0 V3 Validated data as input
  • Using full swath except for nadir-anomaly flagged areas to provide better coverage
  • Resolved some flag and fillValue errors

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

  • Snow/Ice > Freeze/Thaw > Freeze/Thaw State
  • Radar > Radar Backscatter > Sigma Nought
  • Snow/Ice > Freeze/Thaw > Transition Direction
Data Format(s):
  • HDF5
Spatial Coverage:
N: 85.044, 
S: 45, 
E: 180, 
W: -180
Platform(s):SMAP Observatory
Spatial Resolution:
  • 3 km x 3 km
Sensor(s):SMAP L-Band Radar
Temporal Coverage:
  • 13 April 2015 to 7 July 2015
Temporal Resolution1 dayMetadata XML:View Metadata Record
Data Contributor(s):Dunbar, R. S., X. Xu, A. Colliander, C. Derksen, K. C. McDonald, E. Podest, E. G. Njoku, J. S. Kimball, and Y. Kim.

Geographic Coverage

Other Access Options

Other Access Options


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.

Dunbar, R. S., X. Xu, A. Colliander, C. Derksen, K. C. McDonald, E. Podest, E. G. Njoku, J. S. Kimball, and Y. Kim. 2016. SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State, Version 3. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].
3 January 2019
Last modified: 
21 February 2019

Data Description


Freeze/thaw state and freeze/thaw transition direction derived from sigma nought measurements are output on a fixed 3 km EASE-Grid 2.0. The SMAP L-Band Radar measures the backscatter coefficient, or sigma nought (sigma0), which is the normalized measure of the strength of a radar signal reflected back to the antenna. Sigma nought, also a parameter of this product, is derived using Synthetic-Aperture Radar (SAR) processing.

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

File Information


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

File Contents

As shown in Figure 1, each HDF5 file is organized into the following main groups, which contain additional groups and/or data sets:

Figure 1. Subset of File Contents
For a complete list of file contents for the SMAP Level-3 freeze/thaw product, refer to the Data Fields page. 

Data Fields

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

Data element arrays are three dimensional, with the exception of transition_direction and transition_state_flag arrays, which are two dimensional. All arrays have 6000 rows and 6000 columns in each a.m. and p.m. layer. For the a.m./p.m. index of the array, the a.m. layer is assigned to the index value 0 and the p.m. layer is assigned to index value 1.

Ancillary Data

Includes all ancillary data, such as landcover classification and open water body fraction.

Freeze/Thaw Retrieval Data

Includes freeze/thaw data and quality assessment flags.

Radar Data

Includes all radar data, such as cross-polarized sigma nought (σ0, also referred to as sigma0) data, and quality assessment flags.

Metadata Fields

Includes all metadata that describe the full content of each file. For a description of all metadata fields for this product, refer to the Metadata Fields document.

File Naming Convention

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


For example:



Table 1. File Naming Conventions
Variable Description
SMAP Indicates SMAP mission data
L3_FT_A Indicates specific product (L3: Level-3; FT: Freeze/Thaw; A: Active)
yyyymmdd 4-digit year, 2-digit month, 2-digit day; date in Universal Coordinated Time (UTC) of the first data element that appears in the product.
RLVvvv Composite Release ID, where:
R Release
L Launch Indicator (1: Post-launch standard data)
V 1-Digit Major Version Number
vvv 3-Digit Minor Version Number
Example: R13171 indicates a standard data product with a version of 3.171. Refer to the SMAP Data Versions page for version information.
NNN Number of times the file was generated under the same version for a particular date/time interval (002: 2nd time)
.[ext] File extensions include:
.h5 HDF5 data file
.qa Quality Assurance file
.xml XML Metadata file

File Size

Each file is approximately 673 MB.

File Volume

The daily data volume is approximately 673 MB.

Spatial Information


Coverage for this data set spans the Northern Hemisphere for all land regions north of 45°N latitude, and from 180°W to 180°E. The gap in coverage at both the North Pole, called a pole hole, has a radius of approximately 400 km. The swath width is 1000 km, enabling nearly complete coverage of the Northern Hemisphere every three days.

Spatial Coverage Map

Figure 2 shows the spatial coverage of this data set.

Figure 2. Spatial Coverage Map.


The native spatial resolution of the radar footprint is 1 km. Data are then gridded using the 3 km EASE-Grid 2.0 Northern Hemisphere azimuthal projection.

EASE-Grid 2.0

These data are provided on the Northern Hemisphere azimuthal EASE-Grid 2.0 (Brodzik et al. 2012). Each grid cell has a nominal area of approximately 3 x 3 km2regardless of longitude and latitude. Using this projection, all data arrays have dimensions of 6000 rows and 6000 columns.

EASE-Grid 2.0 has a flexible formulation. By adjusting a single scaling parameter, a family of multi-resolution grids that nest within one another can be generated. The nesting can be adjusted so that smaller grid cells can be tessellated to form larger grid cells. Figure 3 shows a schematic of the nesting.

This feature of perfect nesting provides SMAP data products with a convenient common projection for both high-resolution radar observations and low-resolution radiometer observations, as well as for their derived geophysical products. For more on EASE-Grid 2.0, refer to the EASE-Grid 2.0 Format Description.

Figure 3. Perfect Nesting in EASE-Grid 2.0

Temporal Information


Coverage spans from 13 April 2015 through 07 July 2015.

Note: Temporal coverage for this data set is limited due to the premature failure of the SMAP L-Band Radar. On 07 July 2015, the radar stopped transmitting due to an anomaly involving the instrument's high-power amplifier (HPA). For details, refer to the SMAP News Release issued 02 September 2015 by the Jet Propulsion Laboratory (JPL).

Satellite and Processing Events

Due to instrument maneuvers, data downlink anomalies, data quality screening, and other factors, small gaps in the SMAP time series will occur. Details of these events are maintained on two master lists:

SMAP On-Orbit Events List for Instrument Data Users
Master List of Bad and Missing Data


Each Level-3 file is a daily composite of half-orbit files/swaths.

To ensure complete coverage of the freeze/thaw domain in each daily file, a.m. and p.m. data for the current day are combined with a.m. and p.m. data from previous days. A maximum of three days of past data is used, and is necessary only near the southern margin of the freeze/thaw domain.

Data Acquisition and Processing


The SPL3FTA product is derived using a temporal change detection approach that has been previously developed and successfully applied using time-series satellite remote sensing radar backscatter and radiometric brightness temperature data from a variety of sensors and spectral wavelengths. The approach is to identify the landscape Freeze/Thaw (F/T) transition by identifying the temporal response of backscatter to changes in the dielectric constant of the landscape components that occur as the water within the components transitions between frozen and non-frozen conditions.

Classification algorithms assume that the large changes in dielectric constant occurring between frozen and non-frozen conditions dominate the corresponding backscatter temporal dynamics across the seasons, rather than other potential sources of temporal variability such as changes in canopy structure and biomass or large precipitation events. This assumption is valid for most areas of the terrestrial cryosphere.


SMAP Level-3 radar freeze/thaw data (SPL3FTA) are derived from SMAP High-Resolution Radar Sigma Nought, Version 3 (SPL1CS0) data.

Derivation Techniques and Algorithms

This section has been adapted from Dunbar et al. (2014), the Algorithm Theoretical Basis Document (ATBD) for this data set.

SMAP Level-3 radar freeze/thaw data set is derived from SMAP High-Resolution Radar Sigma Nought, Version 1 (SPL1CS0) data set. The derivation of freeze/thaw from SMAP sigma nought measurements occurs during an intermediate Level-2 processing step of the input Level-1 sigma nought data. During the Level-2 processing step, the F/T algorithm utilizes a seasonal threshold approach to convert SMAP sigma nought measurements to F/T state. Refer to Figure 4.

Figure 4. Processing sequence for generation of the L3 F/T product and the binary F/T state flag used in generation of SMAP soil moisture products.

Baseline Algorithm

The seasonal threshold baseline algorithm for SPL3FTA examines the time-series progression of the remote sensing signature relative to signatures acquired during seasonal reference frozen and thawed states. A seasonal scale factor D(t) is defined for an observation acquired at time t as:

D(t)= s(t) - sfr/sth - sfr 

where s(t) is the measurement acquired at time t, for which a F/T classification is sought, and sfr and sth are backscatter measurements corresponding to the frozen and thawed reference states, respectively. A major component of the SMAP baseline algorithm development involved the application of existing satellite L-band radar measurements from the Aquarius/SAC-D mission over the F/T domain to develop pre-launch maps of sfr and sth. The thaw reference (sth) was replaced with the average of the last ten days of SMAP radar data (27 June through 6 July 2015). The new freeze reference (sfr) was derived based on the assumption that the sth reference difference between SMAP and the pre-launch Aquarius values is the same for the freeze case.

A threshold level T is then defined such that:
D(t) > T
D(t) <=T

defines the thawed and frozen landscape states, respectively. This series of equations is run on a cell-by-cell basis for unmasked portions of the F/T domain. The output is a dimensionless binary state variable designating either frozen or thawed condition for each unmasked grid cell. The parameter T was fixed at 0.5 across the entire F/T domain at the start of the SMAP mission. Given the short operating period of the SMAP radar, post-launch optimization experiments were limited to the spring 2015 freeze to thaw transition, which has been evaluated using in situ measurements from the Calibration/Validation (Cal/Val) network.

Ancillary Data

Ancillary data sets are used to:

  1. Support initialization of the thresholds employed in the algorithm,
  2. Set flags that indicate potential problem regions, and
  3. Define masks where no retrievals should be performed.

Ancillary data used in SPL3FTA processing includes data sets of inland open water, permanent ice and snow, and urban areas are used to derive masks so that no retrievals occur over these regions. Ancillary data sets of mountainous areas, fractional open water cover, and precipitation are used to derive flags so that a confidence interval can be associated with the retrieval. A primary source for each of the above ancillary parameters has been selected. This data set is common to all algorithms using that specific parameter. For SPL3FTA Validated data, the lake fraction has been fixed at 50%. Surface soil temperature and 2-meter air temperature from the NASA Global Modeling and Assimilation Office (GMAO) are used offline for optimization of the retrieval thresholds. Measurements from dense and sparse in situ networks have been utilized for error analysis and Cal/Val, and described further in Dunbar et al. (2014). Table 2 lists the ancillary data employed in support of the SPL3FTA product.

Table 2. Input Ancillary Data for SPL3FTA
Data Type Data Source Frequency Resolution Extent Use
Vegetation Type MODIS-International Geosphere Biosphere Programme (IGBP) Once 250 m Global Sensitivity Analysis
Land Surface and 2 m Air Temperature
MERRA and Station Data Daily or close
to time of
25 km and
point data
Global Algorithm
Parameterization (offline)
ECMWF Forecasts Time of
0.25 degrees Global Flag
Static Water Bodies MODIS44W Once 250 m Global Flag
Transient Water Bodies SMAP L2_SM_A As processed 3 km Global Flag
Mountainous Areas NASA Global DEM Once 30 m Global Flag
Permanent Ice and Snow MODIS-IGBP Permanent Ice and Snow Class Once 500 m Global Flag
Urban Areas Global Urban Mapping Project (GRUMP) Once 1 km Global Flag

For more information, refer to the ATBD, Section 4: Retrieval Algorithms (Dunbar et al. 2014).


This product is generated by the SMAP Science Data Processing System (SDS) at the Jet Propulsion Laboratory (JPL) in Pasadena, California USA. To generate this product, the processing software ingests one day's worth of Level-2 files and creates individual Northern Hemisphere composites as two-dimensional or three-dimensional arrays for each output parameter defined in the intermediate Level-2 data. To ensure complete coverage of the freeze/thaw domain in each daily file, a.m. and p.m. data for the current day are combined with a.m. and p.m. data from previous days. A maximum of three days of past data is used, and is necessary only near the southern margin of the freeze/thaw domain.

Wherever data overlap occurs, as is typical at high latitudes, data which were acquired closest to 6:00 a.m. and 6:00 p.m. local solar times are chosen. The intermediate Level-2 data distinguish four levels of freeze/thaw conditions determined from the ascending 6:00 a.m. and descending 6:00 p.m. SPL1CS0 data, including frozen (from both a.m. and p.m. overpass times), non-frozen (a.m. and p.m.), transitional (a.m. frozen; p.m. non-frozen) and inverse-transitional (a.m. non-frozen; p.m. frozen) states.

For more information on each portion of the algorithm processing flow, refer to the ATBD for this product, Section 2.2: L3_FT_A Production (Dunbar et al. 2014).

Quality, Errors, and Limitations

Error Sources

Anthropogenic Radio Frequency Interference (RFI), principally from ground-based surveillance radars, can contaminate both radar and radiometer measurements at L-band. Early measurements and results from ESA's Soil Moisture and Ocean Salinity (SMOS) mission indicate that in some regions RFI is present and detectable. The SMAP radar and radiometer electronics and algorithms include design features to mitigate the effects of RFI. The SMAP radar utilizes selective filters and an adjustable carrier frequency to tune to predetermined RFI-free portions of the spectrum while on orbit.

The landscape freeze/thaw state retrieval represented by the SPL3FTA algorithm and product characterizes the predominant frozen or non-frozen state of the land surface within the sensor Field of View (FOV) and does not distinguish freeze/thaw characteristics among different landscape elements, including surface snow, soil, open water or vegetation. The low frequency L-band SAR retrievals from SMAP are expected to have sensitivity to surface soil freeze/thaw conditions under low to moderate vegetation cover, but effective radar penetration depth and microwave freeze/thaw sensitivity is strongly constrained by intervening vegetation biomass, soil moisture levels, and snow wetness. Ambiguity in relating changes in the radar signal to these specific landscape components is a challenge to validation of the F/T product.

The SMAP seasonal threshold freeze/thaw classification algorithm requires the establishment of accurate and stable frozen and non-frozen reference state backscatter conditions for each 3 km resolution grid cell. Initial reference conditions were established pre-launch from relatively coarse (approximately 100 km) resolution Aquarius/SAC-D satellite L-band scatterometer measurements. The Aquarius data have a different sensor geometry and sampling, and a much coarser FOV than SMAP. Hybrid SMAP/Aquarius-derived references were utilized for the Version 2 Beta release and for this validated release, due to the demise of the SMAP radar on 07 July 2015. The thaw references were derived from SMAP data covering the period from 27 June to 07 July 2015. Using Aquarius (thaw-freeze) reference differences, the hybrid SMAP/Aquarius frozen references were derived using the formula:

  (Equation 1)

The resulting freeze/thaw reference conditions determined from these data may cause some SMAP freeze/thaw classification error, especially for areas with substantial sub-grid scale freeze/thaw heterogeneity relative to the coarse Aquarius FOV.

A major assumption of the seasonal threshold-based temporal dB change freeze/thaw classification is that the major temporal shifts in radar backscatter are caused by land surface dielectric changes from temporal freeze/thaw transitions. This assumption generally holds for higher latitudes and elevations where seasonal frozen temperatures are a significant part of the annual cycle and a large constraint to land surface water mobility and ecosystem processes (Kim et al. 2012). However, freeze/thaw classification accuracy is expected to be reduced where other environmental factors may cause large temporal shifts in radar backscatter, including large rainfall events and surface inundation, and abrupt changes in vegetation biomass such as phenology, disturbance and land cover change.

More information about error sources is provided in the ATBD for this product (Dunbar et al. 2014).

Quality Assessment

For in-depth details regarding the quality of these Version 2 Beta data, refer to the following reports: 
Validation Assessment Report
Beta Assessment Report

Quality Overview

SMAP products provide multiple means to assess quality. Each product contains bit flags, uncertainty measures, and file-level metadata that provide quality information. For information regarding the specific bit flags, uncertainty measures, and file-level metadata contained in this product, refer to the Data Fields and Metadata Fields documents.

Each HDF5 file contains metadata with Quality Assessment (QA) metadata flags that are set by the Science Data Processing System (SDS) at the JPL prior to delivery to the National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). A separate metadata file with an .xml file extension is also delivered to NSIDC DAAC with the HDF5 file; it contains the same information as the HDF5 file-level metadata.

A separate QA file with a .qa file extension is also associated with each data file. QA files are ASCII text files that contain statistical information in order to help users better assess the quality of the associated data file.

If a product does not fail QA, it is ready to be used for higher-level processing, browse generation, active science QA, archive, and distribution. If a product fails QA, it is never delivered to NSIDC DAAC.



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

Software and Tools

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

Version History

Document Creation Date

October 2015

Document Revision Date

April 2016

Related Data Sets

SMAP Data at NSIDC | Overview

SMAP Radar Data at the ASF DAAC

Related Websites


Contacts and Acknowledgments


Scott Dunbar, Xiaolan Xu, Andreas Colliander, Eni Njoku, 
Kyle McDonald, Erika Podest

Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109 USA

Chris Derksen
Climate Research Division 
Environment Canada
Toronto, ON M3H 5T4 Canada

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


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.

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.

Entekhabi, D. et al. 2014. SMAP Handbook–Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw from Space. Pasadena, CA USA: SMAP Project, JPL CL#14-2285, Jet Propulsion Laboratory.

Derksen, C., X. Xu,, R. S. Dunbar, and A. Colliander. 2016. Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L3_FT_A Validated-Release Data Product (Version 3).SMAP Project, JPL D-93721. Jet Propulsion Laboratory, Pasadena, CA. (PDF, 1.4 MB)

Derksen, C., X. Xu, R. S. Dunbar, A. Colliander, J. Kimball, and Y. Kim. 2015. Calibration and Validation for the L3_FT_A Beta-Release Data Product. SMAP Project, D-93983. Jet Propulsion Laboratory, Pasadena, CA. (PDF, 7.7 MB)

Dunbar, S., X. Xu, A. Colliander, C. Derksen, K. McDonald, E. Podest. E .Njoku, J. Kimball, and Y. Kim. 2014. Algorithm Theoretical Basis Document (ATBD): SMAP level 3 radar freeze/thaw data product (L3_FT_A). SMAP Project, Jet Propulsion Laboratory, Pasadena, CA. (PDF, 4.8 MB)

Dunbar, S. 2015. SMAP Level 3 Freeze-Thaw (L3_FT_A) Product Specification Document. SMAP Project, JPL D-72549. Jet Propulsion Laboratory, Pasadena, CA. (PDF, 0.5 MB)

Frolking S., K. McDonald, J. Kimball, R. Zimmermann, J. B. Way and S. W. Running. 1999. Using the space-borne NASA Scatterometer (NSCAT) to determine the frozen and thawed seasons of a boreal landscape. Journal of Geophysical Research, 104(D22), 27,895-27,907.

Kim, Y., J. S. Kimball, K. Zhang, and K. C. McDonald, 2012. Satellite detection of increasing northern hemisphere non-frozen seasons from 1979 to 2008: implications for regional vegetation growth. Remote Sensing of Environment, 121, 472–487.

Kim, Y., J. S. Kimball, J. Glassy, and K. C. McDonald. 2014. MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status. Version 3. [indicate subset used]. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center.

Kim, Y., J.S. Kimball, K.C. McDonald and J. Glassy, 2011. Developing a global data record of daily landscape freeze/thaw status using satellite passive microwave remote sensing. IEEE Transactions on Geoscience and Remote Sensing 49, 949-960.

Kimball, J., K. McDonald, A. Keyser, S. Frolking, and S. Running. 2001. Application of the NASA Scatterometer (NSCAT) for Classifying the Daily Frozen and Non-Frozen Landscape of Alaska, Remote Sensing of Environment, 75, 113-126.

Kimball, J.S., K.C. McDonald, S.W. Running, and S. Frolking. 2004a. Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sensing of Environment, 90, 243-258.

Kimball, J.S., M. Zhao, K.C. McDonald, F.A. Heinsch, and S. Running. 2004b. Satellite observations of annual variability in terrestrial carbon cycles and seasonal growing seasons at high northern latitudes. In Microwave Remote Sensing of the Atmosphere and Environment IV, G. Skofronick Jackson and S. Uratsuka (Eds.), Proceedings of SPIE – The International Society for Optical Engineering, 5654, 244-254.

McDonald, K.C., J.S. Kimball, E. Njoku, R. Zimmermann, and M. Zhao. 2004. Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interactions, 8(20), 1-23.

Podest, E., K.C. McDonald, and J.S. Kimball. 2014. Multi-sensor microwave sensitivity to freeze-thaw dynamics across a complex boreal landscape. Transactions in Geoscience and Remote Sensing, 52, 6818-6828.

Rawlins, M.A, K.C. McDonald, S. Frolking, R.B. Lammers, M. Fahnestock, J.S. Kimball, C.J. Vorosmarty. 2005. Remote Sensing of Pan-Arctic Snowpack Thaw Using the SeaWinds Scatterometer, Journal of Hydrology, 312/1-4, 294-311.

Rignot E., and Way, J.B. 1994. Monitoring freeze-thaw cycles along north-south Alaskan transects using ERS-1 SAR, Remote Sensing of Environment, 49, 131-137. 

Rignot, E., Way, J.B., McDonald, K., Viereck, L., Williams, C., Adams, P., Payne, C., Wood, W., and Shi, J. 1994. Monitoring of environmental conditions in taiga forests using ERS-1 SAR, Remote Sensing of Environment, 49, 145-154.

Way, J. B., R. Zimmermann, E. Rignot, K. McDonald, and R. Oren. 1997. Winter and Spring Thaw as Observed with Imaging Radar at BOREAS, Journal of Geophysical Research, 102(D24), 29673-29684.

Wismann, V. 2000. Monitoring of seasonal thawing in Siberia with ERS scatterometer data. IEEE Transactions on Geoscience and Remote Sensing, 38, 1804–1809.

How To

Programmatically access to data with services such as subsetting, reformatting, and reprojection
This article provides a step-by-step getting started guide to utilizing 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 do I visualize SMAP data in Worldview?
This video tutorial provides step-by-step instructions on how to visualize SMAP data in Worldview ( NASA Worldview is a map-based application... read more
How do I search, order, and customize SMAP data using Earthdata Search?
In this step-by-step tutorial, we will demonstrate how to search, order, and customize NASA Soil Moisture Active Passive, or SMAP data using the NASA Earthdata Search application. NASA Earthdata search provides an interactive map-based search environment where you can filter your results based on... read more
How do I 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
How to visualize SMAP WMS layers with ArcGIS and Google Earth
NASA's Global Imagery Browse Services (GIBS) provides up to date, full resolution imagery for selected SMAP data sets. Adding GIBS layers via OGC methods, such as Web Map Service (WMS), Web Map Tile Service (WMTS) and Tiled Web Map Service (TWMS) provides an easy way to visualize the entire time... read more


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 and a Data Subscription... read more