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    SMAP Handbook

    “A rare characteristic of the SMAP Project is its emphasis on serving both basic Earth System science as well as applications in operational and practice-oriented communities.”

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    Contents of Full Handbook

    1. Introduction and Background
    2. Mission Overview
    3. Instrument Design and L1 Data Products
    4. Soil Moisture Data Products
    5. Value-Added L4_SM Soil Moisture Product
    6. Carbon Cycle Data Products
    7. Science Data Calibration and Validation
    8. NASA SMAP Applications Program
    9. SMAP Project Bibliography

     

    SMAP Handbook Excerpts on ASF’s Roles

     
    The Alaska Satellite Facility (ASF) is one of four ground stations that support the SMAP mission and one of two NASA DAACs that distribute SMAP data. The SMAP baseline science data products will be generated within the project’s Science Data System and made available publicly through the two NASA-designated Earth-science data centers. The ASF Synthetic Aperture Radar (SAR) Distributed Active Archive Center (DAAC) will provide Level 1 radar products, and the National Snow and Ice Data Center (NSIDC) DAAC will provide all other products. The excerpts below from the SMAP Handbook focus on ASF’s roles. Read more about ASF

    Ground Data System (GDS)
    The primary path for commanding the SMAP observatory and returning science and engineering data is through three northern-hemisphere tracking stations and one southern-hemisphere station in Antarctica. Data return at the northern-hemisphere stations is via 11.3-m antennas located at Wallops, Virginia (WGS), Fairbanks, Alaska (ASF), and Svalbard Island, Norway (SGS). Data return at the southern-hemisphere station is via the 10-m antenna at McMurdo Station, Antarctica (MGS). The table below gives characteristics of the four stations and average contact statistics from the science orbit. Because SMAP is in a near-polar orbit, the higher latitude stations have more frequent contact opportunities.

     

    Ground StationAntennaLatitudeAverage # of Contacts per day*Average Coverage Minutes/day*
    Svalbard (SGS) Norway11.3 m78.2ºN10.388.3
    Fairbanks (ASF) Alaska11.3 m64.9ºN6.853.7
    Wallops (WGS) Virginia11.3 m37.9ºN3.325.8
    McMurdo (MGS) Antarctica10.0 m77.8ºS10.490.7

     

    ASF DAAC Support of NASA Missions
    The ASF DAAC provides support for NASA and NASA-partner missions assigned to it by the Earth Science Data and Information System (ESDIS) Project. The ASF DAAC has extensive experience managing diverse airborne and spaceborne mission data, working with various file formats, and assisting user communities to further the use of SAR data.

    These efforts are facilitated, in part, by ASF Scientists and Data Managers, who interact with mission teams, provide subject matter expertise, inform data and metadata formats, evaluate data structure and quality, and address data support needs. A key project component at ASF is the core product team, which provides integration of new datasets into the ASF data system and ensures efficient coordination and support of each mission. The team members have mission-specific expertise and consist of the following personnel:

    • The Project Manager is the team leader who oversees mission activities at ASF and coordinates with external groups.
    • The Product Owner is a primary product stakeholder and oversees ingest, archive, documentation, and distribution of data products as well as managing interactions with mission and ASF scientists and other stakeholders.
    • The User Services Representative ([email protected]) supports data users with products and software tools and communicates user feedback or suggestions for improvement to the Project Manager and Product Owner.
    • Software Engineers design, develop, and maintain software for the acquisition, processing, archiving, and distribution of satellite and aerial remote sensing data.
    • Software Quality Assurance Technicians provide software and web-based-application testing prior to delivery to the production data system to ensure integrity, quality, and overall proper functionality through testing methods to uncover program defects, which in turn are reported to software engineers.
    • The Technical Science Writer composes and edits a variety of ASF materials, from newsletter articles to technical documentation.

    The core product team’s responsibilities for data management include:

    • Ingesting, cataloging, archiving, and distributing data
    • Providing guidance on file formats and integration of new file formats into the ASF data system
    • Describing data products and producing user manuals and guide documents
    • Creating metadata and exporting it to CMR and GCMD (Global Change Master Directory)
    • Ensuring accurate metrics are reported to EMS (ESDIS Metrics System)
    • Designing, developing, and deploying specialized data portals that allow online access to data products and information
    • Creating software tools for data interpretation and analysis
    • Assisting users with the selection and usage of data

    ASF also supports NASA and partner missions through the operation of a ground station with two 11-m antennas, providing complete services, including data downlinking, commanding, and range/Doppler tracking. ASF is part of the NASA Near Earth Network (NEN) supporting a variety of low-Earth-orbit spacecraft.

    ASF DAAC Data Systems
    The ASF DAAC operates a custom data system designed, implemented, and supported by DAAC personnel. During its evolution, the ASF data system has moved from using primarily custom software on capital equipment to commodity hardware and commercial off-the-shelf (COTS) software and hardware solutions. This has greatly lowered development and maintenance costs for the data system, while simultaneously providing a higher level of performance. The ASF DAAC data system provides the following capabilities:

    Data Ingest

    • Automated data ingest occurs from the ASF ground station as well as external data providers in a variety of media and formats.
    • Ingested data are pre-processed when necessary, providing browse or derivative products.

    Data Archive

    • The central ASF data system archive is provided by a Data Direct Networks gridscaler storage system.
    • This system provides direct access to over 1 PB of processed data as well as the capability for automated backups to an offsite location.
    • Raw data are held in a robotic silo for access by the processing system. ASF maintains a backup in an external location in case of silo failure.

    Data Distribution

    • ASF provides direct http access to DAAC data products and utilizes NASA’s User Registration System (URS) for user authentication.
    • NASA data are provided to public users with no restrictions. Partner data are provided to NASA-approved users through URS for authentication and ASF’s internal database for access control.
    • The data system provides web-based access to the archive through Vertex. Vertex supports the data pool with direct download of processed data.
    • Through custom portals and applications, the DAAC provides additional services such as mosaic subsetting, mosaicking, and time-series analysis.

    Data Support

    • ASF DAAC exports relevant metadata to NASA’s ECHO system.
    • ASF DAAC exports ingest, archive, and download metrics to NASA’s EMS system.
    • ASF DAAC assists users with data discovery and usage, maintains product documentation and use guides, and supports feedback between the ASF user community and the core product teams.

    SMAP at ASF DAAC
    ASF provides a variety of services, software tools, and user support to address the needs of the SMAP user community. The ASF core project team will leverage on-going collaborations with the SMAP Project to identify and prioritize SMAP user community needs, which in turn will inform development and implementation of data support and value-adding services for the mission. The SMAP website at ASF will serve as an interactive data portal, providing users with relevant documentation, custom tools and services, and ancillary data and resources.

    Post-Launch SMAP Data
    ASF will ingest, distribute, archive, and support postlaunch Level 1 radar products for the SMAP mission. ASF will receive the Level 1 radar products from the SMAP Science Data System at the Jet Propulsion Laboratory (JPL) in Pasadena, California.

    Non-SMAP Data of Interest to SMAP
    ASF will cross-link from the SMAP website to data collections that complement SMAP data and are of interest to the user community. Some of these collections are distributed by ASF, including the following:

    • Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) data products
    • Jet Propulsion Laboratory Uninhabited Aerial Vehicle SAR (UAVSAR) data products
    • Making Earth System Data Records for Use in Research Environments Inundated Wetlands (MEaSUREs) data products
    • Advanced Land Observing Satellite-Phased Array L-band SAR (ALOS PALSAR)
    • Japanese Earth Resources Satellite-1 (JERS-1) image data and mosaics

    Acronyms

     

    AcronymDefinition
    ASFAlaska Satellite Facility
    ATBDsAlgorithm Theoretical Basis Documents
    Cal/ValCalibration and Validation
    DAACDistributed Active Archive Center
    HDF5Hierarchical Data Format
    JPLJet Propulsion Laboratory
    NASANational Aeronautics and Space Administration
    NSIDCNational Snow and Ice Data Center
    ROPRoutine Observations Phase
    SMAPSoil Moisture Active Passive
    SARSynthetic Aperture Radar
    SDSScience Data System
    SOPScience Operations Phase
    UTCCoordinated Universal Time

     

    SMAP – Science Overview, Requirements, and Measurements

    SMAP’s spaceborne Earth-observation mission will enable global mapping of soil-moisture and freeze-thaw state with unprecedented accuracy, resolution, and coverage. SMAP science objectives are to acquire space-based, hydrosphere-state measurements over a three-year period to:

    • Understand processes that link the terrestrial water, energy, and carbon cycles
    • Estimate global water and energy fluxes at the land surface
    • Quantify net carbon flux in boreal landscapes
    • Enhance weather-forecast and climate-forecast skills
    • Develop improved flood-prediction and drought-monitoring capabilities

    Water and Energy Cycles, Weather, and Climate

    Recent model simulations of the effects of greenhouse gases on climate show that current models agree quite well in predicting temperature change but disagree significantly in predicting surface-moisture change and water-resource availability. Accurate soil-moisture information, such as data from SMAP, will improve the performance and enhance the predictive ability of numerical weather-prediction models and seasonal climate models. Soil moisture is a key control on evaporation and transpiration at the land-atmosphere boundary. Because vaporizing water requires large amounts of energy, soil-moisture control also has a significant impact on the surface energy flux. Soil-moisture variations affect the evolution of weather and climate, particularly over continental regions.

    Earth's water cycle involves the transfer and storage of water in the atmosphere, on the planet's surface, underground, and by life in its many forms.

    Earth’s water cycle involves the transfer and storage of water in the atmosphere, on the planet’s surface, underground, and by life in its many forms.

     

    Carbon Cycle and Ecosystems

    Soil moisture and its freeze-thaw state are also key determinants of the global carbon cycle. Carbon uptake and release in boreal landscapes is one of the major sources of uncertainty in assessing the carbon budget of the Earth system (the so-called missing carbon sink). The SMAP mission will quantify the nature, extent, timing, and duration of landscape seasonal freeze-thaw state transitions that are key to the estimation of terrestrial carbon sources and sinks. SMAP freeze-thaw state measurements will also contribute to understanding how ecosystems respond to and affect global environmental change, improving regional mapping and prediction of boreal-Arctic ecosystem processes.

     

    Water availability is changing as a result of global climate change. SMAP data will help researchers understand how these changes affect water supply and food production. Credit: NASA JPL

    Water availability is changing as a result of global climate change. SMAP data will help researchers understand how these changes affect water supply and food production.
    Credit: NASA JPL

    The global carbon cycle is the complex interaction of different carbon-based gases taking place among Earth’s atmosphere, land, and oceans.

    The global carbon cycle is the complex interaction of different carbon-based gases taking place among Earth’s atmosphere, land, and oceans.

     

    SMAP Goals

     

    The SMAP Project is designed to collect measurements of surface soil moisture and freeze-thaw state, together termed the hydrosphere state. Soil moisture is defined in terms of volume of water per unit volume of soil. Freeze-thaw state is defined as the phase of the water contained within the landscape including soil and vegetation. To meet the goals of science and applications users, SMAP must:

    • Resolve hydrometeorological water and energy flux processes and extend weather and flood forecast skill, spatial resolution of 10 km and temporal resolution of 3 days are required.
    • Resolve hydroclimatological water and energy flux processes and extend climate and drought forecast capability, spatial resolution of 40 km and temporal resolution of 3 days are required.
    • Quantify net carbon flux in boreal landscapes, spatial resolution of 3 km and temporal resolution of 2 days are required. In addition, the SMAP mission will validate a space-based measurement approach that could be used for future systematic hydrosphere state monitoring missions.

     

    SMAP News

     

    NASA Soil Moisture Radar Ends Operations; Mission Science Continues
    NASA Focused on Sentinel as Replacement for SMAP Radar 

    Baseline Science Requirements

     

    • Estimates of soil moisture in the top 5 cm of soil with an error of no greater than 0.04 cm3/cm3 (one sigma) at 10-km spatial resolution and 3-day average intervals over the global land area, excluding regions of snow and ice, frozen ground, mountainous topography, open water, urban areas, and vegetation with water content greater than 5 kg/m2 (averaged over the spatial resolution scale).
    • Estimates of surface binary freeze-thaw state in the region north of 45N latitude, which includes the boreal forest zone, with a classification accuracy of 80% at 3-km spatial resolution and 2-day average intervals.
    • Space-based measurements of soil moisture and freeze-thaw state for at least 3 years to allow seasonal and interannual variations of soil moisture and freeze-thaw to be resolved.
    • The SMAP project shall conduct a calibration and validation program to verify data delivered meets the above requirements.
    SMAP applied science poster. Credit: NASA/JPL.

    SMAP applied science poster. Credit: NASA/JPL.

    Measurements

    Approach

    The SMAP mission will validate a space-based measurement approach that could be used for future systematic hydrosphere-state monitoring missions.

    • To resolve hydrometeorological water and energy flux processes and extend weather- and flood-forecast skill, spatial resolution of 10 km, and temporal resolution of 3 days are required.
    • To resolve hydroclimatological water and energy flux processes and extend climate- and drought-forecast capability, spatial resolution of 40 km and temporal resolution of 3 days are required.
    • To quantify net carbon flux in boreal landscapes, spatial resolution of 3 km and temporal resolution of 2 days are required.

    Accuracy

    The science goal is to combine the attributes of the radar observations (high spatial resolution but lower soil-moisture accuracy) and radiometer observations (higher soil-moisture accuracy but coarse spatial resolution).

    Joint processing of the radar and radiometer data will retrieve soil moisture at a spatial resolution of 10 km, and freeze-thaw state at a spatial resolution of 3 km.

    The provision of constant incidence angle across the 1,000-km swath simplifies the data processing and enables accurate repeat-pass estimation of soil moisture and freeze-thaw.

    Orbit Characteristics

    The SMAP orbit is a 685-km altitude, near-polar, sun-synchronous, 6 a.m. / 6 p.m., eight-day, exact-repeat, frozen orbit.

    • Near-polar orbit provides global land coverage up to high latitudes including all freeze-thaw regions of interest.
    • Sun-synchrony provides observations of the surface close to the same local solar time each orbit throughout the mission, enhancing change-detection algorithms and scientific accuracy.
    • Consistent 6 a.m. observation time is optimal because it minimizes the effect of Faraday rotation and impact on S/C design.
    • Frozen orbit provides minimal altitude variation during an orbit, benefitting radar design and accuracy.
    • 685-km altitude is an exact 8-day repeat orbit, advantageous for radar change-detection algorithms.
    • Orbit provides optimum coverage of global land area at 3-day average intervals, and coverage of land region above 45N at 2-day average intervals.
    SMAP spacecraft and its swath path. Credit: NASA/JPL.

    SMAP spacecraft and its swath path. Credit: NASA/JPL.

    SMAP antenna's scanning pattern. Credit: NASA/JPL.

    SMAP antenna’s scanning pattern. Credit: NASA/JPL.

    SMAP's antenna-beam footprint measures 1,000 km. Image credit: NASA/JPL.

     Image credit: NASA/JPL. 

    astronomy

    SMAP travels in a 98-degree
    polar orbit.

    Data Acquisition

    Low-rate radiometer data and low-resolution radar data will be acquired continuously over fore and aft portions of the scan (full 360 degrees), as well as ascending and descending portions of the orbit.

    High-resolution radar data will be acquired to include at a minimum:

    • 360 degrees of the antenna scan (fore and aft looks) for the morning (6 a.m. equator crossing) half-orbit over the global land region (excluding the Antarctic)
    • 180 degrees of the antenna scan (fore look) for the evening (6 p.m. equator crossing) half-orbit over the boreal land region (north of 45 degrees N latitude)
    • 180 degrees of the antenna scan (fore look) for the morning half-orbit over the coastal ocean region (within 1,000 km of continental boundaries)
    • The Science Operations Phase (SOP) begins after completion of the 90-day post-launch, in-orbit commissioning and lasts for three years. The first part of the SOP is the Calibration and Validation (Cal/Val) phase, which lasts for 12 months. Following the Cal/Val phase is the Routine Observations Phase (ROP), which lasts for 24 months.

    SMAP – Instrument

    Soil Moisture Passive Active (SMAP) is a remote-sensing observatory with two instruments — a synthetic aperture radar (SAR) and a radiometer — that map soil moisture and determine the freeze or thaw state of the area being mapped. Both instruments help map soil-moisture content, and unique properties of SAR enable the freeze-thaw mapping. Externally, the instruments share a common, 20-foot mesh antenna and a feed assembly. Inside the spacecraft, their electronics differ. When combined, the SMAP radar and radiometer deliver high-accuracy, high-resolution global maps of the Earth’s soil moisture and freeze-thaw state.

    The radar actively sends pulses of radio waves down to a spot on Earth and measures the echo that returns microseconds later. The strength and “shape” of the echoes can be interpreted to indicate the moisture level of the soil, even through moderate levels of vegetation. The radiometer passively detects radio waves emitted by the ground from the same small area. The strength of the emission indicates temperatures.

    News: NASA Soil Moisture Radar Ends Operations; Mission Science Continues

    News: NASA Focused on Sentinel as Replacement for SMAP Radar 

    Instrument Events Timetable

    Artist's concept of SMAP observatory and its antenna-beam footprint. Credit: NASA.

    Artist’s concept of SMAP observatory and its antenna-beam footprint. Credit: NASA.

    Specifications

    Spacecraft
    Dimensions, bus only: 1.5 x .9 x .9 m
    Weight: 944 kg
    Power: 1,450 watts
    Altitude: 685 km
    Orbit path: Near-polar, sun-synchronous, equator crossings 6 a.m. and 6 pm. local time
    Orbital inclination: 98.1 degrees

    Radar: L-band
    Frequency: 1.2 to 1.3 GHz
    Polarizations: VV, HH, HV (not fully polarimetric)
    Relative accuracy (3 km grid): 1 dB (HH and VV), 1.5 dB (HV)
    Data acquisition:

    • High-resolution (SAR) data acquired over land
    • Low-resolution data acquired globally
    SMAP's antenna-beam footprint measures 1,000 km. Image credit: NASA/JPL.

    SMAP’s antenna-beam footprint measures 1,000 km. Image credit: NASA/JPL.

    Radiometer: L-band
    Frequency: 1.41 GHz
    Polarizations: H, V, 3rd and 4th Stokes
    Relative accuracy (30 km grid): 1.3 K
    Data collection:

    • High-rate (sub-band) data acquired over land
    • Low-rate data acquired globally

    Antenna
    Conically scanning deployable mesh reflector shared by radar and radiometer
    Diameter: 6 m
    Rotation rate: 14.6 RPM
    Beam efficiency: ~90%
    Beam surface incidence angle: 40°
    Spatial Resolution:

    • SAR: 1-3 km (over outer 70% of swath; ‘high-resolution’ radar)
    • Radiometer (IFOV): 39 km x 47 km
    • Real-aperture radar footprint resolution: 29 km x 35 km (‘low-resolution’ radar)

    Swath width: 1,000 km

    Radar Resolution and Gridding

    The SMAP radar employs unfocused synthetic aperture radar (SAR) processing. The range and azimuth resolutions are determined by the unique antenna scan geometry.

    The SAR single-look samples (time-ordered) are averaged (multi-looked) onto a swath-oriented 1-km grid to form the L1C_S0_HiRes product. The grid posting of the L1C_S0_HiRes product is fixed at 1 km, but the spatial resolution, number of looks, and signal-to-noise ratio (SNR) vary across the swath.

    This panel illustrates the radar-measurement geometry showing the range and Doppler contours. The real aperture radar footprint ellipse is shown at two representative azimuth-scan angles. This schematic, which is not drawn to scale, illustrates how the single-look data samples from successive fore-look scans are oriented and overlap relative to the 1-km gred. Image Credit: NASA/JPL.This panel illustrates the variation as a function of swath position (distance from center track). Image Credit: NASA/JPL.
    This panel illustrates the radar-measurement geometry showing the range and Doppler contours. The real aperture radar footprint ellipse is shown at two representative azimuth-scan angles. The radar 1-MHz bandwidth yields a ground-range resolution of ~250 m. The doppler diversity is maximum at a scan angle perpendicular to the satellite velocity (swath edge), leading to an azimuth single-look resolution of ~450 m. The single-look resolution degrades as the scan angles approach the satellite velocity vector. Image Credit: NASA/JPL.This schematic, which is not drawn to scale, illustrates how the single-look data samples from successive fore-look scans are oriented and overlap relative to the 1-km gred. Image Credit: NASA/JPL.This panel illustrates the variation as a function of swath position (distance from center track). Image Credit: NASA/JPL.

    Frequency and Polarizations

    The L-band frequency enables observations of soil moisture through moderate vegetation cover, independent of cloud cover and night or day. Multiple polarizations enable accurate soil moisture estimates to be made with corrections for vegetation, surface roughness, Faraday rotation, and other perturbing factors.

    The SMAP instrument incorporates an L-band radar (VV, HH, and HV polarizations) and an L-band radiometer (V, H, and 3rd and 4th Stokes parameter polarizations).

    Spatial Resolution

    To obtain high spatial resolution, the radar employs range and Doppler discrimination.

    To mitigate radio-frequency interference (RFI) from ground transmitters, the radiometer employs a digital backend and sub-banding approach. The radiometer ‘high-rate’ mode acquires sub-band data; the radiometer ‘low-rate’ mode acquires data averaged over the full band only.

    Radar Data

    The radar high-resolution measurement samples are created within the radar real-aperture footprint by synthetic aperture processing in range and azimuth. The synthesized single-look samples have variable spatial resolution in the azimuth direction. The single-look samples are averaged (multi-looked) onto 1-km grid pixels to form the L1C_S0_HiRes data product.

    The L1C_S0_HiRes HH and VV data have uncertainty from all sources (excluding rain) of 1.0 dB or less (1-sigma) defined at 3-km spatial resolution and for surfaces of radar cross-section greater than -25 dB. The HV data have uncertainty from all sources (excluding rain) of 1.5 dB or less (1-sigma) defined at 3-km spatial resolution and for surfaces of HV radar cross-section greater than -30 dB.

    Radiometer Data

    The radiometer instantaneous field of view (IFOV) or 3-dB footprint is 39 km x 47 km. The radiometer L1B_TB data product includes compensation for effects of antenna sidelobes (outside the main beam), cross-polarization, Faraday rotation, atmospheric effects (excluding rain), and solar, galactic, and cosmic radiation.

    The L1B_TB have mean uncertainty from all sources (excluding rain) of 1.3 K or less (1-sigma) in the H and V channels, defined on the basis of binning the fore- and aft-look samples onto hypothetical, swath-oriented, 30-km x 30-km grid cells (a different grid is used for the actual L1C_TB data product).

    SMAP Documents, Tools, Acronyms

    Documents

     

    NameDescription
    ATBD DocumentsAlgorithm theoretical basis documents are listed with the products available for download.
    Product Specification DocumentsProduct specification documents are listed with the products available for download.
    ASF SMAP User GuideFor Level 1 products, a streamlined guide to accessing data, using SMAP products, understanding acronyms and abbreviations, and more.
    Radar Backscatter Calibration, L1B_S0_LoRes and L1C_S0_HiRes Beta Level Data ProductsProvides analysis and assessment of calibration quality of SMAP radar normalized backscatter cross-section for the L1B_S0_LoRes and L1C_S0_HiRes beta level data products. Dated 8/5/2015.
    SMAP HandbookWritten in 2013 as a compendium of information on the project near its time of launch. Contains essential information on programmatic, technological, and scientific aspects of the mission.
    Ancillary Data Reports (table includes links to cited data)Application-related descriptions of datasets used with science-algorithm software in generating SMAP science-data products, as well as links to EASE grid information relevant to SMAP products. Also included are links to the ancillary data cited in the reports.
    PublicationsLists publications on remote sensing of soil moisture since 2001. Please submit additional publications to [email protected], with “SMAP Publications” on the subject line.
    Worldview quick-look toolThis tool from NASA’s EOSDIS provides the capability to interactively browse global, full-resolution satellite imagery and then download the underlying data. Most of the 100+ available products are updated within three hours of observation.
    HDF GroupThe HDF Group offers a list of software using HDF5.
    ASF list of potentially useful software tools.

    Tools

     

    NameDescription
    ASF Software ToolsASF offers several tools that can be used on many datasets.
    SMAP Analysis ClientInteractive application developed by the Jet Propulsion Lab (JPL) / NASA for Soil Moisture Active Passive (SMAP) data. Users may want to start with the calendar icon on the far right of the page.
    GDALThe Geospatial Data Abstraction Library helps explore the general contents of SMAP Hierarchical Data Format (HDF) 5 data. The gdalinfo tool summarizes the data structure in the file. The gdal_translate tool can extract the SAR data out of the structure and store it into a more versatile GeoTIFF.
    ArcGIS (commercial GIS system)Recognizes the HDF5 structure and is able to extract the SAR data on the fly. Does not calculate standard statistics for this data layer and is slow in rendering the data this way. Can be significantly improved by converting the HDF5 data layer HH into a GeoTIFF file using the gdal_translate tool.
    HDFViewProvided by the HDF group; looks at data in two ways. For quantitative analysis, the selected data layer must be opened as spreadsheet. For a visual analysis, HDFView provides the image view. The program has no options for stretching data in a statistical fashion. However, the user can manually change the brightness and contrast.
    PanoplyDeveloped by NASA’s Goddard Institute for Space Studies, primarily used for global datasets of lower resolution.
    Interactive Data LanguageThe Interactive Data Language (IDL) provides a more programmatic means to visualize the SMAP HDF5 data. The IDL H5 browser has very limited functionality in terms of changing visual value ranges and stretching the imagery for visualization purposes, but it does provide users the ability to view the HDF5 products.
    Worldview quick-look toolThis tool from NASA’s EOSDIS provides the capability to interactively browse global, full-resolution satellite imagery and then download the underlying data. Most of the 100+ available products are updated within three hours of observation.
    HDF GroupThe HDF Group offers a list of software using HDF5.
    ASF list of potentially useful software tools.

     

    ASF list of potentially useful software tools.

     

    Acronyms

     

    AcronymDefinition
    ASFAlaska Satellite Facility
    ATBDsAlgorithm Theoretical Basis Documents
    Cal/ValCalibration and Validation
    DAACDistributed Active Archive Center
    HDF5Hierarchical Data Format
    JPLJet Propulsion Laboratory
    NASANational Aeronautics and Space Administration
    NSIDCNational Snow and Ice Data Center
    ROPRoutine Observations Phase
    SMAPSoil Moisture Active Passive
    SARSynthetic Aperture Radar
    SDSScience Data System
    SOPScience Operations Phase
    UTCCoordinated Universal Time

    SMAP – Publications and Credits

    Publications

     

    Please submit additional relevant publications to [email protected], with “SMAP Publications” on the subject line.

    Publication YearDocument title and download link (if available)
    2014Assessment of Soil Moisture Data Requirements by the Potential SMAP Data User Community: Review of SMAP Mission User Community – M.E. Brown, V.M. Escobar; Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, Vol. 7 No. 1, Jan. 2014, p.277-283, doi: 10.1109/JSTARS.2013.2261473.
    2014Automated L-Band Radar System for Sensing Soil Moisture at High Temporal Resolution– K. Nagarajan, Pang-Wei Liu, R. DeRoo, J. Judge, R. Akbar, P. Rush, S. Feagle, D. Preston, R. Terwilleger; Geoscience and Remote Sensing Letters, IEEE, Vol. 11 No. 2, 2014, p. 504-508, doi: 10.1109/LGRS.2013.2270453 
    2014The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission – R. Panciera, J.P. Walker, T.J. Jackson, D.A. Gray, M.A. Tanase, Dongryeol Ryu, A. Monerris, H. Yardley, C. Rudiger, Xiaoling Wu, Ying Gao, J.M. Hacker; Geoscience and Remote Sensing, IEEE Transactions on, Vol. 52 No. 1, Part 2, 2014, p. 490-507, doi: 10.1109/TGRS.2013.2241774 
    2014Tests of the SMAP Combined Radar and Radiometer Algorithm Using Airborne Field Campaign Observations and Simulated Data – N.N. Das, D. Entekhabi, E.G. Njoku, J.J.C. Shi, J.T. Johnson, A. Colliander; Geoscience and Remote Sensing, IEEE Transactions on, Vol. 52 No.4, 2014, p. 2018-2028, doi:  10.1109/TGRS.2013.2257606
    2013A downscaling algorithm for combining radar and radiometer observations for SMAP soil moisture retrieval – Peng Guo, Jiancheng Shi, Tianjie Zhao; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 731 – 734, doi: 10.1109/IGARSS.2013.6721261
    2013L-band active / passive time series measurements over a growing season using the ComRAD ground-based SMAP simulator – P. O’Neill, M. Kurum, A. Joseph, J. Fuchs, P. Young, M. Cosh, R. Lang; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 37-40, doi: 10.1109/IGARSS.2013.6721086
    2013NASA’s Soil Moisture Active Passive (SMAP) observatory – K. Kellogg, K., et al.; Aerospace Conference, 2013 IEEE, p. 1-20, doi: 10.1109/AERO.2013.6496938
    2013Performance characterization of the SMAP RFI mitigation algorithm using direct-sampled SMAPVEX 2012 data – S. Misra, J. Johnson, M. Aksoy, D. Bradley, Hsin Li, J. Mederios, J. Piepmeier, I. O’Dwyer; Radio Science Meeting (USNC-URSI NRSM), 2013 US National Committee of URSI National, p. 1, doi: 10.1109/USNC-URSI NRSM.2013.6524988
    2013A radar-radiometer surface soil moisture retrieval algorithm for SMAP, – R. Akbar, M. Moghaddam; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 1095-1098, doi: 10.1109/IGARSS.2013.6721355
    2013A robust algorithm for soil moisture retrieval from the soil Moisture Active Passive mission radar observations – P.S. Narvekar, D. Entekhabi, Seungbum Kim, E. Njoku; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 45-48, doi: 10.1109/IGARSS.2013.6721088
    2013SMAP RFI mitigation algorithm performance characterization using airborne high-rate direct-sampled SMAPVEX 2012 data – S. Misra, et al.; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 41 – 44, doi: 10.1109/IGARSS.2013.6721087
    2013The Soil Moisture Active Passive (SMAP) radar: Measurements at high latitudes and of surface freeze/thaw state – M. Spencer, S. Dunbar, C. Chen; Radar Conference (RADAR), 2013 IEEE, p. 1-5, doi: 10.1109/RADAR.2013.6586087
    2013State of the Art in Large-Scale Soil Moisture Monitoring – Tyson E. Ochsner, Michael H. Coshb, Richard H. Cuencac, Wouter A. Dorigod, Clara S. Drapere, Yutaka Hagimotof, Yann H. Kerrg, Eni G. Njokuh, Eric E. Smalli and Marek Zredaj; Soil Science Society of America Journal, Vol. 77 No. 6, p. 1888-1919; doi:10.2136/sssaj2013.03.0093.
    2013Towards a high-density soil moisture network for the validation of SMAP in Petzenkirchen, Austria – M. Vreugdenhil, et al.; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 1865 – 1868, doi: 10.1109/IGARSS.2013.6723166
    2013US national cropland soil moisture monitoring using SMAP – Zhengwei Yang, R. Mueller, W. Crow; Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International, p. 3746 – 3749, doi: 10.1109/IGARSS.2013.6723645
    2012Advances in Earth observation for water cycle science (editorial) – D. Fernandez-Prieto et al., Copernicus Publications on behalf of the European Geosciences Union, Hydrol. Earth Syst. Sci., 16, p. 543–549, 2012, doi:10.5194/hess-16-543-2012
    2012Active and passive airborne microwave remote sensing for soil moisture retrieval in the Rur catchment, Germany – C. Montzka, et al., Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, p. 6956 – 6959, doi: 10.1109/IGARSS.2012.6352562
    2012An airborne simulation of the SMAP data stream – J.P. Walker et al.; Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, p. 5-7 doi: 10.1109/IGARSS.2012.6351537
    2012Application of QuikSCAT Backscatter to SMAP Validation Planning: Freeze/Thaw State Over ALECTRA Sites in Alaska From 2000 to 2007 – A. Colliander, K. McDonald, R. Zimmermann, R. Schroeder, J.S. Kimball, E.G. Njoku; Geoscience and Remote Sensing, IEEE Transactions on, Vol. 50 No. 2, 2012, p. 461 – 468 , doi: 10.1109/TGRS.2011.2174368
    2012Assessment of the impacts of radio frequency interference on SMAP radar and radiometer measurements – C.W. Chen, J.R. Piepmeier, J.T. Johnson, H. Ghaemi, H.; Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, p. 1-4, doi: 10.1109/IGARSS.2012.6351538
    2012Comparison of Backscattering Models at L-Band for Growing Corn – A. Monsivais-Huertero, J. Judge, Geoscience and Remote Sensing Letters, IEEE, Vol. 8 No. 1, p. 24-28, doi: 10.1109/LGRS.2010.2050459
    2012Design and performance of Astromesh reflector onboard Soil Moisture Active Passive spacecraft – M. Mobrem, et al.; Aerospace Conference, 2012 IEEE, p. 1-10, doi: 10.1109/AERO.2012.6187094
    2012Development of SMAP (soil moisture active and passive) Freeze/Thaw algorithms adapted for the Canadian Tundra – P. Kalantari, M. Bernier, K.C. McDonald, J. Poulin; Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, p. 5218 – 5221, doi: 10.1109/IGARSS.2012.6352433
    2012A dual-polarized, dual-frequency, corrugated feed horn for SMAP – P. Focardi, P.R. Brown, Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE, p. 1-2, doi: 10.1109/APS.2012.6349003
    2012End-to-end data flow on the Soil Moisture Active Passive (SMAP) mission – E. Deems, C. Swan, B. Weiss; Aerospace Conference, 2012 IEEE, p. 1-16, doi: 10.1109/AERO.2012.6187175
    2012An integrated active-passive soil moisture retrieval algorithm for SMAP for bare surfaces – R. Akbar, M. Moghaddam; Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, p. 16 – 19, doi: 10.1109/IGARSS.2012.6350891
    2012Integration of Daily Inundation Extent Estimates into an Ecosystem-Atmosphere Gas Exchange Model – J. Galantowicz, A. Samanta, H. Tian, American Geophysical Union, Fall Meeting 2012, abstract #B41C-0303
    2011A 6-m mesh reflector antenna for SMAP: Modeling the RF performance of a challenging Earth-orbiting instrument – P. Focardi, P. Brown, Y. Rahmat-Samii; Antennas and Propagation (APSURSI), 2011 IEEE International Symposium on, p. 2987- 2990, doi: 10.1109/APS.2011.5997157
    2011AIAA ICES 2011 – Preliminary Evaluation of Passive Thermal Control for the Soil Moisture Active Passive (SMAP) Radiometer – A.J. Mastropietro, Eug Kwack, Rebecca Mikhaylov, Michael Spencer, Pamela Hoffman, Douglas Dawson (JPL); Jeff Piepmeier, Derek Hudson, James Medeiros (GSFC). Download File
    2011An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval – Narendra N. Das, Dara Entekhabi, Eni G. Njoku, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 5, May 2011.
    2011Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops – Anna Balenzano, Francesco Mattia, Senior Member, IEEE, Giuseppe Satalino, and Malcolm W. J. Davidson, IEEE Transactions on Geoscience and Remote Sensing, Vol. 4, No. 2, June 2011.
    2011Effect of Soil Moisture on polarimetric-interferometric repeat pass observations by UAVSAR during 2010 Canadian Soil Moisture campaign – Scott Hensley, Thierry Michel, Jakob Van Zyl, Ron Muellerschoen, Bruce Chapman, Shadi Oveisgharan, Ziad S. Haddad,Tom Jackson, Iliana Mladenova, SMAP Cal/Val Workshop, May 3-5, 2011.
    2011IGARSS 2011 – Utilization of Ancillary Data Sets for SMAP Algorithm Development and Product Generation; Peggy E. O’Neill, Erika Podest, and Eni G. Njoku.
    2011Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture – Chen, F., W.T. Crow, P.J. Starks, and D.N. Moriasi, Advances in Water Resources, 34, 526–536, 2011. Download File
    2010A quasi-global evaluation system for satellite-based surface soil moisture retrievals – Crow, W.T., D.G. Miralles and M.H. Cosh, IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2516-2527. Download File
    2010A technique for estimating spatial sampling errors in coarse-scale soil moisture estimates derived from point-scale observations – Miralles, D.G., W.T. Crow and M.H. Cosh, Journal of Hydrometeorology, 11(6), 1404-1410,10.1175/2010JHM1285.1, 2010. Download File
    2010AGU 2010 – The Contribution of Soil Moisture Information to Forecast Skill: Two Studies; Randal Koster, Sarith Mahanama, and Ben Livneh.
    2010AMS 2010 – NASA’s Soil Moisture Active Passive (SMAP) Mission Applications in the Atmospheric and Hydrologic Sciences; Dara Entekhabi, Eni Njoku, Peggy O’Neill, R. Koster.
    2010An algorithm for merging SMAP radiometer and radar data for high resolution soil moisture retrieval – Das, N., Entekhabi, D., Njoku, E., IEEE-Transactions on Geoscience and Remote Sensing, In press.
    2010An improved approach for estimating observation and model error parameters for soil moisture data assimilation – Crow, W.T. and M.J. van den Berg, Water Resources Research, 46, W12519, 10.1029/2010WR009402, 2010. Download File
    2010Assimilation of SMAP measurements for soil moisture-based military trafficability assessment at tactical scales – Flores, L., D. Entekhabi, and R. L. Bras, IEEE Journal of Selected Topics in Earth Observations and Remote Sensing, 2009-00030.
    2010Backscattering coefficients, coherent reflectivities, and emissivities of randomly rough soil surfaces at L-band for SMAP applications based on numerical solutions of Maxwell equations in three-dimensional simulations – Huang, Shaowu, Tsang, Leung, Njoku, Eni and Chan, Kuan Shan, IEEE Transactions of Geoscience and Remote Sensing, Vol. 48, No. 6, June 2010, 2557-2568. Download File
    2010Evaluating the utility of remotely-sensed soil moisture retrievals for operational agricultural drought monitoring – Bolten, J.D., W.T. Crow, T.J. Jackson, X. Zhan and C.A. Reynolds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 57-66. Download File
    2010IGARSS 2010 – Forward Simulation of Passive Microwave Observation for the Soil Moisture Active Passive (SMAP) Mission; Steven Chan, Eni Njoku, and Scott Dunbar.
    2010IGARSS 2010 – Fostering Applications opportunities for the NASA Soil Moisture Active Passive (SMAP) Mission; M. Susan Moran, Peggy E. O’Neill, Dara Entekhabi, Eni Njoku and Kent Kellogg. 
    2010IGARSS 2010 – QuikSCAT Backscatter Sensitivity to Landscape Freeze/Thaw State over ALECTRA Sites in Alaska from 2000 to 2007: Application to SMAP Validation Planning; A. Colliander, K. McDonald, R. Zimmermann, T. Linke, R. Schroeder, J. Kimball. 
    2010IGARSS 2010 – The NASA Soil Moisture Active Passive (SMAP) Mission: Overview; O’Neil, Entekhabi, Njoku and Kellogg.
    2010The impact of radar incidence angle on soil moisture retrieval skill – Crow, W.T., W. Wagner and V. Naeimi, IEEE Geoscience and Remote Sensing Letters, 7(3), 501-505, 10.1109/LGRS.2010.2040134, July 2010. Download File
    2010MicroRad 2010 – Utilization of Airborne and In Situ Data Obtained in SGP99, SMEX02, CLASIC and SMAPVEX08 Field Campaigns for SMAP Soil Moisture Algorithm Development and Validation; Colliander, A., S. Chan, S. Yueh, M. Cosh, R. Bindlish, T. Jackson, E. Njoku. 
    2010PIEEE 2010 – The Soil Moisture Active and Passive (SMAP) Mission, Entekhabi, D., E. Njoku, P. O’Neill, K. Kellogg, W. Crow, W. Edelstein, J. Entin, S. Goodman, T. Jackson, J. Johnson, J. Kimball, J. Piepmeier, R. Koster, K. McDonald, M. Moghaddam, S. Moran, R. Reichle, J. C. Shi, M. Spencer, S. Thurman, L. Tsang, J. Van Zyl, Proceedings of the IEEE, 98(5). 
    2010PIERS 2010 – Azimuthal Signature of Coincidental Brightness Temperature and Normalized Radar Cross-section Obtained Using Airborne PALS Instrument; Colliander, A., S. Kim, S. Yueh, M. Cosh, T. Jackson, E. Njoku. 
    2010RadarCon 2010 – Monitoring surface soil moisture and freeze-thaw state with the high-resolution radar of the Soil Moisture Active/Passive (SMAP) mission; Kim, S, Van Zyl J, McDonald, K. and Njoku, E. 
    2010Soil Moisture Active Passive Poster – Joshua B. Fisher, Eni G. Njoku, Data Entekhabi.
    2010Study of validity region of small perturbation method for two-layer rough surfaces – Tabatabaeenejad, A., and M. Moghaddam, IEEE Geosci. Remote Sensing Lett, vol. 7, no. 2, pp. 319-323, April 2010.
    20092009 IEEE Radar Conference – A Science Data System Approach For The SMAP Mission; Woollard D, Kwoun OI, Bicknell T, et al., Pages: 664-669 MAY 2009.
    20092009 IEEE Radar Conference – RFI STUDY FOR THE SMAP RADAR; Chan S, Spencer M, Pages: 143-147 MAY 2009.
    20092009 IEEE Radar Conference – SMAP’s Radar OBP Algorithm Development ; Le C, Spencer MW, Veilleux L, et al., Pages 660-663 MAY 2009.
    2009AIAA 2009 – The Soil Moisture Active and Passive Mission (SMAP), Njoku, E., K. Kellogg, P. O’Neill, and D. Entekhabi.
    2009A Change Detection Algorithm for Retrieving High-Resolution Soil Moisture From SMAP Radar and Radiometer Observations – Piles M, Entekhabi D, Camps A, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Volume: 47 Issue: 12 Pages: 4125-4131 DEC 2009.
    2009AGU 2009, Fall – Development of Global High Resolution Soil Moisture Product from the SMAP mission; Narendra N. Das, Dara Entekhabi, Eni Njoku.  
    2009A Satellite Approach to Estimate Land-Atmosphere CO2 Exchange for Boreal and Arctic Biomes Using MODIS and AMSR_E – John Kimball, Lucas Jones, Ke Zhang, Faith Ann Heinsch, Kyle McDonald and Walt Oechel, IEEE Transactions on Geoscience and Remote Sensing, Vol 47, No.2, February 2009. Download File
    2009A study of detection algorithms for pulsed sinusoidal interference in microwave radiometry – J. T. Johnson and L. C. Potter, IEEE Trans. Geosc. Rem. Sens., vol. 47, pp. 628–636, 2009.
    2009Combined Passive and Active Microwave Observations of Soil Moisture During CLASIC – Bindlish, R; Jackson, T; Sun, RJ; et al., IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 6 (4): 644-648 OCT 2009.
    2009Ecohydrological controls on snowmelt partitioning in mixed-conifer sub-alpine forests – Noah.P. Molotch, Paul D. Brooks, Sean P. Burns, Marcy Litvak, Russell K. Monson, Jospeh R. McConnell, and Keith Musselman, Echodydrology. 2, 129-142 (2009) DOI: 10.1002/eco.48. Download File
    2009IEEE Radar Conference – The Soil Moisture Active and Passive Mission (SMAP): Science and Applications; Entekhabi, D; O’Neill, P; Njoku, E, 83-85 2009.
    2009IEEE Radar Conference 2009 – Soil Moisture Active and Passive Mission, Entekhabi, D., E. Njoku and P. O’Neill.
    2009IGARSS 2009 – A change detection algorithm for retrieving high-resolution surface soil moisture from SMAP L-band radar and radiometer observations, Piles, M., D. Entekhabi and A. Camps.
    2009IGARSS 2009 – Airborne L-band RFI observations in the SMAPVEX08 Campaign with the L-band Interference Suppressing Radiometer; Majurec, N., J. Park, N. Niamsuwan, M. Frankford, and J. T. Johnson.
    2009IGARSS 2009 – Algorithm development using the SMAP Algorithm Testbed, Chan, S., S. Dunbar, A. Colliander, E. Njoku and D. Entekhabi.
    2009IGARSS 2009 – High Resolution Mapping of Soil Moisture with SMAP Radar and Radiometer in Support of new Approaches to Water Cycle Science and Applications; Entekhabi et al. 
    2009IGARSS 2009 – PALS-ADD and airborne campaigns to support soil moisture and sea surface salinity missions; Yueh, S. et al.
    2009IGARSS 2009 – Space-borne soil moisture measurements in support of flood hydrology: The NASA SMAP approach; Crow et al.
    2009Impact of hillslope-scale organization of topography, soil moisture, soil temperature and vegetation on modeling surface microwave radiation emission, Flores, A. N., V. Y. Ivanov, D. Entekhabi, and R. L. Bras, IEEE Transactions of Geoscience and Remote Sensing, 47(8), 2557-2571.
    2009Improving satellite rainfall accumulation estimates using spaceborne soil moisture retrievals – Crow, W.T., G.F. Huffman, R. Bindlish and T.J. Jackson, Journal of Hydrometeorology, 10(1), 199-212. Download File
    2009Inversion of dielectric properties of layered rough surface using the simulated annealing method – Tabatabaeenejad, A., and M. Moghaddam, IEEE Trans. Geosci. Remote Sensing, vol. 47, no. 7, pp. 2035-2046, July 2009.
    2009L-Band Radar Estimation of Forest Attenuation for Active/Passive Soil Moisture Inversion – Kurum, M; Lang, RH; O’Neill, PE; et al., IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 47 (9): 3026-3040 Sp. Iss. SI SEP 2009.
    2009PIERS 2009 – Retrieval Algorithm Development Based on SMEX02 Field Campaign Data for the Soil Moisture Active and Passive (SMAP) Mission; Chan and Njoku. 
    2009PIERS 2009 – Soil Moisture Active and Passive (SMAP) Mission; Entekhabi et al.
    2009Role of subsurface physics in the assimilation of surface soil moisture observations – Kumar, S. V., R. H. Reichle, R. D. Koster, W. T. Crow, and C. D. Peters-Lidard, Journal of Hydrometeorology, 10, 1534-1547; http://dx.doi.org/10.1175/2009JHM1134.1.
    2009Survey of L-Band tower and airborne sensor systems relevant to upcoming soil moisture missions, O’Neill, P., T. Jackson, D. Entekhabi and E. Njoku , IEEE Geoscience and Remote Sensing Newsletter, 151, 13-16.
    2009URSI National Radio Science Meeting 2009 – Airborne L-band radio frequency interference studies with the L-band Interference Suppressing Radiometer (LISR) and PALS; Park, J., N. Majurec, N. Niamsuwan, M. Frankford, J. T. Johnson, S. Dinardo, S. Yueh.
    20082008 IEEE Radar Conference – THE SOIL MOISTURE ACTIVE/PASSIVE (SMAP) RADAR; Spencer, M; Kim, Y; Chan, S, 931-935 2008.
    20082008 MICROWAVE RADIOMETRY AND REMOTE SENSING OF THE ENVIRONMENT – Estimation of Canopy Attenuation for Active/Passive Microwave Soil Moisture Retrieval Algorithms; Kurum, M; Lang, RH; O’Neill, PE; et al., : 136-139 2008.
    2008An adaptive ensemble Kalman filter for soil moisture data assimilation – Reichle, R. H., W. T. Crow, and C. L. Keppenne, Water Resources Research, 44, W03423, http://dx.doi.org/10.1029/2007WR006357.
    2008Contribution of soil moisture retrievals to land data assimilation products – Reichle, R. H., W. T. Crow, R. D. Koster, H. Sharif, and S. P. P. Mahanama, Geophysical Research Letters, 35, L01404, http://dx.doi.org/10.1029/2007GL031986.
    2008IGARSS 2008 – Active-passive observations of soil moisture – Implementation concept for the SMAP mission, Njoku, E., D. Entekhabi, and P. O’Neill.
    2008IGARSS 2008 – Hillslope-scale controls on remote sensing of soil moisture with microwave radiometry, Flores, A. V. Ivanov, D. Entekhabi, and R. Bras.
    2008IGARSS 2008 – THE SOIL MOISTURE ACTIVE/PASSIVE MISSION (SMAP); Entekhabi et al.
    2008PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) – Soil Moisture Active/Passive (SMAP) Mission Concept; Entekhabi, D; Jackson, TJ; Njoku, E; et al., art. no. 70850H, 7085: H850-H850 2008.
    2008SPIE 2008 – Soil Moisture Active/Passive (SMAP) Mission Concept; T. J. Jackson, D. Entekhabi, E. Njoku, P. O’Neill, and J. Entin.
    2007Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR) – Reichle, R. H., R. D. Koster, P. Liu, S. P. P. Mahanama, E. G. Njoku, and M. Owe, Journal of Geophysical Research – Atmospheres, 112, D09108, http://dx.doi.org/10.1029/2004GL021700.
    2007Fall AGU 2007 – Soil Moisture Active Passive (SMAP) Mission; Dara Entekhabi, Eni Njoku, Peggy O’Neill,T.J. Jackson, S.W. Boland, J.K. Entin, E. Im.
    2007IGARSS 2007 – Algorithm for High-Resolution Soil Moisture Retrieval With Coincident Active and Passive L-Band Measurements; Dara Entekhabi.
    2007Impact of multiresolution active and passive microwave measurements on soil moisture estimation using the ensemble Kalman smoother – Dunne, SC; Entekhabi, D; Njoku, EG, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 45 (4): 1016-1028 APR 2007.
    2006A Method for Retrieving High Resolution Surface Soil Moisture from Hydros L-Band Radiometer and Radar Observations – Zhan, X., Houser, P. R., Walker , J. P. and Crow, W., IEEE Transactions on Geoscience and Remote Sensing, 44(6):1534-1544, 2006, doi:10.1109/TGRS.2005.863319.
    20062006 IEEE MicroRad – L-band active and passive sensing of soil moisture through forests; Lang, RH, Chauhan, N; Utku, C; et al., : 193-196 2006.
    2005Global assimilation of satellite surface soil moisture retrievals into the NASA Catchment land surface model – Reichle, R. H. and R. D. Koster, Geophysical Research Letters, 32, L02404, http://dx.doi.org/10.1029/2004GL021700.
    2005Relevance of time-varying and time-invariant retrieval error sources on the utility of spaceborne soil moisture products – Crow, W.T., Koster, R., Reichle, R., Sharif, H., Geophysical Research letters, Vol. 32, L24405, doi: 1029/2005GL024889, 2005. Download File
    2004Bias reduction in short records of satellite soil moisture – Reichle, R. H. and R. D. Koster, Geophysical Research Letters, 31, L19501    http://dx.doi.org/10.1029/2004GL020938.
    2004Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation – Reichle, R. H., R. D. Koster, J. Dong, and A. A. Berg Journal of Hydrometeorology, 5, 430-442, 2004.
    2004IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) – Combination of passive and active microwave data for soil moisture estimates; Ghedira, H; Lakhankar, T; Jahan, N; et al., : 2783-2786 2004.
    2004Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02) – Narayan, U; Lakshmi, V; Njoku, EG, REMOTE SENSING OF ENVIRONMENT, 92 (4): 483-496 SEP 30 2004.
    2003IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) – An algorithm to retrieve soil moisture using synergistic active/passive microwave, data on bare soil surface; Zhang, WG; Chao, W; Hong, Z; et al., : 917-919 2003.
    2003IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) – Soil moisture retrieval through changing corn using active passive microwave remote sensing; O’Neill, PE; Joseph, A; De Lannoy, G; et al., : 407-409 2003.
    2003Soil moisture retrieval using the passive/active L- and S-band radar/radiometer – Bolton, JD; Lakshmi, V; Njoku, EG, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 41 (12): 2792-2801 Part 1 DEC 2003.
    2002IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) – Measuring soil moisture change with vegetation cover using passive and active microwave data; Li, Z; Shi, JC; Guo, HD, : 3071-3073 2002.
    2001Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements – Wilson, WJ; Yueh, SH; Dinardo, SJ; et al., IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 39 (5): 1039-1048 MAY 2001.

    Passive active L- and S-band (PALS) microwave sensor for ocean salinity and soil moisture measurements – Wilson, WJ; Yueh, SH; Dinardo, SJ; et al., IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 39 (5): 1039-1048 MAY 2001.
     

    Credits

     

    SMAP Team

     

    Soil Moisture Active Passive (SMAP) is a directed mission within the NASA Earth Systematic Mission Program. The SMAP project is managed by the Jet Propulsion Laboratory (JPL) with participation by the Goddard Space Flight Center (GSFC).

    JPL is responsible for project management, system engineering, instrument management, the radar instrument, mission operations and the ground data system, science-data processing, and delivery of science-data products to a designated archive for public distribution.

    GSFC is responsible for the radiometer instrument, science-data processing, and delivery of science-data products to a designated archive for public distribution.

    ASF and the National Snow and Ice Data Center (NSIDC) are responsible for distributing data to the public.

     

    Key Program/Project Personnel

     

    C. Bonniksen (NASA/Hq), Program Executive, NASA HQ
    J. Entin (NASA/Hq), Program Scientist, NASA HQ
    K. Kellogg (JPL), Project Manager, JPL
    S. Yueh (JPL), Project Scientist, JPL
    P. ONeill (GSFC), Deputy Project Scientist, GSFC

     

    Science Team

     

    The SMAP Science Team (ST) was selected competitively by NASA in 2013 through a ROSES proposal solicitation. ST members are responsible for advising the project on science requirements, science-product definition, science algorithms, calibration/validation planning and implementation, and for publishing science results and supporting education and public outreach for the project. The ST replaces the earlier Science Definition Team (SDT), whose tenure ended in 2013. The ST tenure extends through the end of the SMAP mission.

    Science Team Leader

    D. Entekhabi, Massachusetts Institute of Technology

    U.S. ST Members:

    W. Crow, U.S. Department of Agriculture
    T. Jackson, U.S. Department of Agriculture
    J. Johnson, Ohio State University
    J. Kimball, University of Montana
    R. Koster, NASA Goddard Space Flight Center
    D. Le Vine, NASA Goddard Space Flight Center
    S. Misra, Jet Propulsion Laboratory
    M. Moghaddam, University of Southern California
    S. Moran, U.S. Department of Agriculture
    R. Reichle, NASA Goddard Space Flight Center
    K. Sarabandi, University of Michigan
    L. Tsang, University of Washington
    J. van Zyl, Jet Propulsion Laboratory
    E. Wood, Princeton University

    International ST Members:

    S. Belair, Environment Canada, Canada
    R. Gurney, University of Reading, UK
    Y. Kerr, CESBIO, France
    N. Pierdicca, University of Rome, Italy
    J. Walker, Monash University, Clayton, Australia

    Applications Coordinator:

    M. Brown, NASA Goddard Space Flight Center

     

    Site Content

    Adapted from JPL SMAP website.