SMAP – How to Cite

Citing SMAP Level 1 Datasets

Cite datasets in publications such as journal papers, articles, presentations, posters, and websites. Each SMAP Level 1 dataset has an assigned DOI. Please also send a copy of publications that cite datasets or tools obtained through ASF to uso@asf.alaska.edu.

Format Example
SMAP data [year of data acquisition] (NASA). Dataset: [name of dataset]. Retrieved from ASF [add URL if print publication: asf.alaska.edu] DAAC [day month year of data access]. DOI: [doi] SMAP data 2015 (NASA). Dataset: SMAP SMAP_L1B_S0_LoRes_V2. Retrieved from ASF DAAC 7 December 2015. DOI: https://doi.org/10.5067/j4szzv52b88j

Crediting SMAP Imagery

Include appropriate credit with each image shown in publications such as journal papers, articles, presentations, posters, and websites. For more information, see NASA guidelines.

Type Format Example
Imagery, primary or altered Credit: NASA data [year of data acquisition]. Credit: NASA data 2014.
Imagery, derivative Credit: [Name of image creator or institution, as appropriate], [year of image creation], contains NASA data [year of data acquisition]. Credit: Jones, D., 2015, contains NASA data 2015.

SMAP Level 1 Datasets and DOIs

Dataset DOI
L1A Radar 10.5067/DM0U37IYZ6NK
L1B_S0_LoRes 10.5067/NEWTOCOKVZHF
L1C_S0_HiRes 10.5067/NVVUJ0MNG3PN
SMAP_L1A_Radar_V2 10.5067/O383X9CEPGWV
SMAP_L1B_S0_LoRes_V2 10.5067/J4SZZV52B88J
SMAP_L1C_S0_HiRes_V2 10.5067/CBUK530QAO9M
SMAP_L1A_Radar_RO 10.5067/STFA3C0ZSXNC
SMAP_L1B_S0_LoRes_V3 10.5067/UCZC0LUSSQ0I
SMAP_L1C_S0_HiRes_V3 10.5067/E0QIAMXM89YY

SMAP – Publications and Credits

Publications

Please submit additional relevant publications to uso@asf.alaska.edu, with “SMAP Publications” on the subject line.

Assessment 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.

Automated 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 

The 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 

Tests 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.2013.2257605

A 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

L-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

NASA’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

Performance 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

A 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

A 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

SMAP 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

The 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

State 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.

Towards 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

US 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

Advances 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

Active 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

An 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

Application 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

Assessment 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

Comparison 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

Design 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

Development 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

A 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

End-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

An 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

Integration 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

A 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

AIAA 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

An 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.

Dense 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.

Effect 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.

IGARSS 2011 – Utilization of Ancillary Data Sets for SMAP Algorithm Development and Product Generation; Peggy E. O’Neill, Erika Podest, and Eni G. Njoku.

Improving 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

A 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

A 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

AGU 2010 – The Contribution of Soil Moisture Information to Forecast Skill: Two Studies; Randal Koster, Sarith Mahanama, and Ben Livneh.

AMS 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.

An 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.

An 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

Assimilation 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.

Backscattering 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

Evaluating 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

IGARSS 2010 – Forward Simulation of Passive Microwave Observation for the Soil Moisture Active Passive (SMAP) Mission; Steven Chan, Eni Njoku, and Scott Dunbar.

IGARSS 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. 

IGARSS 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. 

IGARSS 2010 – The NASA Soil Moisture Active Passive (SMAP) Mission: Overview; O’Neil, Entekhabi, Njoku and Kellogg.

The 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

MicroRad 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. 

PIEEE 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). 

PIERS 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. 

RadarCon 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. 

Soil Moisture Active Passive Poster – Joshua B. Fisher, Eni G. Njoku, Data Entekhabi.

Study 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.

2009 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.

2009 IEEE Radar Conference – RFI STUDY FOR THE SMAP RADAR; Chan S, Spencer M, Pages: 143-147 MAY 2009.

2009 IEEE Radar Conference – SMAP’s Radar OBP Algorithm Development ; Le C, Spencer MW, Veilleux L, et al., Pages 660-663 MAY 2009.

AIAA 2009 – The Soil Moisture Active and Passive Mission (SMAP), Njoku, E., K. Kellogg, P. O’Neill, and D. Entekhabi.

A 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.

AGU 2009, Fall – Development of Global High Resolution Soil Moisture Product from the SMAP mission; Narendra N. Das, Dara Entekhabi, Eni Njoku.  

A 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

A 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.

Combined 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.

Ecohydrological 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

IEEE Radar Conference – The Soil Moisture Active and Passive Mission (SMAP): Science and Applications; Entekhabi, D; O’Neill, P; Njoku, E, 83-85 2009.

IEEE Radar Conference 2009 – Soil Moisture Active and Passive Mission, Entekhabi, D., E. Njoku and P. O’Neill.

IGARSS 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.

IGARSS 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.

IGARSS 2009 – Algorithm development using the SMAP Algorithm Testbed, Chan, S., S. Dunbar, A. Colliander, E. Njoku and D. Entekhabi.

IGARSS 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. 

IGARSS 2009 – PALS-ADD and airborne campaigns to support soil moisture and sea surface salinity missions; Yueh, S. et al.

IGARSS 2009 – Space-borne soil moisture measurements in support of flood hydrology: The NASA SMAP approach; Crow et al.

Impact 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.

Improving 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

Inversion 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.

L-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.

PIERS 2009 – Retrieval Algorithm Development Based on SMEX02 Field Campaign Data for the Soil Moisture Active and Passive (SMAP) Mission; Chan and Njoku. 

PIERS 2009 – Soil Moisture Active and Passive (SMAP) Mission; Entekhabi et al.

Role 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.

Survey 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.

URSI 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.

2008 IEEE Radar Conference – THE SOIL MOISTURE ACTIVE/PASSIVE (SMAP) RADAR; Spencer, M; Kim, Y; Chan, S, 931-935 2008.

2008 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.

An 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.

Contribution 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.

IGARSS 2008 – Active-passive observations of soil moisture – Implementation concept for the SMAP mission, Njoku, E., D. Entekhabi, and P. O’Neill.

IGARSS 2008 – Hillslope-scale controls on remote sensing of soil moisture with microwave radiometry, Flores, A. V. Ivanov, D. Entekhabi, and R. Bras.

IGARSS 2008 – THE SOIL MOISTURE ACTIVE/PASSIVE MISSION (SMAP); Entekhabi et al.

PROCEEDINGS 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.

SPIE 2008 – Soil Moisture Active/Passive (SMAP) Mission Concept; T. J. Jackson, D. Entekhabi, E. Njoku, P. O’Neill, and J. Entin.

Comparison 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.

Fall 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.

IGARSS 2007 – Algorithm for High-Resolution Soil Moisture Retrieval With Coincident Active and Passive L-Band Measurements; Dara Entekhabi.

Impact 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.

A 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.

2006 IEEE MicroRad – L-band active and passive sensing of soil moisture through forests; Lang, RH, Chauhan, N; Utku, C; et al., : 193-196 2006.

Global 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.

Relevance 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

Bias 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.

Global 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.

IEEE 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.

Retrieval 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.

IEEE 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.

IEEE 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.

Soil 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.

IEEE 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.

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.

Seasat – References

View Seasat technical reports, general references, and publications focused on Seasat data processing, oceans, snow and ice, and land applications….

Illustration of the deployed Seasat spacecraft on orbit (image credit: NASA)

Seasat – Product Specification Guide

During its brief 106-days of lifetime, the Seasat-1 spacecraft, launched on June 28, 1978, by NASA’s Jet Propulsion Laboratory (JPL), collected information on sea-surface winds, sea-surface temperatures, wave heights, internal waves, atmospheric water, sea ice features, ice sheet topography, and ocean topography….

Seasat Raw Telemetry Format: Seasat minor frames are comprised of a 24-bit sync code, a 1-bit fill flag, a 7-bit frame number, 8 bits for time and status, and 1,140 bits of payload. The sync code signifies the beginning of each minor frame. The fill flag is supposed to be 1 when no valid data is being sent, 0 if the payload is valid. The frame number allows for sequencing of minor frames and the creation of range lines from them. The time and status bits encode several metadata fields. Finally, the payload contains the actual data samples recorded by the satellite.

Seasat – Technical Challenges – 1. Raw Telemetry

Seasat was not equipped with an onboard recorder, so in order to collect data during the mission, three U.S. and two international ground stations downlinked data from the satellite in real time: Fairbanks, Alaska; Goldstone, California; Merritt Island, Florida; Shoe Cove, Newfoundland; and Oakhanger, United Kingdom.

The data were originally archived on 39-track raw data tapes. Years later, to ensure the preservation of the data, those tapes were duplicated in 1988 and again in 1999. During the second transcription, the raw telemetry data were transferred onto 29, more modern SONY SD1-1300L 19-mm tapes. It is from these 13-year-old tapes that ASF’s online Seasat archive was obtained.

An off-the-shelf SAR processor was not available to decode or process Seasat raw telemetry data. However, ASF was able to use the Vexcel product SyncPrep to byte-align the data captured from disk, validate that the data appeared to be Seasat SAR data and estimate the bit error rate (BER) of the data. The BER provided insight into how much of the original SAR data could be processed to products and how difficult that process would be.

In the initial analysis of a 14 GB raw telemetry file, SyncPrep reported bit error rates as high as 0.4, or as many as 1 bit in 2.5 bits in error. This extreme level of “bit rot” persists for much of the Seasat archives and initially seemed to make much of the data unusable. Only through concerted efforts over the course of a year were approximately 90 percent of the Seasat SAR data able to be recovered.

1.1 Minor Frames and Range Lines

The commercial product SyncPrep, used for much of the raw data ingest at ASF, does not decode Seasat telemetry. So while SyncPrep will analyze the data, it will not actually decode metadata or create the range lines needed for focusing the raw data into SAR imagery. Accordingly, a decoder for Seasat raw signal data had to be developed at ASF. First, though, an understanding of the telemetry data format was required.

According to the Interface Control Document for Seasat, the telemetry stream is organized into repeated 1,180-bit telemetry packets, referred to as minor frames. Each minor frame begins with 40 bits of metadata followed by 1,140 bits of payload. The exact subdivision of the minor frames is diagramed below:

Seasat Raw Telemetry Format: Seasat minor frames are comprised of a 24-bit sync code, a 1-bit fill flag, a 7-bit frame number, 8 bits for time and status, and 1,140 bits of payload. The sync code signifies the beginning of each minor frame. The fill flag is supposed to be 1 when no valid data is being sent, 0 if the payload is valid. The frame number allows for sequencing of minor frames and the creation of range lines from them. The time and status bits encode several metadata fields. Finally, the payload contains the actual data samples recorded by the satellite.

Seasat Raw Telemetry Format: Seasat minor frames are comprised of a 24-bit sync code, a 1-bit fill flag, a 7-bit frame number, 8 bits for time and status, and 1,140 bits of payload. The sync code signifies the beginning of each minor frame. The fill flag is supposed to be 1 when no valid data is being sent, 0 if the payload is valid. The frame number allows for sequencing of minor frames and the creation of range lines from them. The time and status bits encode several metadata fields. Finally, the payload contains the actual data samples recorded by the satellite.

The payload in each minor frame is only 1,140 bits. A complete range line of Seasat data consists of the payload from 60 minor frames, each of which contains 228 samples of 5 bits each. So, in order to form range lines from the telemetry data, the payloads from up to 60 minor frames needed to be combined.

Each minor frame number 0 denotes the start of a range line. Minor frame numbers then increase until the start of the next range line, when they are reset to 0.

Aside: Determining Data Size

The pulse repetition frequency (PRF) Rate Code and the Bits Per Sample are required in order to determine the number of minor frames per range line. Seasat had four PRF rate codes: 1: 1464 Hz, 2: 1540 Hz, 3: 1581 Hz, and 4: 1647 Hz. For the entire mission, Seasat stayed with a PRF rate code of 4 and a Bits Per Sample of 5. This should result in 60 to 69 minor frames per range line according to the platform specifications. ASF engineers found that no range line had more than 60 minor frames; they always had either 59 or 60 minor frames. Thus, the output range lines were sized using:

                  60 minor frames * 1,140 bits/frame * 1 sample/5 bits = 13,680 samples

Thus, 13,680 should be the final size of a single range line once it is decoded into byte samples. For each such range line created, a set of 18 metadata values are also generated.

Range Line Creation: Creation of a single range line of data requires combining the payload from up to 60 minor frames in order. The 228 samples from minor frame 0 go into the output range line first, followed by the 228 samples from minor frame 1, those from minor frame 2, etc., until all minor frames for this range line have been unpacked. When only 59 frames are in a range line, the remaining 13,680 samples of output for that range line are set to zero. Concurrent with data sample unpacking, image metadata from the Time and Status bits in the first 10 minor frames are decoded and stored.

Range Line Creation: Creation of a single range line of data requires combining the payload from up to 60 minor frames in order. The 228 samples from minor frame 0 go into the output range line first, followed by the 228 samples from minor frame 1, those from minor frame 2, etc., until all minor frames for this range line have been unpacked. When only 59 frames are in a range line, the remaining 13,680 samples of output for that range line are set to zero. Concurrent with data sample unpacking, image metadata from the Time and Status bits in the first 10 minor frames are decoded and stored.

1.2 Subcommutated Header Fields

The time and status bits encode 18 metadata fields subcommutated in the first 10 minor frames of each range line, i.e. each of these fields need to be created using certain bits from certain numbered minor frames. For example, the Last Digit of Year can be found in bits 33-36 of minor frame 0, giving a range of values from 0-15. Of course, this field should always be 8, as 1978 was the year Seasat was in operation.

The metadata field Day of Year must be created using bits 33-37 of minor frame 4 as the lower-order 5 bits, and bits 37-40 of minor frame 5 as the higher order 4 bits, giving a 9-bit value. Similarly, the MSEC of Day field is 27 bits long and constructed from all or parts of the time and status bytes from minor frames 1-4. The following table shows how each of the 18 metadata fields were created.

Time and Status Byte: Locations of subcommutated metadata values. Only minor frames 0 through 9 contain valid time and status words; in all other minor frames, the time and status word is unused.

Time and Status Byte: Locations of subcommutated metadata values. Only minor frames 0 through 9 contain valid time and status words; in all other minor frames, the time and status word is unused.

Metadata Field Definition Type Notes
Station Code Signifies which ground station collected the data during the mission Constant 5: Fairbanks, AK; 6: Goldstone, CA; 7: Merrit Island, FL; 9: Oak Hangar, United Kingdom; 10: Shoe Cove, Newfoundland.
Last Digit of Year Last digit of the year Constant Should always be 8, since the mission was in 1978
Day of Year Julian day of the year Rarely Since no datatakes could possibly be more than a day, this value will change at most once in a datatake.
MSEC of Day Millisecond of the day Linear Should change consistently throughout a datatake
Clock Drift Timing offset in spacecraft clock Curve Must be added to the other times in order to get proper spacecraft locations
No Scan Indicator Unused Bit Field Unused
Bits Per Sample Number of bits per data sample Constant Throughout the mission, this value was always 5
MFR Lock Bit Unused Bit Field Unused
PFR Rate Code Pulse Repetition Frequency Code Constant Throughout the mission this value was always 4, denoting a PRF of 1647 Hz
Delay to Digitization Delay between sending pulses and when pulses are listened for Rarely Used to calculate the slant range to the first pixel in a datatake
SCU Bit Unused Bit Field Unused
SDF Bit Unused Bit Field Unused
ADC Bit Unused Bit Field Unused
Time Gate Bit Unused Bit Field Unused
Local PRF Bit Unused Bit Field Unused
Auto PRF Bit Unused Bit Field Unused
PRF Lock Bit Unused Bit Field Unused
Local Delay Bit Unused Bit Field Unused

Metadata Fields in the Raw Data: Eighteen fields of metadata can be decoded for each range line created during decoding. There are 10 bit fields, four fields that should be constant for a datatake, two fields that should change rarely, and two fields that should change steadily.

Written by Tom Logan, July 2013

Seasat – How to Cite

Citing Seasat Data

Cite data in publications such as journal papers, articles, presentations, posters, and websites. Please send copies of, or links to, published works citing data, imagery, or tools accessed through ASF to uso@asf.alaska.edu with “New Publication” on subject line.

Format Example
Seasat data 1978 (NASA). Processed by ASF DAAC 2013. Retrieved from ASF DAAC [add URL if print publication: asf.alaska.edu] [day month year of data access]. Seasat data 1978 (NASA). Processed by ASF DAAC 2013. Retrieved from ASF DAAC 7 December 2015.

Crediting Seasat Imagery

Include appropriate credit with each image shown in publications such as journal papers, articles, presentations, posters, and websites. For more information, see NASA guidelines.

Example
Credit: NASA 1978, processed by ASF DAAC 2013.

Seasat Datasets and DOIs

Dataset DOI
L1_Seasat 10.5067/LZ2D3Z6BW3GH