Earth Science

Soil Moisture Active and Passive (SMAP)

Orbiter · Directed · Agency-led · Launched on January 31, 2015 · Extended Mission · LCC: $916M/$1.3B (2025 $)Life-cycle cost: NASA’s reported cost to build, launch, and operate a mission through its prime mission, plus documented partner contributions, in 2025 dollars. Extended missions are not included. Methods · Wikipedia

Scope Active Mission WindowActivePapers published from the first full month after science operations begin through two years after the mission ends. Citations are counted through the third calendar year after each paper appears. For SMAP, papers published April 1, 2015 to January 1, 2024. Methods LifetimeLifetimeEvery tracked publication to date, with every citation to date. For SMAP, papers published from 2015 to November 3, 2026. Methods
Tracked publicationsTracked publications: peer-reviewed papers we found that name the mission or its instruments in their titles, abstracts, or keywords. Papers that used the data without naming it are missed. Methods1,846
CitationsCitations: every citation these publications have drawn to date. Older missions have had longer to collect them. Methods41,423
Citation indices
h-indexh-index: the largest h such that h papers have at least h citations each. It only grows with time, so older missions score higher. Methods93
g-indexg-index: the largest g for which the g most-cited papers together hold at least g² citations. Like the h-index, but it lets the most-cited papers count for more. Methods138
m-indexm-index: a mission’s h-index divided by the years since its first peer-reviewed paper. It falls every 1 January even when nothing else changes; the figure shown is as of November 3, 2026. It also discounts the long operating life that larger missions paid for. Methods7.8
toritori (total research impact, from SciX): for every paper citing one of the mission’s papers, 1 divided by the citing paper’s reference count times the cited paper’s author count, summed, with self-citations removed. It favors citations from papers with short reference lists and from outside the mission’s own authors. Computed over the tracked citation graph, which can be slightly incomplete. Always over every tracked publication to date, whichever scope is chosen. Methods171
riqriq (research impact quotient): 1,000 times the square root of tori, divided by the years since the mission’s first paper. A rate, not a total, so it does not keep growing with age the way the h-index and tori do. Like tori, always over every tracked publication to date. Methods1,005

Publications and citations by year (lifetime)

Publications per yearCumulative citations

Institutional contributions (lifetime)The institutions SciX lists for each paper’s authors, as printed on the paper, located through the Research Organization Registry. A square counts a paper once, however many of its authors worked there. Methods

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Tracked publications over the lifetime (1,846)

All publications CSV · JSON

  1. Validation of SMAP surface soil moisture products with core validation sites

    Colliander, A., 2017, RSEnv

    589 lifetime citations

  2. Assessment of the SMAP Passive Soil Moisture Product

    Chan, Steven K., 2016, ITGRS

    503 lifetime citations

  3. The global distribution and dynamics of surface soil moisture

    McColl, Kaighin A., 2017, NatGe

    457 lifetime citations

  4. Development and assessment of the SMAP enhanced passive soil moisture product

    Chan, S. K., 2018, RSEnv

    405 lifetime citations

  5. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms

    Wigneron, J.-P., 2017, RSEnv

    403 lifetime citations

  6. Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture

    Chew, C. C., 2018, GeoRL

    283 lifetime citations

  7. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

    Reichle, Rolf H., 2017, JHyMe

    265 lifetime citations

  8. Inroads of remote sensing into hydrologic science during the WRR era

    Lettenmaier, Dennis P., 2015, WRR

    262 lifetime citations

  9. A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

    Li, Qingliang, 2022, ESSD

    250 lifetime citations

  10. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations

    Ma, Hongliang, 2019, RSEnv

    239 lifetime citations

  11. Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation

    Chen, Fan, 2018, RSEnv

    237 lifetime citations

  12. Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

    Zhang, Dianjun, 2016, Senso

    233 lifetime citations

  13. Soil Moisture Remote Sensing: State-of-the-Science

    Mohanty, Binayak P., 2017, VZJ

    223 lifetime citations

  14. Analysis of CYGNSS Data for Soil Moisture Retrieval

    Clarizia, Maria Paola, 2019, IJSTA

    217 lifetime citations

  15. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

    Al-Yaari, A., 2019, RSEnv

    212 lifetime citations

  16. L-band vegetation optical depth and effective scattering albedo estimation from SMAP

    Konings, Alexandra G., 2017, RSEnv

    210 lifetime citations

  17. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

    McNairn, Heather, 2015, ITGRS

    209 lifetime citations

  18. SMAP soil moisture improves global evapotranspiration

    Purdy, Adam J., 2018, RSEnv

    208 lifetime citations

  19. Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

    Fang, Kuai, 2017, GeoRL

    206 lifetime citations

  20. The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product

    Das, Narendra N., 2019, RSEnv

    203 lifetime citations

How we found these papers

We searched NASA’s Science Explorer (SciX) for peer-reviewed articles naming SMAP in the title, abstract or keywords; standard filters drop articles that are not peer-reviewed and magazine pieces.

((=abs:"Soil Moisture Active Passive" OR =abs:"SMAP Reflectometry" OR (abs:SMAP AND (abs:(satellite OR soil OR radiometer OR microwave OR ocean OR freeze OR thaw OR vegetation OR drought OR SAR) OR =abs:salinity OR =abs:"remote sensing" OR =abs:"land surface" OR =abs:"gap filling" OR abs:"L-band"))) AND NOT =abs:"GIC-SMAP") AND property:refereed AND doctype:article AND pubdate:[2015-04 TO 2040-01] AND NOT bibstem:("A&R" OR "ACPD" OR "AIASJ" OR "AMTD" OR "AeAm" OR "AirSp" OR "AsNow" OR "AsUAI" OR "AvWST" OR "BGD" OR "C&E" OR "C&T" OR "CAPJ" OR "CliPD" OR "E&S" OR "ENews" OR "ESDD" OR "ESSDD" OR "ESuDD" OR "GID" OR "GMDD" OR "GeCAS" OR "HESSD" OR "IrAJ" OR "JCos" OR "JRASC" OR "LAstr" OR "MNSSA" OR "Met" OR "NHESD" OR "NPGD" OR "NewSc" OR "OcScD" OR "Orion" OR "PhT" OR "PhTea" OR "PhuZ" OR "PhyOJ" OR "PhyW" OR "PlR" OR "SOILD" OR "SciAm" OR "SpFl" OR "TCD" OR "ZemVs")

Open in SciX

Added after review (2)

  1. Improved Hydrological Simulation Using SMAP Data: Relative Impacts of Model Calibration and Data Assimilation Assimilates SMAP soil moisture data to improve hydrological simulations. Decision 🤖
  2. Using Smos Passive Microwave Data to Develop Smap Freeze/thaw Algorithms Adapted for the Canadian Subarctic Develops SMAP freeze/thaw algorithms for the Canadian subarctic. Decision 🤖

Removed after review (3)

  1. The Polarimetric L-Band Imaging Synthetic Aperture Radar (PLIS): Description, Calibration, and Cross-Validation Calibrates an airborne radar; mentions SMAP only through its field campaign names. Decision 🤖
  2. Flash flood in the mountainous region of Rio de Janeiro state (Brazil) in 2011: part I—calibration watershed through hydrological SMAP model Uses a hydrological model abbreviated SMAP; not the NASA satellite. Decision 🤖
  3. A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation Uses an image classification method abbreviated SMAP; not the NASA satellite. Decision 🤖