Earth Science

SMAP

1,824 tracked publications and 39,628 citations from 2015–2026. Soil Moisture Active and Passive. Launched 2015. Life-cycle cost: $1.3B in 2025 dollars. Active Mission Window April 1, 2015 to January 1, 2024: 1,154 publications, 142 top-10% credit.

Orbiter · h-index 91 · 80 papers with 100+ citations · prime mission ended 2018


Lifetime

Scope Papers published from the first full month after science operations begin through 2 years after the mission ends. Citations are counted through the third calendar year after each paper appears. Methods Papers published from the first full month after science operations begin through 2 years after the prime mission ends. Citations are counted through the third calendar year after each paper appears. Methods Every tracked publication to date, with every citation to date. Methods

Key measures

Active Mission Window · papers April 1, 2015 to January 1, 2024
Tracked publicationsTracked publications: peer-reviewed research papers we found for the mission. The list may not be complete. Methods1,154
CitationsCitations received by the tracked publications in the selected scope. The two windowed scopes count each paper’s citations only within its citation window. Methods18,807
Mean citations per publicationMean citations: total citations divided by tracked publications in the selected scope. One blockbuster paper can lift it. Methods16
Median citations per publicationMedian citations: the middle paper’s citation count in the selected scope; half the papers have more, half fewer. A single blockbuster sways it less than the mean. Methods10
Uncited publicationsUncited publications: papers with no citations in the selected scope. Methods32 (2.8%)
Top-10% creditTop 10%: among the 10% most-cited papers from this division’s missions, ranked against papers published around the same time. A paper naming several missions is split evenly among the missions in this division that claim it, so counts can be fractional. Methods142 · 3.7% of division
Top-1% creditTop 1%: among the 1% most-cited papers from this division’s missions within the selected window. Not adjusted for publication year. A paper naming several missions is split evenly among the missions in this division that claim it, so counts can be fractional. Methods20 · 5.3% of division
Lifetime indices · every tracked publication to date, in any scope
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. Methods91
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. Methods135
m-index, as of October 4, 2026m-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, so it belongs to the date shown; it also discounts the long operating life that larger missions paid for. Methods7.6
toritori (total research impact, from ADS): 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. Methods128
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. Methods871

Tracked publications

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

    Colliander, A., 2017, RSEnv

    569 citations

  2. Assessment of the SMAP Passive Soil Moisture Product

    Chan, Steven K., 2016, ITGRS

    490 citations

  3. The global distribution and dynamics of surface soil moisture

    McColl, Kaighin A., 2017, NatGe

    434 citations

  4. 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

    400 citations

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

    Chan, S. K., 2018, RSEnv

    391 citations

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

    Chew, C. C., 2018, GeoRL

    273 citations

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

    Lettenmaier, Dennis P., 2015, WRR

    256 citations

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

    Reichle, Rolf H., 2017, JHyMe

    251 citations

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

    Ma, Hongliang, 2019, RSEnv

    235 citations

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

    Li, Qingliang, 2022, ESSD

    228 citations

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

    Zhang, Dianjun, 2016, Senso

    226 citations

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

    Chen, Fan, 2018, RSEnv

    221 citations

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

    Mohanty, Binayak P., 2017, VZJ

    216 citations

  14. Analysis of CYGNSS Data for Soil Moisture Retrieval

    Clarizia, Maria Paola, 2019, IJSTA

    209 citations

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

    McNairn, Heather, 2015, ITGRS

    208 citations

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

    Al-Yaari, A., 2019, RSEnv

    205 citations

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

    Konings, Alexandra G., 2017, RSEnv

    204 citations

  18. SMAP soil moisture improves global evapotranspiration

    Purdy, Adam J., 2018, RSEnv

    204 citations

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

    Das, Narendra N., 2019, RSEnv

    196 citations

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

    Fang, Kuai, 2017, GeoRL

    194 citations

How we found these papers

We searched NASA’s Astrophysics Data System (ADS) for peer-reviewed articles naming SMAP in the title, abstract or keywords; standard filters drop articles that are not peer-reviewed and magazine pieces. SciX is ADS’s current interface.

((=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 "AIASJ" OR "AeAm" OR "AirSp" OR "AsNow" OR "AsUAI" OR "AvWST" OR "C&E" OR "C&T" OR "CAPJ" OR "E&S" OR "ENews" OR "IrAJ" OR "JCos" OR "JRASC" OR "LAstr" OR "MNSSA" OR "Met" OR "NewSc" OR "Orion" OR "PhT" OR "PhTea" OR "PhuZ" OR "PhyOJ" OR "PhyW" OR "PlR" OR "SciAm" OR "SpFl" 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 🤖