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Description

This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

Version Description

Changes to this version include:
  • Transitioned to Validated-Stage 2
  • Using SPL2SMAP V3 Validated data as input
For the full major and minor version history, go to https://nsidc.org/data/smap/version-history

Product Summary

Platforms
SMAP
Instruments
SMAP L-BAND RADAR ,
SMAP L-BAND RADIOMETER
Spatial Extent

N: 85.044 S: -85.044 E: 180 W: -180

Spatial Resolution
9 Kilometers x 9 Kilometers
Spatial Reference System(s)
WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Location
GLOBAL LAND
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
2015-04-13 to 2015-07-07
Temporal Resolution
1 Day
Concept ID
C2872767144-NSIDC_CPRD
Data State
COMPLETE
Number of Files/Granules
84
Processing Level
3
Published
Science Keywords
Brightness Temperature ,
SIGMA NAUGHT ,
Soil Moisture/Water Content

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Entekhabi, Dara, Das, Narendra, Njoku, Eni, Johnson, Joel, & Shi, Jiancheng. (2016). SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 [Data set]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/7KKNQ5UURM2W Date Accessed: 2026-11-06

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Publications Citing This Dataset

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Title Authors Year Referenced Datasets
Regional nudged storylines for the European hot and dry summers 20182022: response of evapotranspiration regimes to climate change
  • Klimiuk, Tatiana, Sanchez-Benitez, Antonio, Ludwig, Patrick, Pinto, Joaquim G.
How Does Assimilating SMAP Soil Moisture Improve Characterization of the Terrestrial Water Cycle in an Integrated Land Surface-Subsurface Model?
  • Zhao, Haojin, Montzka, Carsten, Keller, Johannes, Li, Fang, Vereecken, Harry, Hendricks Franssen, HarrieJan
A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning
  • Gungor Sahin, Onur, Gunduz, Orhan
Multi-Decadal Soil Moisture and Crop Yield VariabilityA Case Study With the Community Land Model (CLM5)
  • Boas, Theresa, Bogena, Heye, Ryu, Dongryeol, Western, Andrew, Hendricks Franssen, HarrieJan
Forward and backward full-pol scattering analysis using SMAP reflectometer and radar datasets
  • Perez-Portero, Adrian, Munoz-Martin, Joan Francesc, Rodriguez-Alvarez, Nereida, Bosch-Lluis, Xavier, Oudrhiri, Kamal
Estimating Backward Scattering using GNSS-Reflectometry Measurements for Soil Moisture Retrieval
  • Perez-Portero, Adrian, Rodriguez-Alvarez, Nereida, Francesc Munoz-Martin, Joan, Bosch-Lluis, Xavier, Oudrhiri, Kamal
Evaluation of SMAP and CYGNSS Soil Moistures in Drought Prediction Using Multiple Linear Regression and GLDAS Product
  • Edokossi, Komi, Jin, Shuanggen, Calabia, Andres, Molina, Inigo, Mazhar, Usman
Deducing land-atmosphere coupling regimes from SMAP soil moisture
  • Makhasana, Payal R., Santanello, Joseph A., Lawston-Parker, Patricia M., Roundy, Joshua K.
Diurnal Vegetation Moisture Cycle in the Amazon and Response to Water
  • Asgarimehr, Milad, Entekhabi, Dara, Camps, Adriano
Analysis of signal-to-noise ratio retrieved from multi-GNSS satellite data of land surface reflections
  • Liu, Qi, Li, Weiqiang, Zhang, Shuangcheng, Nan, Yang, Peng, Jilun, Ma, Zhongmin
Assessment of Surface Fractional Water Impacts on SMAP Soil Moisture Retrieval
  • Du, Jinyang, Kimball, John S., Chan, Steven K., Chaubell, Mario Julian, Bindlish, Rajat, Dunbar, R. Scott, Colliander, Andreas
Coccidioides undetected in soils from agricultural land and uncorrelated with time or the greater soil fungal community on undeveloped land
  • Wagner, Robert, Montoya, Liliam, Head, Jennifer R., Campo, Simon, Remais, Justin, Taylor, John W.
NASA's NMME-based S2S Hydrologic Forecast System for Food Insecurity Early Warning in Southern Africa
  • Hazra, Abheera, McNally, Amy, Slinski, Kimberly, Arsenault, Kristi R., Shukla, Shraddhanand, Getirana, Augusto, Jacob, Jossy P., Sarmiento, Daniel P., Peters-Lidard, Christa, Kumar, Sujay V., Koster, Randal D.
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land ...
  • Boas, Theresa, Bogena, Heye Reemt, Ryu, Dongryeol, Vereecken, Harry, Western, Andrew, Hendricks Franssen, Harrie-Jan
A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan
  • McNally, Amy, Jacob, Jossy, Arsenault, Kristi, Slinski, Kimberly, Sarmiento, Daniel P., Hoell, Andrew, Pervez, Shahriar, Rowland, James, Budde, Mike, Kumar, Sujay, Peters-Lidard, Christa, Verdin, James P.
Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution
  • Rouf, Tasnuva, Girotto, Manuela, Houser, Paul, Maggioni, Viviana
On the Relationship Between Radar Backscatter and Radiometer Brightness Temperature From SMAP
  • Zeng, Jiangyuan, Shi, Pengfei, Chen, Kun-Shan, Ma, Hongliang, Bi, Haiyun, Cui, Chenyang
Improved Understanding of the Link Between Catchment-Scale Vegetation Accessible Storage and Satellite-Derived Soil Water Index
  • Bouaziz, Laurene J. E., SteeleDunne, Susan C., Schellekens, Jaap, Weerts, Albrecht H., Stam, Jasper, Sprokkereef, Eric, Winsemius, Hessel H. C., Savenije, Hubert H. G., Hrachowitz, Markus
Improving SMAP freeze-thaw retrievals for pavements using effective soil temperature from GEOS-5: Evaluation against in situ road temperature data over the US
  • Kraatz, Simon, Jacobs, Jennifer M., Schroder, Ronny, Cho, Eunsang, Miller, Heather J., Vuyovich, Carrie M.
Gap filling of highresolution soil moisture for SMAP/sentinel1: a twolayer machine learningbased framework
  • Mao, Hanzi, Kathuria, Dhruva, Duffield, Nick, Mohanty, Binayak P.
Analysis of the Radar Vegetation Index and Potential Improvements
  • Szigarski, Christoph, Jagdhuber, Thomas, Baur, Martin, Thiel, Christian, Parrens, Marie, Wigneron, Jean-Pierre, Piles, Maria, Entekhabi, Dara
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