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Description

SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products:

  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/KPJNN2GI1DQR)
  • SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/60HB8VIP2T8W)
  • SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KGLC3UH4TMAQ).
    For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.

Version Description

Changes to this version include: Revisions to the land surface modeling system; Revisions to the parameters and parameterizations of the surface energy balance and the snow depletion curve; Analysis increments are no longer computed for the “catchment deficit” model prognostic variable in the Ensemble Kalman filter update step; Added x and y coordinate variables as well as an EASE-Grid 2.0 projection grid mapping variable; Minor bug fixes. For the full major and minor version history, go to https://nsidc.org/data/smap/data_versions.

Product Summary

Platforms
SMAP
Instruments
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
2015-03-31 to 2020-08-27
Temporal Resolution
3 Hour
Concept ID
C2872768088-NSIDC_CPRD
Data State
DEPRECATED
Number of Files/Granules
0
Processing Level
4
Published
Updated
Science Keywords
Surface Soil Moisture

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

Reichle, R., De Lannoy, G., Koster, R., Crow, W., Kimball, J., & Liu, Q. (2018). SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4 [Dataset]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/60HB8VIP2T8W Date Accessed: 2026-09-24

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Publications Citing This Dataset

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Title Authors Year Referenced Datasets
Northwest boundary of the western Pacific subtropical high and its relationship with the soil moisture in southern China during boreal summer
  • Liu, Binshuo, Zhou, Yang, Lei, Boyang, Sun, Shanlei, Chen, Haishan
A Multimodal Deep Learning Approach for Soil Moisture Downscaling Using Remote Sensing and Weather Data
  • Xu, Yijia, Cai, Shuohao, Huang, Jingyi, Liu, Jiangui, Shang, Jiali, Yang, Zhengwei, Zhang, Zhou
Assessing and comparing the subseasonal variations of summer soil moisture of satellite products over eastern China
  • Zhou, Yang, Chen, Haishan, Sun, Shanlei
Dynamics of pedogenic carbonate growth in the tropical domain of Myanmar
  • Licht, A., Kelson, J., Bergel, S., Schauer, A., Petersen, S. V., Capirala, A., Huntington, K. W., DupontNivet, G., Win, Zaw, Aung, Day Wa
The contributions of gauge-based precipitation and SMAP brightness temperature observations to the skill of the SMAP Level-4 soil moisture product
  • Reichle, Rolf H., Liu, Qing, Ardizzone, Joseph V., Crow, Wade T., De Lannoy, Gabrielle J. M., Dong, Jianzhi, Kimball, John S., Koster, Randal D.
Root Zone Soil Moisture Comparisons: AirMOSS, SMERGE, and SMAP
  • Tobin, Kenneth J., Crow, Wade T., Bennett, Marvin E.
The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis
  • Qiu, Jianxiu, Dong, Jianzhi, Crow, Wade T., Zhang, Xiaohu, Reichle, Rolf H., De Lannoy, Gabrielle J. M.
Nitrogenfixing trees have no net effect on forest growth in the coterminous United States
  • Staccone, Anika P., KouGiesbrecht, Sian, Taylor, Benton N., Menge, Duncan N. L.
Sub-seasonal variability of surface soil moisture over eastern China
  • Zhou, Yang, Dong, Xuan, Chen, Haishan, Cao, Lu, Shao, Qing, Sun, Shanlei, Yang, Ben, Rao, Jian
Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 US drought
  • Li, Xing, Xiao, Jingfeng, Kimball, John S., Reichle, Rolf H., Scott, Russell L., Litvak, Marcy E., Bohrer, Gil, Frankenberg, Christian
The hurricane harvey (2017) Texas rainstorm: Synoptic analysis and sensitivity to soil moisture
  • Galarneau, Thomas J., Zeng, Xubin
Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
  • Vergopolan, Noemi, Chaney, Nathaniel W., Beck, Hylke E., Pan, Ming, Sheffield, Justin, Chan, Steven, Wood, Eric F.
Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the
  • Xu, Xiaoyong
Investigating the Role of Extreme Synoptic Patterns and Complex Topography During Two Heavy Rainfall Events in Crete in February 2019
  • Lagouvardos, Konstantinos, Dafis, Stavros, Giannaros, Christos, Karagiannidis, Athanassios, Kotroni, Vassiliki
Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product
  • Reichle, Rolf H., Liu, Qing, Koster, Randal D., Crow, Wade T., De Lannoy, Gabrielle J. M., Kimball, John S., Ardizzone, Joseph V., Bosch, David, Colliander, Andreas, Cosh, Michael, Kolassa, Jana, Mahanama, Sarith P., Prueger, John, Starks, Patrick, Walker, Jeffrey P.
Assessing the feasibility of satellitebased thresholds for hydrologically driven landsliding
  • Thomas, Matthew A., Collins, Brian D., Mirus, Benjamin B.
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
  • Sun, Alexander Y, Scanlon, Bridget R
Landslides triggered by Hurricane Maria: Assessment of an extreme event in Puerto Rico
Verification of the SMAP Level-4 Soil Moisture Analysis Using Rainfall Observations in Australia
  • Reichle, R., Liu, Q., Lannoy, G. De, Crow, W., Jones, L., Kimball, J., Koster, R.
Time to branch out? Application of hierarchical survival models in plant phenology
  • Elmendorf, Sarah C., Crimmins, Theresa M., Gerst, Katharine L., Weltzin, Jake F.
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