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Description

This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

Version Description

Changes to this version include:

  • As part of the algorithm changes, the following data fields were added: bulk_density, clay_fraction.
  • The baseline algorithm (SCA-V) remains unchanged.
  • Improved aggregation of values in input ancillary data, e.g. roughness, soil texture, NDVI. The fix has negligible impacts on retrievals estimated to be of recommended quality.
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 RADIOMETER
Spatial Extent

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

Spatial Resolution
36 Kilometers x 36 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
1 Day
Concept ID
C2872767284-NSIDC_CPRD
Data State
DEPRECATED
Number of Files/Granules
0
Processing Level
3
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

ONeill, P., Chan, S., Njoku, E., Jackson, T., Bindlish, R., & Chaubell, J. (2019). SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 6 [Dataset]. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/EVYDQ32FNWTH Date Accessed: 2026-09-28

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Publications Citing This Dataset

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Title Authors Year Referenced Datasets
The Simulated Role of the Antecedent Snow and Soil Moisture Conditions on the 1316 March 2021 Dust Storm in East Asia
  • Letcher, Theodore, LeGrand, Sandra, Sparrow, Kent, Eylander, John
The Land Component LM4.1 of the GFDL Earth System Model ESM4.1: Model
  • Shevliakova, E., Malyshev, S., MartinezCano, I., Milly, P. C. D., Pacala, S. W., Ginoux, P., Dunne, K. A., Dunne, J. P., Dupuis, C., Findell, K. L., Ghannam, K., Horowitz, L. W., Knutson, T. R., Krasting, J. P., Naik, V., Phillipps, P., Zadeh, N., Yu, Yan, Zeng, F., Zeng, Y.
Ocean Salinity Retrieval and Prediction for Soil Moisture Active Passive Satellite Using Data-To-Data Translation
  • Han, Kyung-Hoon, Hong, Sungwook
Surface Soil Moisture Retrieval of China Using Multi-Source Data and
  • Yang, Zhangjian, He, Qisheng, Miao, Shuqi, Wei, Feng, Yu, Mingxiao
A new snow depth data set over northern China derived using GNSS interferometric reflectometry from a continuously operating network (GSnow-CHINA v1. 0 ...
  • Wan, Wei, Zhang, Jie, Dai, Liyun, Liang, Hong, Yang, Ting, Liu, Baojian, Guo, Zhizhou, Hu, Heng, Zhao, Limin
Soil moisture estimation in South Asia via assimilation of SMAP retrievals
  • Ahmad, Jawairia A., Forman, Barton A., Kumar, Sujay V.
Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information
  • Zhou, Jianhong, Crow, Wade T., Wu, Zhiyong, Dong, Jianzhi, He, Hai, Feng, Huihui
Soil moisture estimates over sporadically flooded farmlands: synergies and biases of remote sensing and in situ sources
  • Cappelletti, Lucia M., Sorensson, Anna A., Salvia, Mercedes, Ruscica, Romina C., Spennemann, Pablo, Fernandez-Long, Maria E., Jobbagy, Esteban
Soil moisture retrieval using BuFeng-1 A/B based on land surface clustering algorithm
  • Guo, Zhizhou, Liu, Baojian, Wan, Wei, Lu, Feng, Niu, Xinliang, Ji, Rui, Jing, Cheng, Li, Weiqiang, Chen, Xiuwan, Yang, Jun, Bai, Zhaoguang
Development of a physics-informed data-driven model for gaining insights into hydrological processes in irrigated watersheds
  • Li, Kailong, Huang, Guohe, Wang, Shuo, Razavi, Saman
Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through
  • Zhang, Zhen, Chatterjee, Abhishek, Ott, Lesley, Reichle, Rolf, Feldman, Andrew F., Poulter, Benjamin
Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis
  • Wu, Kai, Ryu, Dongryeol, Nie, Lei, Shu, Hong
A triple collocation-based 2D soil moisture merging methodology considering spatial and temporal non-stationary errors
  • Zhou, Jianhong, Crow, Wade T., Wu, Zhiyong, Dong, Jianzhi, He, Hai, Feng, Huihui
Integrating Soil Moisture Active/Passive Observations with Rainfall Data Using an Analytic Model for Drought Monitoring at the Continental Scale
  • Ma, Feng, Luo, Lifeng, Ye, Aizhong
Relationship between active and passive microwave signals over vegetated surfaces
  • Link, Moritz, Jagdhuber, Thomas, Ferrazzoli, Paolo, Guerriero, Leila, Entekhabi, Dara
Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sens. 2021, 13, 133
  • Sun, Hao, Cui, Yajing
Evaluation of the Land GNSS-Reflected DDM Coherence on Soil Moisture
  • Dong, Zhounan, Jin, Shuanggen
Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites
  • Lee, Junchan, Bisnath, Sunil, Lee, Regina S.K., Kilane, Narin Gavili
Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data
  • Tyagi, Shivani, Pandey, Dharmendra Kumar, Putrevu, Deepak, Srivastava, Prashant K., Misra, Arundhati
Improving Spatial Patterns Prior to Land Surface Data Assimilation via Model Calibration Using SMAP Surface Soil Moisture Data
  • Zhou, Jianhong, Wu, Zhiyong, Crow, Wade T., Dong, Jianzhi, He, Hai
A New Soil Moisture Retrieval Algorithm from the L-Band Passive Microwave Brightness Temperature Based on the Change Detection Principle
  • Zheng, Xingming, Feng, Zhuangzhuang, Xu, Hongxin, Sun, Yanlong, Li, Lei, Li, Bingze, Jiang, Tao, Li, Xiaojie, Li, Xiaofeng
Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale
  • Li, Wei, Ciais, Philippe, Stehfest, Elke, van Vuuren, Detlef, Popp, Alexander, Arneth, Almut, Di Fulvio, Fulvio, Doelman, Jonathan, Humpenoder, Florian, Harper, Anna B., Park, Taejin, Makowski, David, Havlik, Petr, Obersteiner, Michael, Wang, Jingmeng, Krause, Andreas, Liu, Wenfeng
Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data
  • Yan, Qingyun, Huang, Weimin, Jin, Shuanggen, Jia, Yan
Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers
  • Khazaei, Morteza, Hamzeh, Saeid, Weng, Qihao
Global soil moisture estimation using CYGNSS data
  • Yan, Qingyun, Jin, Shuanggen, Huang, Weimin, Jia, Yan
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