NASA-USDA Global Soil Moisture Data

Dataset Availability
2010-01-01T00:00:00 - 2020-09-20T00:00:00
Dataset Provider
Earth Engine Snippet
geophysical soil moisture nasa usda hsl smos


The NASA-USDA Global soil moisture and the NASA-USDA SMAP Global soil moisture datates provide soil moisture information across the globe at 0.25°x0.25° spatial resolution. These datasets include surface and subsurface soil moisture (mm), soil moisture profile (%), and surface and subsurface soil moisture anomalies. Soil moisture anomalies are unitless and represent standardized anomalies computed using a 31-days moving window. Values around 0 indicate typical moisture conditions, while very positive and very negative values indicate extreme wetting (soil moisture conditions are above average) and drying (soil moisture conditions are below average), respectively.

This dataset is generated by integrating satellite-derived Soil Moisture Ocean Salinity (SMOS) Level 2 soil moisture observations into the modified two-layer Palmer model using a 1-D Ensemble Kalman Filter (EnKF) data assimilation approach. The assimilation of the SMOS soil moisture observations helped improve the model-based soil moisture predictions particularly over poorly instrumented areas (e.g., Southern African, Middle East) of the world that lack good quality precipitation data.

This dataset was developed by the Hydrological Science Laboratory (HSL) at NASA’s Goddard Space Flight Center in cooperation with USDA Foreign Agricultural Services and USDA Hydrology and Remote Sensing Lab.


0.25 arc degrees


Name Units Min Max Description
ssm mm 0* 25.39*

Surface soil moisture

susm mm 0* 274.6*

Subsurface soil moisture

smp fraction 0* 1*

Soil moisture profile

ssma - -4* 4*

Surface soil moisture anomaly

susma - -4* 4*

Subsurface soil moisture anomaly

* estimated min or max value

Terms of Use

Terms of Use

This dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.


  • Bolten, J., W.T. Crow, X. Zhan, T.J. Jackson, and C.A. Reynolds (2010). Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring, IEEE Transactions on Geoscience and Remote Sensing, 3(1): 57-66. DOI 10.1109/JSTARS.2009.2037163 Google Scholar

  • Bolten, J., and W. T. Crow (2012). Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture, Geophysical Research Letters, 39: (L19406). DOI 10.1029/2012GL053470 Google Scholar

  • I. E. Mladenova, J.D. Bolten, W.T. Crow, M.C. Anderson, C.R. Hain, D.M. Johnson, R. Mueller (2017). Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1328-1343, DOI 10.1109/JSTARS.2016.2639338

  • Sazib, N., I. E. Mladenova, J.D. Bolten (2018). Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data. Remote Sensing, 10(8), p.1265. DOI 10.3390/rs10081265 Google Scholar

  • Kerr, Y. H., and D. Levine (2008). Forward to the special issue on the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing, 46(3): 583–585. DOI 10.1109/TGRS.2008.917807 Google Scholar

Explore in Earth Engine

var dataset = ee.ImageCollection('NASA_USDA/HSL/soil_moisture')
                  .filter('2017-04-01', '2017-04-30'));
var soilMoisture ='ssm');
var soilMoistureVis = {
  min: 0.0,
  max: 28.0,
  palette: ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
Map.setCenter(-6.746, 15.529, 2);
Map.addLayer(soilMoisture, soilMoistureVis, 'Soil Moisture');