NASA-USDA SMAP Global Soil Moisture Data [deprecated]

  • This dataset, superseded by NASA/SMAP/SPL4SMGP/007, provides global soil moisture information from 2015-04-02 to 2020-12-31 with a 3-day cadence.

  • The dataset includes surface and subsurface soil moisture in millimeters, soil moisture profile in percentage, and unitless soil moisture anomalies at a 0.25°x0.25° spatial resolution.

  • Soil moisture anomalies indicate typical (around 0), extreme wetting (positive), or extreme drying (negative) conditions compared to average.

  • The data is generated by integrating SMAP Level 3 observations into a modified two-layer Palmer model using an Ensemble Kalman Filter approach.

  • This dataset was developed by NASA GSFC in cooperation with USDA and is in the public domain.
NASA_USDA/HSL/SMAP_soil_moisture
Dataset Availability
2015-04-02T12:00:00Z–2020-12-31T12:00:00Z
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("NASA_USDA/HSL/SMAP_soil_moisture")
Cadence
3 Days
Tags
geophysical
hsl
nasa
smap
soil
soil-moisture
usda

Description

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 Active Passive (SMAP) Level 3 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 SMAP soil moisture observations helped improve the model-based soil moisture predictions particularly over poorly instrumented areas 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.

Bands

Pixel Size
27830 meters

Bands

Name Units Min Max Pixel Size Description
ssm mm 0* 25.39* meters

Surface soil moisture
susm mm 0* 274.6* meters

Subsurface soil moisture
smp Fraction 0* 1* meters

Soil moisture profile
ssma Dimensionless -4* 4* meters

Surface soil moisture anomaly
susma Dimensionless -4* 4* meters

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.

Citations

Citations:

  • 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

  • Entekhabi, D, Njoku, EG, O'Neill, PE, Kellogg, KH, Crow, WT, Edelstein, WN, Entin, JK, Goodman, SD, Jackson, TJ, Johnson, J, Kimball, J, Piepmeier, JR, Koster, RD, Martin, N, McDonald, KC, Moghaddam, M, Moran, S, Reichle, R, Shi, JC, Spencer, MW, Thurman, SW, Tsang, L & Van Zyl, J (2010). The soil moisture active passive (SMAP) mission, Proceedings of the IEEE, 98(5): 704-716. doi:10.1109/JPROC.2010.2043918 Article

  • O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish (2016). SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, Version 4. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center.doi:10.5067/ZX7YX2Y2LHEB

DOIs

Explore with Earth Engine

Code Editor (JavaScript)

var dataset = ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture')
                  .filter(ee.Filter.date('2017-04-01', '2017-04-30'));
var soilMoisture = dataset.select('ssm');
var soilMoistureVis = {
  min: 0.0,
  max: 28.0,
  palette: ['0300ff', '418504', 'efff07', 'efff07', 'ff0303'],
};
Map.setCenter(-6.746, 46.529, 2);
Map.addLayer(soilMoisture, soilMoistureVis, 'Soil Moisture');
Open in Code Editor
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 …
NASA_USDA/HSL/SMAP_soil_moisture, geophysical,hsl,nasa,smap,soil,soil-moisture,usda
2015-04-02T12:00:00Z/2020-12-31T12:00:00Z
-60 -180 90 180
Google Earth Engine