NASA-USDA Enhanced SMAP Global Soil Moisture Data

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NASA_USDA/HSL/SMAP10KM_soil_moisture
Dataset Availability
2015-04-02T12:00:00Z–2022-08-02T12:00:00
Dataset Provider
Earth Engine Snippet
ee.ImageCollection("NASA_USDA/HSL/SMAP10KM_soil_moisture")
Tags
geophysical hsl moisture nasa smap soil usda

Description

The NASA-USDA Enhanced SMAP Global soil moisture data provides soil moisture information across the globe at 10-km spatial resolution. This dataset includes: surface and subsurface soil moisture (mm), soil moisture profile (%), surface and subsurface soil moisture anomalies (-).

The 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. Soil moisture anomalies were computed from the climatology of the day of interest. The climatology was estimated based on the full data record of the SMAP satellite observation and the 31-day-centered moving-window approach. The assimilation of the SMAP soil moisture observations help 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 at NASA's Goddard Space Flight Center in cooperation with USDA Foreign Agricultural Services and USDA Hydrology and Remote Sensing Lab.

Bands

Resolution
10000 meters

Bands

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.

Citations

Citations:
  • Sazib, N., J. D. Bolten, and I. E. Mladenova. 2021. Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts Over Australia and California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 779-787. doi:10.1109/jstars.2021.3136756

    Mladenova, I.E., Bolten, J.D., Crow, W., Sazib, N. and Reynolds, C., 2020. Agricultural drought monitoring via the assimilation of SMAP soil moisture retrievals into a global soil water balance model. Front. Big Data, 3(10). doi:10.3389/fdata.2020.00010

  • Sazib, N., J. D. Bolten, and I. E. Mladenova. 2021. Leveraging NASA Soil Moisture Active Passive for Assessing Fire Susceptibility and Potential Impacts Over Australia and California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 779-787. doi:10.1109/jstars.2021.3136756

  • Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J. and Reynolds, C., 2019. Evaluating the operational application of SMAP for global agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3387-3397. doi:10.1109/JSTARS.2019.2923555

  • Sazib, N., Mladenova, I., & Bolten, J. (2020). Assessing the Impact of ENSO on Agriculture over Africa using Earth Observation Data. Frontiers in Sustainable Food Systems, 4, 188. doi:10.3389/fsufs.2020.509914 Google Scholar

  • Sazib, N., Mladenova, I. and Bolten, J., 2018. Leveraging the google earth engine for drought assessment using global soil moisture data. Remote sensing, 10(8): 1265. doi:10.3390/rs10081265

  • 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][https://doi.org/10.1029/2012GL053470] 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

  • 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

  • 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

Explore in Earth Engine

var dataset = ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_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');