Summary:

Satellite systems such as the NASA’s Soil Moisture Active Passive (SMAP) mission allow us to detect how much water there is in the surface layer of the soil. When we integrate this information into a hydrologic model we can estimate the amount of water present in the surface as well as in the root zone of the soil profile, referred to as root-zone soil moisture (RZSM).

The NASA – USDA-FAS soil moisture products are produced by assimilating Level 3 SMAP-based soil moisture retrievals into the two-layer Palmer hydrologic model using an Ensemble Kalman Filter (EnKF) technique. The composite products are produced from 3 consecutive days of quarter degree EnKF results.

Suggested Use:

The deviations of the current soil moisture values relative to some long-term climatological average called anomalies tell us whether there is sufficient water supply for plants to function properly and achieve optimal yield formation. Negative soil moisture anomaly values as the dark red colored areas show shortage of water, which is indicative of agricultural drought. Positive values (green end of the color bar) indicate surplus of water.

Satellite/Sensor:

• Satellite data: Active Passive Soil Moisture (SMAP)

• Model data: 2-layer modified Palmer Hydrologic Model

• Spatial coverage: Global; Grid spacing: 0.25°

• Temporal extent: April 2015-present

Credits:

• References:

  • Bolten, J. D., W. T. Crow, X. Zhan, C. Reynolds, and T. J. Jackson (2009), Assimilation of a satellite-based soil moisture product in a two-layer water balance model for a global crop production decision support system, in Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, pp. 449–463, Springer, Berlin.

  • Bolten, J. D., W. T. Crow, T. J. Jackson, X. Zhan, and C. A. Reynolds (2010), Evaluating the utility of remotely-sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE J. Sel. Topics Appl. Earth Obs., 3(1), 57–66.

  • Bolten, J. D. and W. T. Crow (2012), Improved prediction of quasi-global vegetation using remotely-sensed surface soil moisture, Geophysical Research Letters, 39(19).

  • Sazib, N., Mladenova, I. and Bolten, J., “Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data,” Remote Sensing, 10(8), p.1265, 2018.

• Points of Contact

  • John D. Bolten: john.bolten@nasa.gov

  • Iliana E. Mladenova: iliana.e.mladenova@nasa.gov

  • Nazmus Sazib: nazmus.s.sazib@nasa.gov

• Links to the

  • Crop Explorer: https://ipad.fas.usda.gov/cropexplorer/

  • GEE: https://explorer.earthengine.google.com/#detail/NASA_USDA%2FHSL%2FSMAP_soil_moisture

Summary:

Satellite systems such as the NASA’s Soil Moisture Active Passive (SMAP) mission allow us to detect how much water there is in the surface layer of the soil. When we integrate this information into a hydrologic model we can estimate the amount of water present in the surface as well as in the root zone of the soil profile, referred to as root-zone soil moisture (RZSM).

The NASA – USDA-FAS soil moisture products are produced by assimilating Level 3 SMAP-based soil moisture retrievals into the two-layer Palmer hydrologic model using an Ensemble Kalman Filter (EnKF) technique. The composite products are produced from 3 consecutive days of quarter degree EnKF results.

Suggested Use:

The deviations of the current soil moisture values relative to some long-term climatological average called anomalies tell us whether there is sufficient water supply for plants to function properly and achieve optimal yield formation. Negative soil moisture anomaly values as the dark red colored areas show shortage of water, which is indicative of agricultural drought. Positive values (green end of the color bar) indicate surplus of water.

Satellite/Sensor:

• Satellite data: Active Passive Soil Moisture (SMAP)

• Model data: 2-layer modified Palmer Hydrologic Model

• Spatial coverage: Global; Grid spacing: 0.25°

• Temporal extent: April 2015-present

Credits:

• References:

  • Bolten, J. D., W. T. Crow, X. Zhan, C. Reynolds, and T. J. Jackson (2009), Assimilation of a satellite-based soil moisture product in a two-layer water balance model for a global crop production decision support system, in Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, pp. 449–463, Springer, Berlin.

  • Bolten, J. D., W. T. Crow, T. J. Jackson, X. Zhan, and C. A. Reynolds (2010), Evaluating the utility of remotely-sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE J. Sel. Topics Appl. Earth Obs., 3(1), 57–66.

  • Bolten, J. D. and W. T. Crow (2012), Improved prediction of quasi-global vegetation using remotely-sensed surface soil moisture, Geophysical Research Letters, 39(19).

  • Sazib, N., Mladenova, I. and Bolten, J., “Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data,” Remote Sensing, 10(8), p.1265, 2018.

• Points of Contact

  • John D. Bolten: john.bolten@nasa.gov

  • Iliana E. Mladenova: iliana.e.mladenova@nasa.gov

  • Nazmus Sazib: nazmus.s.sazib@nasa.gov

• Links to the

  • Crop Explorer: https://ipad.fas.usda.gov/cropexplorer/

  • GEE: https://explorer.earthengine.google.com/#detail/NASA_USDA%2FHSL%2FSMAP_soil_moisture