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Sciences and Exploration Directorate
Operational

Soil Moisture Active-Passive Mission (SMAP)

The Soil Moisture Active Passive (SMAP) mission is an orbiting observatory that measures the amount of water in the surface soil everywhere on Earth. It was launched in January 2015 and started operation in April 2015. The SMAP radiometer has been operating flawlessly. The radar instrument, ceasing operation in early 2015 due to failure of radar power supply, collected close to 3 months of science data. The prime mission phase of three years was completed in 2018, and since then SMAP has been in extended operation phase.

Launch Date
January 31, 2015
Class
Flight Project
Websites
Key Staffs
  • Science Team Member
  • Calibration Team Member
  • Science Team Member
  • Deputy Project Scientist

The Soil Moisture Active Passive (SMAP) mission is an orbiting observatory that measures the amount of water in the surface soil everywhere on Earth. It was launched in January 2015 and started operation in April 2015. The SMAP radiometer has been operating flawlessly. The radar instrument, ceasing operation in early 2015 due to failure of radar power supply, collected close to 3 months of science data. The prime mission phase of three years was completed in 2018, and since then SMAP has been in extended operation phase.

Related Publications

2026. "Advancing Water Security and National Preparedness Through Earth Science Observations and the Earth Science Workforce.", Earth and Space Science, 13 (4): e2025EA004804 [10.1029/2025ea004804] [Journal Article/Letter]

2026. "Statistical and machine learning for improving the utility of soil moisture networks for satellite‐based soil moisture product assessment in the Upper Missouri River Basin.", Vadose Zone Journal, 25 (2): e70086 [10.1002/vzj2.70086] [Journal Article/Letter]

2026. "Assessment of vegetation water dynamics by comparing microwave remote sensing signals from satellites and field-based GNSS reflectometry.", EGUsphere preprint, [10.5194/egusphere-2026-1759] [Journal Article/Letter]

2026. "Harmonizing Terrestrial Carbon Cycle Observations Over CONUS NEON Sites: Assessing the Information Contributions of Multiple Data Constraints.", Global Change Biology, 32 (2): e70761 [10.1111/gcb.70761] [Journal Article/Letter]

2025. "A Machine Learning Approach for Improving the Accuracy of Gridded Precipitation With Uncertainty Quantification.", International Journal of Climatology, 46 (1): e70161 [10.1002/joc.70161] [Journal Article/Letter]