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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
December 2015
Class
--
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

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2025. "Investigating the Accuracy of Multiple Gridded Precipitation Products and Their Impact on Hydrological Model Behaviors in Indian River Basins.", Journal of Hydrometeorology, [10.1175/jhm-d-25-0009.1] [Journal Article/Letter]

2026. "Divergent Responses of Multi-frequency Vegetation Optical Depth Products to Climate Variations in China.", Journal of Remote Sensing, 6 1028 [10.34133/remotesensing.1028] [Journal Article/Letter]

2026. "Automatic calibration of DSSAT and APEX models for maize yield simulation and economic optimum nitrogen rate determination.", European Journal of Agronomy, 172 127870 [10.1016/j.eja.2025.127870] [Journal Article/Letter]

2025. "Optimizing on-farm corn yield prediction by a multi-source data fusion approach using remote sensing and machine learning.", Smart Agricultural Technology, 12 101630 [10.1016/j.atech.2025.101630] [Journal Article/Letter]