High Resolution Soil Moisture Observations from the SMAP Mission.

NASA’s Soil Moisture Active Passive (SMAP) Mission was launch January 2015. The objective of the mission is global mapping of surface soil moisture and landscape freeze/thaw state. SMAP utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate onboard the SMAP spacecraft in a 685-km Sun-synchronous near-polar orbit, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width. Merging of the high-resolution active (radar) and coarse-resolution but high-sensitivity passive (radiometer) L-band observations will enable an unprecedented combination of accuracy, resolution, coverage and revisit-time for soil moisture and freeze/thaw state retrievals SMAP active-passive soil moisture retrieval product prior to beta release scheduled for October 2015.

The Level-2 active-passive soil moisture (L2_SM_AP) product at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging the active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (~36 km) L-band radiometer brightness temperature data using the high-resolution (~3 km) L-band radar backscatter data. The L2_SM_AP product will include important and relevant data fields derived from the L2_SM_P (SMAP passive only product) and L2_SM_A (SMAP active only product). Also included are ancillary information used to retrieve the soil moisture, quality flags for the disaggregated brightness temperature and retrieved soil moisture, and surface flags and georeferencing information at the 9-km EASE2 grid resolution.

This presentation will discuss early results of the active-passive product obtained from the first 6 months of SMAP observations. In addition to the primary data fields, quality flags and surface flags that are essential for assessing the quality of the L2_SM_AP disaggregated brightness temperature and surface soil moisture product will be illustrated. The presentation will also discuss the errors associated with the L2_SM_AP disaggregated brightness temperature and surface soil moisture retrievals. The errors in disaggregated brightness temperatures are due to inherent errors/noise in the inputs (brightness temperature and radar backscatter) to the L2_SM_AP algorithm, assumptions in the algorithm, and errors in the algorithm parameters. The soil moisture estimates are retrieved from the disaggregated brightness temperatures using the “tau-omega” model, and suffer from having uncertainties due to errors in ancillary data, noisy parameters, and errors in the disaggregated brightness temperatures themselves. Finally, in this presentation the accuracies of the SMAP L2-SM_AP soil moisture retrievals will be shown using comparisons with in situ data over the core validation sites. Root mean square difference (RMSD), unbiased RMSD (ubRMSD), bias and correlation will be computed to track the performance of the SMAP L2_SM_AP algorithm and products.