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Title: The soil moisture active passive validation experiment 2012 (SMAPVEX12): pre-launch calibration and validation of the SMAP satellite

Author
item MCNAIRN, H. - Agriculture And Agri-Food Canada
item Jackson, Thomas
item WISEMAN, GRANT - Agriculture And Agri-Food Canada
item BELAIR, S. - Environment Canada
item BULLOCK, PAUL - University Of Manitoba
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item Cosh, Michael
item MAGAGI, R. - Universite De Sherbrooke
item MOGHADDAM, M. - University Of Michigan
item ADAMS, J. - University Of Guelph
item BERG, A. - University Of Guelph
item HOMAYOUNI, S. - Agriculture And Agri-Food Canada
item OJO, E. ROTIMI - University Of Manitoba
item ROWLANDSON, TRACY - University Of Guelph
item SHANG, J. - Agriculture And Agri-Food Canada
item GOITA - Universite De Sherbrooke
item HOSSEINI, M. - Universite De Sherbrooke

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2014
Publication Date: 5/1/2015
Publication URL: http://handle.nal.usda.gov/10113/59873
Citation: Mcnairn, H., Jackson, T.J., Wiseman, G., Belair, S., Bullock, P., Colliander, A., Cosh, M.H., Magagi, R., Moghaddam, M., Adams, J., Berg, A., Homayouni, S., Ojo, E., Rowlandson, T., Shang, J., Goita, Hosseini, M. 2015. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Pre-launch calibration and validation of the SMAP satellite. IEEE Transactions on Geoscience and Remote Sensing. 53:2784-2801.

Interpretive Summary: An aircraft-based field experiment was successfully conducted to provide an extensive data set with a wide range of moisture and vegetation conditions for the development and validation of soil moisture products that will be provided by the Soil Moisture Active Passive (SMAP) satellite. SMAP, scheduled for launch in November 2014, will integrate an L-band radiometer and radar on a single platform with the goal of providing high resolution global soil moisture. To prepare for SMAP and support the needs identified for further algorithm development, a field experiment was conducted using aircraft simulators of the satellite, SMAP Validation Experiment 2012 (SMAPVEX12). The campaign took place over agricultural fields in southern Manitoba (Canada). Multiple algorithm options were evaluated, which led to selection of the baseline algorithm. The SMAPVEX12 experiment provided high quality soil moisture and vegetation data, captured over a wide range of conditions coincident with airborne active and passive microwave acquisitions. The soil moisture retrieval results are encouraging and indicate SMAP will be a key source of data to service the needs of the agriculture and forest communities.

Technical Abstract: The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in November 2014. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a 6-week period in 2012. Several times per week NASA flew aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements and biomass samples in support of this campaign. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. The aircraft-based Passive Active L-band Sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.