Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #320138

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Early results of the Soil Moisture Active Passive Validation Experiment (SMAPVEX15)

Author
item Cosh, Michael
item Jackson, Thomas
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item Goodrich, David - Dave
item Holifield Collins, Chandra
item McKee, Lynn
item KIM, S. - Jet Propulsion Laboratory
item YUEH, S. - Jet Propulsion Laboratory

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/1/2015
Publication Date: 12/15/2015
Citation: Cosh, M.H., Jackson, T.J., Colliander, Andreas, Goodrich, D.C., Holifield Collins, C., McKee, L.G., Kim, S., Yueh, S. 2016. Early results of the Soil Moisture Active Passive Validation Experiment (SMAPVEX15). American Geophysical Union Fall Meeting. December 14-18, 2015, San Francisco, CA. Abstract # H54B-07.

Interpretive Summary:

Technical Abstract: In August of 2015, the Soil Moisture Active Passive Validation Experiment (SMAPVEX15) was conducted to provide a high resolution soil moisture dataset for the calibration/validation of the Soil Moisture Active Passive Mission (SMAP). The Upper San Pedro River Basin and the USDA-ARS Walnut Gulch LTAR Watershed provides the infrastructure for the experiment with its extensive soil moisture and soil temperature network. A total of seven aircraft flights are planned for the Passive Active L-Band Scanning instrument (PALS) to provide a high resolution soil moisture map for a variety of soil moisture conditions across the domain. Extensive surface roughness, vegetation and soil rock fraction mapping was conducted to provide a ground truth estimate of the many ancillary datasets used in the SMAP soil moisture algorithms. A review of the methodologies employed in the experiment, as well as initial findings will be discussed.