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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #363403
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: A method for assessing SMAP core validation site scaling bias using enhanced sampling and random forests

Author
item WHITCOMB, J. - University Of Southern California
item MOGHADDAM, M. - University Of Southern California
item CLEWLEY, D. - University Of Plymouth
item COLLIANDER - Jet Propulsion Laboratory
item Cosh, Michael
item POWERS, J. - Agri-Food And Biosciences Institute
item FRIESEN, M. - Agriculture And Agri-Food Canada
item MCNAIM, H. - Agriculture And Agri-Food Canada
item BERG, A. - University Of Guelph
item Bosch, David
item Holifield Collins, Chandra
item Prueger, John
item ENTEKHABI, D. - Center For Integration Of Medicine And Innovative Technology (CIMIT)

Submitted to: IEEE IGARSS Annual Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/1/2019
Publication Date: 7/28/2019
Citation: Witcomb, J., Moghaddam, M., Clewley, D., Colliander, Cosh, M.H., Powers, J., Friesen, M., Mcnaim, H., Berg, A., Bosch, D.D., Holifield Collins, C.D., Prueger, J.H., Entekhabi, D. 2019. A method for assessing SMAP core validation site scaling bias using enhanced sampling and random forests.Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Paper No. 4461.

Interpretive Summary:

Technical Abstract: In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.