Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests

Whitcomb, J, Clewley, D, Colliander, A, Cosh, MH, Powers, J, Friesen, M, McNairn, H, Berg, AA, Bosch, DD, Coffin, A, Collins, CH, Prueger, JH, Entekhabi, D and Moghaddam, M 2020 Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13. 6457-6472. https://doi.org/10.1109/JSTARS.2020.3033591

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Official URL: http://dx.doi.org/10.1109/JSTARS.2020.3033591


In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilises sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP’s radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed.Here,we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a random forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics.This method,which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP’s entire allowable unbiased root-mean-square error(ubRMSE).Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

Item Type: Publication - Article
Additional Information. Not used in RCUK Gateway to Research.: Jane Whitcomb and Mahta Moghaddam are with the University of Southern California, Los Angeles, CA 90007 USA (e-mail: jbwhitco@usc.edu; mahta@usc.edu). Daniel Clewley is with Plymouth Marine Laboratory, Plymouth PL1 3DH, U.K. (e-mail: dac@pml.ac.uk). Andreas Colliander is with Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125 USA (e-mail: andreas.colliander@jpl.nasa.gov). Michael H. Cosh is with USDA-ARS Hydrology and Remote Sensing Laboratory,Beltsville,MD20705-2350USA(e-mail:Michael.Cosh@ars.usda.gov). Jarrett Powers and Matthew Friesen are with Agriculture and Agri-Food Canada, Science and Technology Branch, Winnipeg, MB R3C 1B2, Canada (e-mail: jarrett.powers@agr.gc.ca; matthew.friesen@agr.gc.ca). Heather McNairn is with Agriculture and Agri-Food Canada, Science and Technology Branch, Ottawa, ON K1A 0C5, Canada (e-mail: heather.mcnairn@agr.ac.ca). Aaron A. Berg is with the Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada (e-mail: aberg@uoguelph.ca). David D. Bosch and Alisa Coffin are with USDA-ARS, Southeast Watershed Research Laboratory, Tifton, GA 31794 USA (e-mail: david.bosch@ars.usda.gov; alisa.coffin@usda.gov). ChandraHolifieldCollinsiswithUSDA-ARSSouthwestWatershedResearch Laboratory, Tucson, AZ 85719 USA (e-mail: chandra.holifield@ars.usda.gov). John H. Prueger is with the USDA-ARS National Laboratory for Agriculture and Environment, Ames, IA 50011 USA (e-mail: john.prueger@ars.usda.gov). DaraEntekhabiiswiththeMassachusettsInstituteofTechnology,Cambridge, MA 02139 USA (e-mail: darae@mit.edu).
Additional Keywords: Index Terms—Random forests, soil moisture, Soil Moisture Active Passive (SMAP), upscaling
Divisions: Plymouth Marine Laboratory > National Capability categories > NERC Earth Observation Data Acquisition & Analysis Service (NEODAAS)
Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User: S Hawkins
Date made live: 09 Dec 2020 15:19
Last Modified: 09 Dec 2020 15:19
URI: https://plymsea.ac.uk/id/eprint/9087

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