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dc.contributor.authorFranz, Trenton E.
dc.contributor.authorLoecke, Terrance D.
dc.contributor.authorBurgin, Amy J.
dc.contributor.authorZhou, Yuzhen
dc.contributor.authorLe, Tri
dc.contributor.authorMoscicki, David
dc.date.accessioned2018-11-09T18:11:48Z
dc.date.available2018-11-09T18:11:48Z
dc.date.issued2017-06-19
dc.identifier.citationFranz, T. E., T. D. Loecke, A. J. Burgin,Y. Zhou, T. Le, and D. Moscicki (2017), Spatiotemporal predictions of soil properties and states in variably saturated landscapes, J. Geophys. Res. Biogeosci., 122, 1576–1596, doi:10.1002/2017JG003837.en_US
dc.identifier.urihttp://hdl.handle.net/1808/27288
dc.description.abstractUnderstanding greenhouse gas (GHG) fluxes from landscapes with variably saturated soil conditions is challenging given the highly dynamic nature of GHG fluxes in both space and time, dubbed hot spots, and hot moments. On one hand, our ability to directly monitor these processes is limited by sparse in situ and surface chamber observational networks. On the other hand, remote sensing approaches provide spatial data sets but are limited by infrequent imaging over time. We use a robust statistical framework to merge sparse sensor network observations with reconnaissance style hydrogeophysical mapping at a well‐characterized site in Ohio. We find that combining time‐lapse electromagnetic induction surveys with empirical orthogonal functions provides additional environmental covariates related to soil properties and states at high spatial resolutions (~5 m). A cross‐validation experiment using eight different spatial interpolation methods versus 120 in situ soil cores indicated an ~30% reduction in root‐mean‐square error for soil properties (clay weight percent and total soil carbon weight percent) using hydrogeophysical derived environmental covariates with regression kriging. In addition, the hydrogeophysical derived environmental covariates were found to be good predictors of soil states (soil temperature, soil water content, and soil oxygen). The presented framework allows for temporal gap filling of individual sensor data sets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate that the framework, with its flexible temporal and spatial monitoring options, will be useful in designing future monitoring networks as well as support the next generation of hyper‐resolution hydrologic and biogeochemical models.en_US
dc.publisherAmerican Geophysical Unionen_US
dc.rights©2017. American Geophysical Union. All Rights Reserved.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleSpatiotemporal predictions of soil properties and states in variably saturated landscapesen_US
dc.typeArticleen_US
kusw.kuauthorLoecke, Terrance
kusw.kudepartmentEnvironmental Studies Programen_US
dc.identifier.doi10.1002/2017JG003837en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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©2017. American Geophysical Union. All Rights Reserved.
Except where otherwise noted, this item's license is described as: ©2017. American Geophysical Union. All Rights Reserved.