ALBERT

Description: Understanding 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 datasets 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 vs. 120 in-situ soil cores indicated a ~30% reduction in Root Mean Square Error for soil properties (clay wt. percent and total soil carbon wt. 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 datasets as well as provides flexible geometric interpolation to complex areas/volumes. We anticipate 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.