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. Recent advances in radar remote sensing popularized the mapping of surfacesoil moisture at different spatial scales. Surface soil moisture measurementsare used in combination with hydrological models to determine subsurface soilmoisture values. However, variability of soil moisture across the soil columnis important for estimating depth-integrated values, as decoupling betweensurface and subsurface can occur. In this study, we employ new methods toinvestigate the occurrence of (de)coupling between surface and subsurfacesoil moisture. Using time series datasets, lagged dependence was incorporatedin assessing (de)coupling with the idea that surface soil moisture conditionswill be reflected at the subsurface after a certain delay. The main approachinvolves the application of a distributed-lag nonlinear model (DLNM) tosimultaneously represent both the functional relation and the lag structurein the time series. The results of an exploratory analysis using residualsfrom a fitted loess function serve as a posteriori information to determine(de)coupled values. Both methods allow for a range of (de)coupled soilmoisture values to be quantified. Results provide new insights into thedecoupled range as its occurrence among the sites investigated is not limitedto dry conditions.

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values