Robust estimation of average soil water content with spatial resolution of a few tens to a few hundreds of meters is essential for evaluating models or data assimilation products. Due to the high spatial variability of soil moisture at the point scale, sufficient coverage of spatial observations is required to estimate a robust field average. If sensors fail over time, averaging the remaining measurements risks the introduction of artificial shifts in the resulting time series. Here, we explore the problem of using incomplete soil moisture observations to estimate spatial averages and propose a correction accounting for temporal persistence of spatial patterns. By transforming, i.e. upscaling, each sensor measurement to the field scale using information from time periods with sufficient coverage, the dependence on full spatial coverage can be decreased. The transformed values allow to build a more robust approximation to the spatial mean, even when spatial coverage becomes sparse. We found that high temporal stability of the sensors does not necessarily guarantee that the transformed time series will provide a good estimate of the mean and therefore recommend the use of robust statistics to derive the field mean, which requires at least three estimates per observation time. The proposed protocol is applicable for observational time series with varying sample size across a given spatial extent, and it can be adopted for other variables exhibiting a temporally stable bias between the individual point observations and field scale average.

Canopies shape net precipitation patterns, which are spatially heterogeneous and control soil moisture response to rainfall. The vast majority of studies on canopy water fluxes were conducted in forests, whereas grassland canopies are often assumed to be spatially homogeneous, therefore likely not inducing patches of heterogeneity at and below the soil surface. Yet, some studies on short-structured vegetation, such as grasslands, proposed the importance of canopy-induced heterogeneity for net precipitation, but systematic investigations on the effects on soil wetting patterns are missing. Therefore, in this study, we investigated soil moisture response to rainfall in a managed temperate grassland by exploring the individual impacts of spatially varying throughfall, vegetation height and antecedent soil moisture status on the soil wetting patterns. We applied linear mixed effects models to disentangle the role of grassland canopy versus abiotic drivers. The spatial average soil water response showed that less and less water was stored in the upper parts of the soil as the growing season progressed and the soils dried, indicating bypass flow. Spatial variation of grass height was a significant driver of soil wetting patterns along with precipitation and antecedent soil moisture status. Soil wetting was suppressed in locations with tall canopy, although surprisingly this was not directly related to throughfall patterns. Instead, our results suggest that seasonally drier conditions and grassland stemflow kick off preferential flow. Ultimately, our results confirm that spatial variation of the canopy affects soil moisture wetting patterns not only in forests and indicate a strong influence of preferential flow on soil water patterns.