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.