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.