  The evaluation of modelled or satellite-derived soil moisture (SM) estimates
is usually dependent on comparisons against in-situ SM measurements. However,
the inherent mismatch in spatial support (i.e., scale) necessitates a cautious
interpretation of point-to-pixel comparisons. The upscaling of the in-situ
measurements to a commensurate resolution to that of the modelled or retrieved
SM will lead to a fairer comparison and statistically more defensible
evaluation. In this study, we presented an upscaling approach that combines
spatiotemporal fusion with machine learning to extrapolate point-scale SM
measurements from 28 in-situ sites to a 100 m resolution for an agricultural
area of 100 km by 100 km. We conducted a four-fold cross-validation, which
consistently demonstrated comparable correlation performance across folds,
ranging from 0.6 to 0.9. The proposed approach was further validated based on a
cross-cluster strategy by using two spatial subsets within the study area,
denoted as cluster A and B, each of which equally comprised of 12 in-situ
sites. The cross-cluster validation underscored the capability of the upscaling
approach to map the spatial variability of SM within areas that were not
covered by in-situ sites, with correlation performance ranging between 0.6 and
0.8. In general, our proposed upscaling approach offers an avenue to
extrapolate point measurements of SM to a spatial scale more akin to climatic
model grids or remotely sensed observations. Future investigations should delve
into a further evaluation of the upscaling approach using independent data,
such as model simulations, satellite retrievals or field campaign data.
