Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data

This study investigates the spatial representativeness of the temporal dynamics of absolute soil moisture and its temporal anomalies over North America based on a range of data sets. We use three main data sources: in situ observations, the remote-sensing-based data set of the European Space Agency Climate Change Initiative on the Essential Climate Variable soil moisture (ECV-SM), and land surface model estimates from European Centre for Medium-Range Weather Forecasts's ERA-Land. The intercomparisons of the three soil moisture data sources are performed at the in situ locations as well as for the full-gridded products. The applied method allows us to quantify the spatial footprint of soil moisture. At the in situ locations it is shown that for absolute soil moisture the ECV-SM and ERA-Land products perform similarly, while for the temporal anomalies the ECV-SM product shows more similarity in spatial representativeness with the in situ data. When taking into account all grid cells of the ECV-SM and ERA-Land products to calculate spatial representativeness, we find the largest differences in spatial representativeness for the absolute values. The differences in spatial representativeness between the single products can be related to some of their intrinsic characteristics, i.e., for ECV-SM low similarities are found in topographically complex terrain and areas with dense vegetation, while for ERA-Land the smoothed model topography and surface properties affect soil moisture and its spatial representativeness. Additionally, we show that the applied method is robust and can be used to analyze existing networks to provide insight into the locations in which higher station density would be of most benefit.