Spatial horizontal correlation characteristics in the land data assimilation of soil moisture
Abstract. Remote sensing images deliver important information about soil moisture, but often cover only part of an area, for example due to the presence of clouds or vegetation. This paper examines the potential of incorporating the spatial horizontal correlation characteristics of surface soil moisture observations in land data assimilation in order to obtain improved estimates of soil moisture at uncovered grid cells (i.e. grid cells without observations). Observing system simulation experiments were carried out to assimilate the synthetic surface soil moisture observations into the Community Land Model for the Babaohe River Basin in northwestern China. The estimation of soil moisture at the uncovered grid cells was improved when information about surrounding observations and their spatial correlation structure was included. Including an increasing number of observations for covered and uncovered grid cells in the assimilation procedure led to a better prediction of soil moisture with an upper limit of five observations. A further increase of the number of observations did not further improve the results for this specific case. High observational coverage resulted in a better assimilation performance, depending also on the spatial distribution of observation data. In summary, the spatial horizontal correlation structure of soil moisture was found to be helpful for improving the surface soil moisture data characterization, especially for uncovered grid cells.