Remote-Sensing Soil Moisture Using Four-Dimensional Data Assimilation.

Abstract

The feasibility of synthesizing distributed fields of remotely-sensed soil moisture by the novel application of four-dimensional data assimilation applied in a hydrological model was explored in this study. Six Push Broom Microwave Radiometer images gathered over Walnut Gulch, Arizona were assimilated into the TOPLATS hydrological model. Several alternative assimilation procedures were implemented, including a method that adjusted the statistics of the modeled field to match those in the remotely sensed image, and the more sophisticated, traditional methods of statistical interpolation and Newtonian nudging. The high observation density characteristic of remotely-sensed imagery poses a massive computational burden when used with statistical interpolation, necessitating observation reduction through subsampling or averaging. For Newtonian nudging, the high observation density compromises the conventional weighting assumptions, requiring modified weighting procedures. Remotely-sensed soil moisture images were found to contain horizontal correlations that change with time and have length scales of several tens of kilometers, presumably because they are dependent on antecedent precipitation patterns. Such correlation therefore has a horizontal length scale beyond the remotely sensed region that approaches or exceeds the catchment scale. This suggests that remotely-sensed information can be advected beyond the image area and across the whole catchment. The remotely-sensed data was available for a short period providing limited opportunity to investigate the effectiveness of surface-subsurface coupling provided by alternative assimilation procedures. Surface observations were advected into the subsurface using incomplete knowledge of the surface-subsurface correlation measured at only 2 sites. It is perceived that improved vertical correlation specification will be a need for optimal soil moisture assimilation. Based on direct measurement comparisons and the plausibility of synthetic soil moisture patterns, Newtonian nudging assimilation procedures were preferred because they preserved the observed patterns within the sampled region, while also calculating plausible patterns in unmeasured regions. Statistical interpolation reduced to the trivial limit of direct data insertion in the sampled region and gave less plausible patterns outside this region. Matching the statistics of the modeled fields to those observed provided plausible patterns, but the observed patterns within sampled area were largely lost.