Downscaling and Assimilation of Surface Soil Moisture Using Ground Truth Measurements

Methods for reconciliation of spatial and temporal scales of data have become increasingly important as remote sensing data become more readily available and as the science of hydrology moves more heavily toward distributed modeling. The purpose of this paper is to develop a method to disaggregate coarse-resolution remote sensing data to finer scale resolutions that are more appropriate for use in hydrologic studies and water management. This disaggregation is done with the help of point measurements on the ground. The downscaling of remote sensing data is achieved by three main steps: initialization, spatial pattern mimicking, and assimilation. The first two steps are part of the main algorithm, and the last step, assimilation, is included for fine-tuning and to ensure further compatibility between the coarse-scale and fine-scale images. The assimilation step also incorporates the information coming from the point measurements. The approach has been applied and validated by downscaling images for two cases. In the first case, a synthetically generated random field is reproduced at fine and coarse resolutions. The downscaled image has been shown to match the spatial properties of the true image according to the variogram test as well as the magnitude of values according to the various univariate goodness-of-fit measures R
²
= 0.91. In the second case, a soil moisture field from the Southern Great Plains (SGP 97) experiments is downscaled from a resolution of 800 m X 800 m to a resolution of 50 m X 50 m.