Submitted to: Advances in Water Resources
Publication Type: Peer reviewed journal
Publication Acceptance Date: 10/12/2004
Publication Date: 1/1/2005
Citation: Crow, W.T., Ryu, D., Famiglietti, J.S. 2005. Upscaling of field-scale soil moisture heterogeniety using distributed land surface modeling. Advances in Water Resources. 28:1-14. Interpretive Summary: Current and next-generation satellite sensors will be capable of globally mapping surface soil moisture, but only at relatively low spatial resolutions (> 25 km). Such a low resolution posses a problem for validating these observations since independent ground-based observations of soil moisture are typically limited to sparse point-scale observations which capture very little of the highly spatially variable soil moisture field. Using data from the 2002 Soil Moisture Experiment (SMEX02) this paper describes a technique for merging local soil moisture information with high-resolution modeling of the land surface to produce accurate estimates of surface soil moisture at coarse spatial resolutions (> 25 km). Such estimates are of potentially great value for ongoing efforts to develop and optimize spaceborne systems to operationally observe soil moisture.
Technical Abstract: Accurate coarse-scale soil moisture information is required for robust validation of current- and next-generation soil moisture products derived from spaceborne radiometers. Due to large amounts of land surface and rainfall heterogeneity, such information is difficult to obtain from existing ground-based networks of soil moisture sensors. Using ground-based field data collected during the Soil Moisture Experiment in 2002 (SMEX02), the potential for using distributed modeling predictions of the land surface as an upscaling tool for field-scale soil moisture observations is examined. Results demonstrate that distributed models are capable of accurately capturing a significant level of field-scale soil moisture heterogeneity observed during SMEX02. Soil moisture upscaling strategies based on the merger of ground-based observations with modeling predictions are developed and shown to be more accurate during SMEX02 than upscaling approaches that utilize either ground-based observations or model predictions in isolation.