|Santanello, jr., J.|
|Moran, Mary - Susan|
Submitted to: Remote Sensing of Environment
Publication Type: Peer reviewed journal
Publication Acceptance Date: 2/3/2007
Publication Date: 7/23/2007
Citation: Santanello, Jr., J.A., Peters-Lidard, C.D., Garcia, M.E., Mocko, D.M., Tischler, M.A., Moran, M.S., Thoma, D.P. 2007. Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed. Remote Sensing of Environment. 110:79-97. doi:10.1016/j.rse.2007.02.007. Interpretive Summary: Soil moisture is a critical component of atmospheric, land-surface, and hydrologic models that impacts weather forecasts on daily to seasonal timescales. Because detailed maps of soil properties are difficult to obtain, approximations of general soil types and properties are routinely implemented in forecast models and lead to incorrect predictions of soil moisture andn meteorological conditions. In this study, the problem of estimating soil moisture and soil properties is approached from a unique perspective. The first testbed for this experiment is the Walnut Gulch Experimental Watershed in southeastern Arizona, where 6 daily estimates of near-surface soil moisture across the watershed were derived from passive microwave data using established techniques. Then, a land-surface model was run to determine which soil types and properties are required in the model to simulate the soil moisture conditions that match those from satellite. By adjusting the sand, clay, and silt contents (i.e. the properties that control the flow of moisture) of the soil in a physically consistent manner, errors in model simulated versus observed soil moisture were minimized. Using the resultant soil types to simulate another time period clearly demonstrates the improvement in soil moisture simulations over those using coarse or default soil property maps. Results also show that this methodology can be successful with as few as 2 satellite overpasses that capture the typical range of soil moisture variability for a given region. Overall, this study demonstrates the potential to gain physically meaningful and much-needed soils information at high-resolution using few but appropriately timed satellite retrievals of soil moisture in models. Ultimately, such information will improve weather forecasts on daily to climatic timescales.v
Technical Abstract: Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon ’90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.