|RONDINELLI, W.J. - Iowa State University|
|HORNBUCKLE, B. - Iowa State University|
|PATTON, J.C. - Iowa State University|
|WALKER, V.A. - Iowa State University|
|CARR, B.D. - Iowa State University|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/9/2015
Publication Date: 4/1/2015
Citation: Rondinelli, W., Hornbuckle, B., Patton, J., Cosh, M.H., Walker, V., Carr, B., Logsdon, S.D. 2015. Different rates of soil dyring after rainfall are observed by the SMOS satellite and the South Fork In Situ Soil Moisture Network. Journal of Hydrometeorology. 16(2):889-903. doi:10.1175/JHM-D-14-0137.1.
Interpretive Summary: Soil moisture monitoring from space is a useful tool for agricultural and hydrologic applications, provided the estimates are accurate. It has been observed that soil moisture estimates from the Soil Moisture Ocean Salinity (SMOS) mission have discrepancies when compare to in situ network data, immediately following precipitation events. This may be a result of different measurement depths of the satellite and the in situ network. Though these measurements are correlated, they introduce errors in the data record. Future network installations can be altered to address this error source, or modeling systems can be implemented to correct the bias of the instrumentation following precipitation events. This information is useful to in situ network managers, and remote sensing scientists attempting to conduct validation of their satellite instruments.
Technical Abstract: Soil moisture affects the spatial variation of land–atmosphere interactions through its in'uence on the balance of latent and sensible heat 'ux. Wetter soils are more prone to 'ooding because a smaller fraction of rainfall can in'ltrate into the soil. The Soil Moisture and Oceanic Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near–surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. We found that the rate of soil drying after signi'cant rainfall observed b y SMOS is higher than the rate observed by a United States Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork of the Iowa River. We conclude that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few cm thick, while the network observes a deeper soil layer centered at the depth at which its’ in situ soil moisture sensors are buried. We also found that SMOS near surface soil moisture is drier than the South Fork network soil moisture, on average. Our conclusion that SMOS and the network observe different layers of the soil cannot explain the dry bias, but it may explain some of the root–mean–square–error (RMSE) in the relationship. SMOS observations are also noisier than the network observations.