Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/11/2011
Publication Date: 2/9/2012
Citation: Alfieri, J.G., Kustas, W.P., Gao, F.N., Prueger, J.H., Baker, J.M., Hatfield, J.L. 2012. The factors influencing seasonal variations in evaporative fluxes from spatially distributed Agro-Ecosystems [abstract]. Chapman Conference on Remote Sensing of the Terrestrial Water Cycle. 2012 CDROM. Interpretive Summary:
Technical Abstract: The exchange of moisture between the land surface and the atmosphere is the result of complex network of interacting processes, most of which are regulated, at least in part, by spatially variable atmospheric and surface conditions. Because these processes are often strongly nonlinear, scaling measurements collected at one scale to another remains a nontrivial task. Since field measurements are commonly used to develop, calibrate, and validate both numerical models and remotely sensed products, errors in the upscaling of point measurements can propagate into and adversely impact the accuracy and utility of these models and products. In an effort to identify the key environmental drivers controlling the latent heat flux ('E) from agro-ecosystems and their potential impacts on upscaling in-situ flux measurements, eddy covariance and micrometeorological data collected over maize and soy at three distinct sites located in Maryland, Iowa, and Minnesota, respectively were evaluated. The magnitudes of the evaporative fluxes were comparable for measurements collected during clear-sky days with similar environmental conditions; on average, the measurements of 'E agreed to within 50 W m-2, or 10%. When considered in terms of evaporative fraction (fe), however, there were marked differences among the sites. For example, while the magnitude and diurnal pattern of fe for mature maize at the Minnesota site was nearly constant (fe = 0.66) during the day, fe at both the Maryland and Iowa site increased steadily during the day from a minimum value near 0.68 at mid-morning to peak value of 0.87 in the afternoon. These differences appear to be primarily linked to differences in soil moisture and vegetation density at the various sites. The utility of remote sensing data to provide the necessary vegetation metrics to identify the underlying cause of the difference in fe will also be discussed.