Issues of scale are central to the development of a validation research framework. Here one of the key research questions is:
``How do we convert data that represents one particular time or space scale to another scale for comparison with other information?''
We hypothesize that: remotely--sensed observations can best be validated through the use of a rigorous statistical methodology that accounts for variability in the data (including ancillary data) at a variety of space and time scales; and integrating other environmental variables together to better constrain the specific variable of interest is a powerful validation method that has yet to be fully exploited because of the lack of complete, high-quality, and long-term data.
In this presentation we will describe a small (1 km^2) prototype experimental validation site at which we are testing our hypotheses. The site is extensively instrumented with both in-situ and remote sensors so important ancillary data can be carefully characterized at several spatial scales for long periods of time. Initially we are focusing on the validation of remotely-sensed observations of soil moisture. Data generated at the site are instantly available to other researchers through the use of wireless technologies and the world wide web. The site is located near the campus of Iowa State University.