ABSTRACT
In this chapter, the author discusses results and lessons learned from research performed through seven years of experience working on FIFE, and addresses questions related to scaling effects in remotely sensed data. Results from a variety of empirical analyses that have been performed through FIFE are discussed, and results from more recent efforts using physically-based simulation models to examine these effects in a systematic fashion are presented. Consequently, inversion strategies based on these models are susceptible to the types of errors discussed in the section Scaling Biophysical Models. These studies focused on the scale-dependent variation in NDVI images using scale variance techniques, and demonstrated significant interaction between sensor resolution and the phenomenon of interest. In this chapter, a variety of issues related to scaling effects in remotely sensed data have been addressed in association with how these effects propagate through biophysical models that use remotely sensed inputs.
