Substantial research seeks to improve estimates of ecosystem processes and fluxes at a range of scales, notably from the stand scale (<1 km2) using ecosystem physiology and eddy covariance techniques, to the landscape (≃102 km2) and global (108km2) scales using a variety of modeling and data acquisition approaches. One approach uses remotely sensed ecosystem properties in the scaling process: This approach combines digital maps of key ecosystem properties such as land cover type, leaf area index, and/or canopy chemistry with quantitative models of biological processes based on these ecosystem properties. Constraints on parametrizing models for global scale applications mean that relatively simple algorithms must be used which are based almost exclusively on satellite-derived inputs, for example, the planned Earth Observation System (EOS)-MODIS Land Science Team model output. Presently, there are limited ways of validating these outputs. At the landscape scale, the opportunity exists to combine remote sensing data with spatially distributed, process-based biogeochemistry models to examine variation in ecosystem processes such as NPP as a function of land cover type, canopy attributes, and/or location along environmental gradients. These process models can be validated against direct measurements made with eddy covariance flux towers and ground-based NPP sampling. Once rum and validated over local landscapes, these fine scale models may provide our best opportunity to provide meaningful evaluation (or 'validation' in some sense) of simpler, globally applied models. In this article, we 1) provide a biological framework that links ecosystem attributes and ecosystem carbon flux processes at a variety of scales, and summarizes the state of knowledge and models in these areas, 2) describe the need for developing NPP surfaces at a local landscape scale as a means of validating global models, in particular the MODIS NPP product, 3) describe the approach of the BigFoot project to performing such a validation exercise for a series of sites in North America, and 4) present an example using one such model (PnET-II) across diverse vegetation types in a heterogeneous landscape in central North America.