Dynamic global vegetation models (DGVMs) simulate ecosystem responses to multiple environmental factors. Traditionally, these models were evaluated with sparse ground measurements, but satellite observations now allow evaluation at model grid cell scales with near-global coverage. Agro-IBIS is the first DGVM to explicitly simulate the major crop types of the United States. We evaluate these simulated agroecosystems using satellite information of greenness for the first time at the regional scale. We compare Agro-IBIS-simulated leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) of crops, forests, and grasses with AVHRR (1982–2000) and MODIS (2000–2002) LAI and FPAR data sets across the central and eastern United States. Compared with MODIS data, Agro-IBIS overestimates growing season LAI over broadleaf crops and simulates reasonable but lower growing season LAI over deciduous forest, with an early onset bias in the southern region. Agro-IBIS underestimates needleleaf forest LAI and substantially overestimates the magnitude of grassland growing season LAI, with an early onset bias of one month. Similar bias patterns in FPAR occur in all biomes. While we do not trust the LAI/FPAR magnitudes from AVHRR data, we do suggest that AVHRR data may be used to evaluate the timing of onset and offset of the growing season of broadleaf crops and grasses. Evaluation of broadleaf crop peak LAI from Agro-IBIS and MODIS with ground measurements shows significant discrepancies. Continuing improvements in DGVMs (especially grass algorithms in Agro-IBIS) and validation of satellite data sets (especially over crops in MODIS) are needed to understand regional-scale terrestrial ecosystem processes.