Near-surface remote sensing has been used to document seasonal growth patterns (i.e. phenology) for plant communities in diverse habitats. Phenology from this source may only apply to the area within the images. Meanwhile ecosystem models can accommodate variable weather and landscape differences to plant growth, but accuracy is improved by adding ground-truthed inputs. The objective of this study was to use PhenoCam data, image analysis, and Beer’s law with established extinction coefficients to compare leaf area index (LAI) development in the ALMANAC model for diverse plant types and environments. Results indicate that PhenoCam time series imagery can be used to improve leaf area development in ALMANAC by adjusting parameter values to better match LAI derived values in new diverse environments. Soybeans, mesquite, and maize produced the most successful match between the model simulations and PhenoCam data out of the eight species simulated. This study represents, to our knowledge, the first independent evaluation of the ALMANAC process-based plant growth model with imagery in agroecosystems available from the PhenoCam network. The results show how PhenoCam data can make a valuable contribution to validate process-based models, making these models much more realistic and allows for expansion of PhenoCam influence.