CROP MODELS ARE VITAL TOOLS TO DETERMINE AND PREDICT BOTH CROP PRODUCTION AND ITS IMPACTS ON THE ENVIRONMENT. HOWEVER, AS BOTH CROP HYBRID DEVELOPMENT AND MANAGEMENT BECOME MORE FOCUSED AND SPECIFIC, CROP MODELS NEED INCREASINGLY DETAILED INFORMATION TO ACCURATELY PREDICT CROP PRODUCTION AND ENVIRONMENTAL IMPACTS. PEOPLE HAVE USED IMAGERY (REMOTE SENSING) TO ASSESS PLANT HEALTH, BUT THESE MEASUREMENT TYPICALLY OCCUR ONLY ONCE OR TWICE DURING A GROWING SEASON. ONE OF THE QUESTIONS WE HAVE IS: HOW DO THESE CROP IMAGING INDICIES CHANGE THROUGHOUT THE DAY, CROP GROWTH STAGES, OR EVEN THE ENTIRE GROWING SEASON? OUR STUDY PROPOSES A NOVEL USE OF REMOTE SENSING IMAGERY TO COLLECT DETAILED MEASUREMENTS OF A MAIZE CANOPY FOR USE AS INPUTS INTO A CROP MODEL AS A METHOD OF IMPROVING CROP MODEL PREDICTIONS.TO ACHIEVE THIS GOAL, WE MAKE USE OF AN IMAGERY DATASET OF A MAIZE CANOPY WITH IMAGES TAKEN EVERY TEN MINUTES DURING THE GROWING SEASON AND A DETAILED CROP MEASUREMENT DATASET WITH PHYSICAL SAMPLES TAKEN AT FIVE TIME POINTS DURING THE GROWING SEASON. USING STATISTICAL TECHNIQUES THAT DEVELOP RELATIONSHIPS BETWEEN THE REMOTELY SENSED IMAGERY AND MEASURED PLANT CHARACTERISTICS, WE CAN CREATE DETAILED, GROWING-SEASON-LONG CROP MODEL PARAMETER INPUTS. THESE INPUTS WILL BE IMPLEMENTED INTO A CROP MODEL AND ANALYZED TO SEE WHAT IMPROVEMENTS THESE DETAILED INPUTS HAVE ON CROP MODEL PREDICTIONS OF CROP PRODUCTION AT THE EXPERIMENTAL SITE DURING THE GROWING SEASON OF 2020. THESE NOVEL METHODS CAN PROVE USEFUL FOR AGRICULTURE RESEARCHERS, CROP BREEDERS, AGRONOMISTS, AND FARMERS TO HAVE MORE SPECIFIC AND ACCURATE PREDICTIONS OF CROPS IN CROP MODELS. AS MORE RESEARCHERS AND AGRONOMISTS USE REMOTE SENSING IMAGERY TO MONITOR THEIR CROP CANOPIES, THIS STUDY DEVELOPS THE METHODS TO BUILD A MODELING PLATFORM THAT AGRONOMISTS AND FARMERS CAN UTILIZE TO MAKE INFORMED MANAGEMENT DECISIONS THAT ARE SPECIFIC TO THEIR FIELDS AND CROPS AND ARE THE MOST ECONOMICALLY AND ENVIRONMENTALLY BENEFICIAL.