Yield and Quality Prediction Using Satellite Passive Imagery and Ground-Based Active Optical Sensors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Abstract

Remote sensing is one possible approach for improving crop nitrogen use efficiency to save fertilizer costs, reduce environmental pollution, and improve crop yield and quality. Feasibility and potential of using remote sensing tools to predict crop yields and quality as well as to detect nitrogen requirements, application timing, rate, and places in season were investigated based on a two-year (2012-2013) and four-crop (corn, spring wheat, sugar beet, and sunflower) study. Two ground-based active optical sensors, GreenSeekerTM and Holland Scientific Crop CircleTM, and the RapidEyeTM satellite imagery were used to collect sensing data. Highly significant statistical relationships between INSEY (NDVI normalized by growing degree days) and crop yield and quality indices were found for all crops, indicating that remote sensing tools may be useful for managing in-season crop yield and quality prediction.