Yield and quality prediction using satellite passive imagery and ground-based active optical sensors in sugar beet, spring wheat, corn, and sunflower

Remote sensing is one possible approach for improving crop nitrogen use efficiency to save fertilizer cost, reduce environmental pollution, and improve crop yield and quality. Feasibility and potential of using remote sensing tools to predict crops yield and quality as well as detect nitrogen requirements, application timing, rate, and places in season were investigated based on 2012-2013 two-year and four-crop (corn, spring wheat, sugar beet, and sunflower) study. Two ground-based active optical sensors, GreenSeeker and Holland Scientific Crop Circle, and the RapidEye 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.