Comparison of Satellite Imagery and Ground‐Based Active Optical Sensors as Yield Predictors in Sugar Beet, Spring Wheat, Corn, and Sunflower

Core Ideas Satellite imagery could be used to predict yield the study crops. Satellite imagery could be used to screen fields for in‐season N application. Obtaining satellite imagery early enough in the season to screen fields for in‐season N is a problem. Algorithms using active‐optical (AO) sensors have been developed to direct in‐season N application to crops. Many farmers in the United States have a large number of farm fields to manage. Farmers using AO technology must visit each field and operate the sensor across the entire field in order to conduct in‐season N application. A field might be driven over with an on‐the‐go N fertilizer applicator, but the application might not be required. The objective of this study was to determine whether satellite imagery might be used to predict yield in sugar beet, spring wheat, corn and sunflower similar to the yield prediction possible using AO sensors. If so, the algorithms produced could be used to select fields that would benefit from in‐season N application. Two N‐rate studies in sugar beet, spring wheat, corn and sunflower, were conducted with experimental unit size of 9 by 9 m large enough to fit a satellite pixel of 5 by 5 m size within each unit. The AO sensor and satellite imagery data were related to yield of sugar beet, spring wheat, corn and sunflower in some site‐years. The problem is the ability to acquire the satellite imagery early enough in the season to be useful as a screening tool. These results indicate that even though satellite imagery could be used as a field screening tool, a better option may be to mount an AO sensor on a farm implement for an early season activity, or to explore the use of unmanned aerial vehicles (UAVs).