Hyperspectral Imagery for Mapping Crop Yield for Precision Agriculture

Abstract

Crop yield is one of the most important pieces of information for crop management in precision agriculture. It integrates the effects of various spatial variables such as soil properties, topographic attributes, tillage, plant population, fertilization, irrigation, and pest infestations. A yield map can therefore be an indispensable input for variable rate application either by itself or in combination with other spatial information. Imagery from traditional satellite systems, such as the U.S. Landsat satellites and the French SPOT satellites, has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. However, this type of imagery has limited use for assessing within-field yield variability because of its coarse spatial resolution, long repeat cycles, and slow data delivery. Therefore, airborne multispectral and hyperspectral imaging systems have been more widely used for assessing within-field crop growth and yield variation. Remote sensing imagery obtained during the growing season has potential not only for after-season management, but also for within-season management. This chapter presents a brief overview of high resolution remote sensing imagery for mapping crop yield variability and illustrates how airborne hyperspectral imagery can be used for crop yield estimation based on different methods. Research results have demonstrated that hyperspectral imagery can be useful for estimating and mapping within-field crop yield variability for precision agriculture.