Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

摘要

With increased use of precision agriculture techniques, information concerning within-field crop yield variability is becoming increasingly important for effective crop management. Despite the commercial availability of yield monitors, many crop harvesters are not equipped with them. Moreover, yield monitor data can only be collected at harvest and used for after-season management. On the other hand, remote sensing imagery obtained during the growing season can be used to generate yield maps for both within-season and after-season management. This paper gives an overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability. The methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed. Five application examples are provided to illustrate how airborne multispectral and hyperspectral imagery and high-resolution satellite imagery have been used for mapping crop yield variability. Image processing techniques including vegetation indices, unsupervised classification, correlation and regression analysis, principal component analysis, and supervised and unsupervised linear spectral unmixing are used in these examples. Some of the advantages and limitations on the use of different types of remote sensing imagery and analysis techniques for yield mapping are also discussed.