Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery

Abstract

An innovative phenology-based classification method was developed to map corn and soybean in multiple years using training data limited to a single year. Unlike traditional mapping efforts mainly based on multi-spectral image data, the classifier employed by this method takes phenological metrics as the major input. Phenological metrics represent crop characteristics related to crop calendar and progress such as the timing of emergence, maturity, harvest, etc. While considerable inter-annual variability exists among remotely sensed images from different years, phenological characteristics of each crop type are relatively consistent for a long period of time. Therefore, it is assumed that phenological metrics can be used to classify crop types in multiple years with the same rules, which is a valuable feature not possessed by traditional classification inputs.The classification method was tested in mapping corn and soybean, which are two dominant summer crop types in the central United States, and the experiment was carried out for Doniphan County, Kansas during years 2006–2010. Over 100 Landsat TM and ETM+images in this period were utilized and phenological metrics were calculated from Enhanced Vegetation Index time series using techniques including image segmentation and curve-fitting. Several sets of input variables, ranging from multi-spectral features of selected dates, which are widely used in traditional mapping efforts, to phenological metrics and derived measurements such as accumulated temperature, were tested using a random forest classifier. When the classifier was trained by reference data collected in the same year as that of remotely sensed data, most sets of input variables yielded accuracies higher than 88%. However, when the training data used by the classifier were obtained in a year different from the mapping years, only input sets containing phenological metrics were able to achieve acceptable accuracies greater than 80%. The use of phenological metrics as classification inputs avoids the restrictive requirements of a large ground reference dataset, enabling frequent and routine crop mapping without repeated collection of reference data.