Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning

Abstract

Timely crop type information (preferably before harvest) is useful for predicting food surpluses or shortages. This study assesses the performance of several machine learning classifiers, namely SVM (support vector machine), DT (decision tree), k-NN (k-nearest neighbour), RF (random forest) and ML (maximum likelihood) for crop type mapping based on a series of Sentinel-2 images. Four experiments with different combinations of image sets were carried out. The first three experiments were undertaken with 1) single-date (uni-temporal) images; 2) combinations of five images selected from the best performing single-date images; and 3) five images selected manually based on crop development stages. The fourth experiment involved the chronologic addition of images to assess the performance of the classifiers when only pre-harvest images are used, with the purpose of investigating how early in the season reasonable accuracies can be achieved. The experiments were carried out in two different sites in the Western Cape Province of South Africa to provide a good representation of the grain-producing areas in the region which has a Mediterranean climate. The significance of image selection on classification accuracies as well as the performance of machine learning classifiers when only pre-harvest images are used were evaluated. The classification results were analysed by comparing overall accuracies and kappa coefficients, while McNemar′s test and ANOVA (analysis of variance) were used to assess the statistical significance of the differences in accuracies among experiments. The results show that by selecting images based on individual performance, a viable alternative to selecting images based on crop developmental stages is offered, and that the classification of crops with an entire time series can be just as accurate as when they are classified with a subset of hand-selected images. We also found that good classification accuracies (77.2%) can be obtained with the use of SVM and RF as early as eight weeks before harvest. This result shows that pre-harvest images have the potential to identify crops accurately, which holds much potential for operational within-season crop type mapping.