A new synergistic approach for crop discrimination in a semi-arid region using Sentinel-2 time series and the multiple combination of machine learning classifiers

Abstract

Abstract Accurate monitoring of agricultural lands and crop types is a crucial tool for sustainable food production. Therefore, to provide reliable and updated crop maps, the improvement of satellite image classification approaches is essential. In this context, machine learning algorithms present a potential tool for efficient and effective classification of remotely sensed data. The main strengths of machine learning algorithms are the capacity to handle data of high dimensionality, and mapping classes characterized by strong complex dynamics. The main objective of this work was to develop a new synergistic approach for crop discrimination in the semi-arid region of Chichaoua province, located in the Marrakesh-Safi region, Morocco, using high spatio-temporal resolution imagery and a multiple combination of machine learning classifiers. This approach was developed based on 10m spatial resolution open access Sentinel-2 (S2) images and machine learning algorithms. The atmospherically corrected S2 images were accessed through the Theia Land Data Center. Reference dataset was collected from a field survey carried out during the 2018 agricultural season in order to train the classifiers. Artificial Neural Networks, Support Vector Machine, K-Nearest Neighbors, Bagged Trees, Naive Bayes, Discriminant Analysis and Decision Trees classifiers were trained over the study area and the accuracy metrics, mainly Overall Accuracy (OA) and Kappa coefficient (K), were assessed. The trained models were single classifiers to build the ensemble classifier system. The obtained results showed high OA and K values up to 96% and 0.95 respectively, achieved by the developed approach. Therefore, based on these results, the approach we developed using the combination of multiple classifiers has a significant impact on crop classification quality.