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

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.

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

“…en, this pixel is affected to a class having the highest probability of pixel belonging. MLC is a one of the most commonly used supervised classification methods in remote sensing to derive land use/cover maps [3,[42][43][44]. SVM Classifier.…”

“…e support vector machine (SVM) is one of the most useful machine learning algorithms. It is based on statistical learning theory and has been extensively exploited in remote sensing for LC/LU mapping and crop classification [44].…”

“…It is such parallel system of calculation which consists of large number of basic processors with interconnections [49]. ANN is extensively adopted machine learning technique for LC/LU mapping [44,50,51]. It has the ability to learn from training ground samples and store the pattern of the each input variable (land cover classes in the present study).…”

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

“…Several studies have demonstrated that RS approaches can be used to produce reliable crop area estimates and crop type classification [39,[70][71][72]. In the ASA region of Arizona, nine major crop types were able to be classified using a RS approach with an overall accuracy of >85% [41].…”

Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco

“…However, high-resolution satellite data such as Landsat and Sentinel images are available and provide huge amounts of data regularly on large areas in short time and remotely. Satellite image-based land cover land use mapping, generally, still have some challenges despite the improvement of spatial and temporal resolution [26][27][28][29]. Particularly, forest tree species discrimination can face the presence of similar classes in the study area and/or the presence of several features within the same pixel, contributing to spectral confusion between different forest over types [30,31].…”