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Open AccessArticle

Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data

1
Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain
2
Higher Polytechnic School of Ávila, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(11), 1735; https://doi.org/10.3390/rs12111735
Received: 31 March 2020 / Revised: 22 May 2020 / Accepted: 25 May 2020 / Published: 28 May 2020
The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool. View Full-Text
Keywords: Sentinel-2A; Landsat-8; crop classification; machine learning; satellite-based remote sensing; irrigation; land management Sentinel-2A; Landsat-8; crop classification; machine learning; satellite-based remote sensing; irrigation; land management
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MDPI and ACS Style

Chakhar, A.; Ortega-Terol, D.; Hernández-López, D.; Ballesteros, R.; Ortega, J.F.; Moreno, M.A. Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data. Remote Sens. 2020, 12, 1735.

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