Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area

Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.

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