EFFECT OF ATMOSPHERIC CORRECTION ALGORITHM ON LANDSAT-8 AND SENTINEL-2 CLASSIFICATION ACCURACY IN PADDY FIELD AREA

Fadila Muchsin (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))
Kuncoro Adi Pradono (Directorate of Laboratory Management, Research Facilities, and Science and Technology Park, National Research and Innovation Agency (BRIN))
Indah Prasasti (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))
Dianovita Dianovita (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))
Kurnia Ulfa (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Indonesia)
Kiki Winda Veronica (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))
Dandy Aditya Novresiandi (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))
Andi Ibrahim (Research Center for Remote Sensing, National Research and Innovation Agency (BRIN))

International Journal of Remote Sensing and Earth Sciences (IJReSES)
Vol 20, No 1 (2023)

Landsat-8 and Sentinel-2 satellite imageries are widely used for various remote sensing applications because they are easy to access and free to download. A precise atmospheric correction is necessary to be applied to the optical satellite imageries so that the derived information becomes more accurate and reliable. In this study, the performance of atmospheric correction algorithms (i.e., 6S, FLAASH, DOS, LaSRC, and Sen2Cor) was evaluated by comparing the object's spectral response, vegetation index, and classification accuracy in the paddy field area before and after the implementation of atmospheric correction. Overall, the results show that each algorithm has varying accuracy. Nevertheless, all atmospheric correction algorithms can improve the classification accuracy, whereby those derived by the 6S and FLAASH yielded the highest accuracy.