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

Fadila Muchsin, Kuncoro Adi Pradono, Indah Prasasti, Dianovita Dianovita, Kurnia Ulfa, Kiki Winda Veronica, Dandy Aditya Novresiandi, Andi Ibrahim

Abstract

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.

Keywords

atmospheric correction, Landsat-8, Sentinel-2, classification accuracy

Full Text:

PDF

References

Akar, Ö., & Güngör, O. (2012). Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, 1(2), 105–112. https://doi.org/10.9733/jgg.241212.1

Breiman, L. (2001). Random Forest. In Random Forest (45th ed., Vol. 45). https://doi.org/10.14569/ijacsa.2016.070603

Chavez, P. S. (1996). Image-based atmospheric corrections - Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.

Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J. C., … Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219(August 2017), 145–161. https://doi.org/10.1016/j.rse.2018.09.002

European Space Agency. (2022). Sentinel-2 Products Specification Document (PSD).

Franch, B., Vermote, E. F., Sobrino, J. A., & Fédèle, E. (2013). Analysis of directional effects on atmospheric correction. Remote Sensing of Environment, 128, 276–288. https://doi.org/10.1016/j.rse.2012.10.018

Franch, Belen, San Bautista, A., Fita, D., Rubio, C., Tarrazó-Serrano, D., Sánchez, A., … Uris, A. (2021). Within-field rice yield estimation based on sentinel-2 satellite data. Remote Sensing, 13(20). https://doi.org/10.3390/rs13204095

Ghimire, B., Rogan, J., & Miller, J. (2010). Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic. Remote Sensing Letters, 1(1), 45–54. https://doi.org/10.1080/01431160903252327

Lisenko, S. A. (2018). Atmospheric correction of multispectral satellite images based on the solar radiation transfer approximation model. Atmospheric and Oceanic Optics, 31(1), 72–85. https://doi.org/10.1134/S1024856018010116

Muchsin, F., Dirghayu, D., Prasasti, I., Rahayu, M. I., Fibriawati, L., Pradono, K. A., … Mahatmanto, B. (2019). Comparison of atmospheric correction models: FLAASH and 6S code and their impact on vegetation indices (case study: Paddy field in Subang District, West Java). IOP Conference Series: Earth and Environmental Science, 280(1). https://doi.org/10.1088/1755-1315/280/1/012034

Muchsin, F., Supriyatna, Harmoko, A., & Prasasti, I. (2022). Evaluation of Atmospheric Correction Methods of Sentinel-2 for Monitoring Paddy Rice Growth in Cianjur and Klaten Regency. Journal of Physics: Conference Series, 2243(1). https://doi.org/10.1088/1742-6596/2243/1/012023

Muchsin, Fadila, Fibriawati, L., & Pradhono, K. A. (2018). Atmospheric Correction Models of Landsat-7 Imagery. Jurnal Penginderaan Jauh Dan Pengolahan Data Citra Digital, 14(2). https://doi.org/10.30536/j.pjpdcd.1017.v14.a2595

Sola, I., García-Martín, A., Sandonís-Pozo, L., Álvarez-Mozos, J., Pérez-Cabello, F., González-Audícana, M., & Montorio Llovería, R. (2018). Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. International Journal of Applied Earth Observation and Geoinformation, 73(May), 63–76. https://doi.org/10.1016/j.jag.2018.05.020

Ulfa, K., Hendayani, Oktavia, M. I., Pradono, K. A., Fibriawati, L., Muchsin, F., … Damanik, K. W. V. (2020). Evaluation of atmospheric correction algorithms for Sentinel-2 over paddy field area. IOP Conference Series: Earth and Environmental Science, 500(1). https://doi.org/10.1088/1755-1315/500/1/012081

USGS. (2022). Landsat 8-9 Level 2 Science Product ( L2SP ) Guide March 2022 Landsat 8-9 (Vol. 2).

Vermote, E. F., & Kotchenova, S. (2008). Atmospheric correction for the monitoring of land surfaces. Journal of Geophysical Research Atmospheres, 113(23), 1–12. https://doi.org/10.1029/2007JD009662

DOI: http://dx.doi.org/10.30536/j.ijreses.2023.v20.a3845

Refbacks

  • There are currently no refbacks.