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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Dianovita
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Earth & Planetary Sciences

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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; Kurnia Ulfa; Kiki Winda Veronica; Dandy Aditya Novresiandi; Andi Ibrahim
International Journal of Remote Sensing and Earth Sciences Vol. 20 No. 1 (2023)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2023.v20.a3845

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.
NEW AUTOMATED CLOUD AND CLOUD-SHADOW DETECTION USING LANDSAT IMAGERY Kustiyo; Dianovita; Hedi Ismaya; Mulia Inda Rahayu; Erna Sri Adiningsih
International Journal of Remote Sensing and Earth Sciences Vol. 9 No. 2 (2012)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2012.v9.a1831

Abstract

Cloud cover has become a major problem in the use of optical satellite imageries, particularly in Indonesian region located along equator or tropical region with high cloud cover almost all year round. In this study, a new method for cloud and cloud shadow detection using Landsat imagery for specific Indonesian region was developed to provide a more efficient and effective way to detect clouds and cloud shadows. Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) were used as inputs into the model. The first step was to detect cloud based on cloud physical properties using albedo and thermal bands, the second step was to detect cloud shadows using the Near Infrared (NIR), and Short Wave Infrared (SWIR) bands, and finally, the geometric relationships were used to match the cloud and cloud shadow layer, before proceeding to the production of the final cloud and cloud shadow mask. The results were then compared with other method such as tree base cloud separation. It showed that method we proposed could provide better result than tree base method, the accuracy result of this method was 98.75%.
Co-Authors Andi Ibrahim Dandy Aditya Novresiandi Erna Sri Adiningsih Fadila Muchsin Hedi Ismaya Indah Prasasti Kiki Winda Veronica Kuncoro Adi Pradono Kurnia Ulfa Kustiyo Mulia Inda Rahayu