Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

¹ Research Center for Oceanography, National Research and Innovation Agency, Ancol, North Jakarta 14430, Indonesia
² Research Center for Remote Sensing, National Research and Innovation Agency, Cibinong, West Java 16915, Indonesia
³ Center for Data and Information, National Research and Innovation Agency, Cibinong, West Java 16915, Indonesia

^* E-mail: devica.natalia.brginting@gmail.com

Abstract

Sentinel-2 is high-resolution multispectral imagery that launched by the European Space Agency on June 23, 2015 for Sentinel-2A and March 7, 2017 for Sentinel-2B. The two satellites were launched with the aim of land monitoring studies, including vegetation, soil, and water cover, as well as the observation of inland waterways and coastal areas. In 2018, Sentinel-2 produced bottom-of-atmosphere (L2A) imagery derived from top-of-atmosphere (L1C), which has been atmospherically corrected using Sen2Cor algorithm. However, there is an overcorrection effect due to inaccuracies of digital elevation model, over-detection of clouds over bright targets, and miss-classification of topographic shadows. This research aims to explore the application of Sentinel-2 imagery for mangrove mapping by comparing two levels of data, including L1C and L2A. L2A is divided into two, namely L2A atmospherically corrected using the Sen2Cor method (L2A_Sen2Cor) and dark object subtraction method (L2A_DOS). The classification scheme was built based on in-situ data containing seven objects: water, clouds, built-up, cloud shadows, bare land, mangroves, and land vegetation using random forest classification. The comparison of each level of data is analyzed based on the spectral signature and accuracy assessment using confusion matrix. The result shows that there are differences in the spectral signature between L1C and L2A data because of atmospheric impacts. L2A outperforms L1C, as shown by the higher coefficient of determination (R²). The accuracy is in the range of 93.7 – 95.4%, with the best accuracy shown by L2A_Sen2Cor.