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Proceedings Paper

Atmospheric correction for Landsat 8 over case 2 waters
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Paper Abstract

The most interaction between humankind and water occurs in coastal and inland waters (Case 2 waters) at a scale of tens or hundred of meters, but there is not yet an ocean color product at this spatial scale. Landsat 8 is a promising candidate to address the remote sensing of these kinds of waters due to its improved signal-to-noise ratio (SNR), spectral resolution, 12-bit quantization, and high spatial resolution. Standard atmospheric correction algorithms developed for heritage ocean color instruments (e.g. MODIS, SeaWiFS) require a sufficient SNR in two bands where the water-leaving signal is negligible, which is not always possible, particularly for Landsat 8's bands. The model-based empirical line method (MoB-ELM) atmospheric algorithm for Landsat 8 imagery does not rely on this assumption. In this work, we evaluate the performance of this algorithm. We compare the MoB-ELM algorithm with in situ data and with three standard atmospheric correction algorithms. The results from our algorithm are comparable with the standard algorithms in some bands when comparing remote-sensing reflectances. When compared with in situ remote-sensing reflectance, the MoB-ELM perform similar to the standard algorithm in most cases. A comparison of retrieved chlorophyll-a concentration was perform as well, showing that the MoB- ELM outperforms the rest at high concentrations commonly found in Case 2 waters. These results show that our atmospheric correction algorithm allows one to use Landsat 8 to study Case 2 waters as an alternative to heritage ocean color satellites.

Paper Details

Date Published: 8 September 2015
PDF: 16 pages
Proc. SPIE 9607, Earth Observing Systems XX, 96070R (8 September 2015); doi: 10.1117/12.2188345
Show Author Affiliations
Javier A. Concha, Rochester Institute of Technology (United States)
John R. Schott, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9607:
Earth Observing Systems XX
James J. Butler; Xiaoxiong (Jack) Xiong; Xingfa Gu, Editor(s)

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