Title: Atmospheric correction over coastal waters using multilayer neural networks
Authors: YONGZHEN FANLI WEIGATEBE CHARLESJAMET CÉDRICZIBORDI GIUSEPPESCHROEDER T.STAMNES KNUT
Citation: REMOTE SENSING OF ENVIRONMENT vol. 199 p. 218-240
Publisher: ELSEVIER SCIENCE INC
Publication Year: 2017
JRC N°: JRC106751
ISSN: 0034-4257
URI: http://www.sciencedirect.com/science/article/pii/S0034425717303310?via%3Dihub
http://publications.jrc.ec.europa.eu/repository/handle/JRC106751
DOI: 10.1016/j.rse.2017.07.016
Type: Articles in periodicals and books
Abstract: Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) method. We used a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and trained a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The method is validated using both a synthetic dataset and Aerosol Robotic Network Ocean Color (AERONET-OC) measurements.
JRC Directorate:Sustainable Resources

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