Atmospheric correction over coastal waters using multilayer neural networks
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 col...
| Published in: | Remote Sensing of Environment |
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| Main Authors: | , , , , , , |
| Language: | English |
| Published: |
ELSEVIER SCIENCE INC
2017
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| Subjects: | |
| Online Access: | https://publications.jrc.ec.europa.eu/repository/handle/JRC106751 http://www.sciencedirect.com/science/article/pii/S0034425717303310?via%3Dihub https://doi.org/10.1016/j.rse.2017.07.016 |
| Summary: | 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.D.2 - Water and Marine Resources |
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