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Zeng, Qun
- Water Environment Remote Sensing Atmospheric Correction of Geostationary Ocean Color Imager Data over Turbid Coastal Waters in the Bohai Sea Using Artificial Neural Networks
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Authors
Affiliations
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, CN
2 School of Urban and Environment Science, Huazhong Normal University, Wuhan 430079, Hubei, CN
3 Nanjing University, Nanjing, Jiangsu Province 210023, CN
4 Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, CN
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, CN
2 School of Urban and Environment Science, Huazhong Normal University, Wuhan 430079, Hubei, CN
3 Nanjing University, Nanjing, Jiangsu Province 210023, CN
4 Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, CN
Source
Current Science, Vol 110, No 6 (2016), Pagination: 1079-1085Abstract
The Geostationary Ocean Color Imager (GOCI) can produce good ocean colour products in the open sea. However, an atmospheric correction problem continues to occur for turbid coastal water environment monitoring. In this communication, a regional atmospheric correction method based on an artificial neural network (ANN) model has been proposed. The ANN model was built according to differences in the spatial and radiometric characteristics between the Medium Resolution Imaging Spectrometer (MERIS) and GOCI, with 3000 pixels of the top-of-atmosphere (TOA) reflectance of seven GOCI images from 2011 to 2012 above turbid water used as the inputs and coinciding validated remote-sensing reflectance (Rrs) of MERIS1 used as the outputs. Subsequently, the water-leaving reflectance of GOCI in turbid coastal water areas of the Bohai Sea was derived. Compared with the products produced by the standard GOCI Data Processing System (GDPS Version 1.3), the Rrs retrieved according to the proposed method showed a significant improvement in spatial pattern. Although the ANN model displayed a degree of difficulty in representing high water-leaving reflectance values, a comparison with three in situ measurements collected on 11 November 2011 in the study area showed encouraging results. The results suggest that the ANN method can be used for atmospheric correction process in turbid waters without requiring numerous in situ measurements.Keywords
Artificial Neural Network, Atmospheric Correction, Ocean Color Imager, Remote Sensing, Turbid Coastal Waters.Full Text
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