Coloured dissolved organic matter (CDOM) is an important water component that affects water colour and ecological environment under water. The remote estimation of CDOM is always a challenge in the field of water-colour remote sensing owing to its weak signal. To further study the CDOM-retrieval
approach, field experiments, including water-quality analysis and spectral measurements, were carried out in Lake Taihu waters from 8 to 21 November 2007. On the foundation of analysing water-inherent optical properties, sensitive spectral factors were selected, and then neural-network models
were established for retrieving CDOM. The results show that the model with 10 nodes in the hidden layer performs best, yielding a correlation coefficient (R) of 0.887 and a root-mean-square error of 0.156 m−1. Meanwhile, the predictive errors of the model developed
here and the previously proposed algorithms were compared with each other. The mean value of the relative error of the former is 12.8% (standard deviation of 29.9%), and is much lower than its counterpart of other models, which indicates that the developed model has a higher accuracy for CDOM
retrieval in Lake Taihu waters. Meanwhile, other datasets collected at different times were also imported into the model for applicability analysis; the derived errors suggest a relatively good performance of the model. This research firstly explores the CDOM retrieval in optically complex
lake waters, and the corresponding findings support a technical framework for accurately extracting CDOM information in Lake Taihu waters, based on an adequate understanding of water optical properties.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, JiangSu Nanjing, China
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, JiangSu Nanjing, China
College of Remote Sensing, Nanjing University of Information Science and Technology, JiangSu Nanjing, China
Publication date: 20 July 2011
More about this publication?