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A neural-network model to retrieve CDOM absorption from in situ measured hyperspectral data in an optically complex lake: Lake Taihu case study

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Abstract:

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.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431161.2010.481297

Affiliations: 1: Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, JiangSu Nanjing, China 2: Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, JiangSu Nanjing, China 3: College of Remote Sensing, Nanjing University of Information Science and Technology, JiangSu Nanjing, China

Publication date: 2011-07-20

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