The diffuse attenuation coefficient, K
d(λ), is an important water optical property. Detection of K
d(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogeochemical
processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical
d(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new K
d(490) models were developed for these waters by means of the support vector machine
approach. The obtained results showed that the two pre-existing K
d(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ~30%. More importantly, among the new models, type-specific models generally
outperformed the aggregated model. For water classified as Type 1 + Type 2, the type-specific model produced validation errors with MAPE = 16.8% and RMSE = 0.98 m−1. For water classified as Type 3, the MAPE and RMSE of the type-specific model
were found to be 18.8% and 1.85 m−1, respectively. The findings in this study demonstrate that water classification (prior to algorithm development) is needed for the development of excellent K
d(490) retrieval algorithms, and the type-specific models thus
developed are an important supplement to existing K
d(490) retrieval models for highly turbid inland waters.
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 & Technology, JiangSu Nanjing, China
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, JiangSu Nanjing, China
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, JiangSu Nanjing, China
May 3, 2014
More about this publication?