Skip to main content
padlock icon - secure page this page is secure

New strategy to improve estimation of diffuse attenuation coefficient for highly turbid inland waters

Buy Article:

$61.00 + tax (Refund Policy)

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 pre-existing K 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
No Metrics

Document Type: Research Article

Affiliations: 1: Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, JiangSu Nanjing, China 2: Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, JiangSu Nanjing, China 3: State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, JiangSu Nanjing, China

Publication date: May 3, 2014

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more