Application of neural networks to AVHRR chlorophyll-a and turbidity estimation

Yuanzhi Zhang; Jouni Pulliainen; Sampsa Koponen; Martti Hallikainen

doi:10.1117/12.452813

14 January 2002 Application of neural networks to AVHRR chlorophyll-a and turbidity estimation

Yuanzhi Zhang, Jouni Pulliainen, Sampsa Koponen, Martti Hallikainen

Proceedings Volume 4488, Ocean Optics: Remote Sensing and Underwater Imaging; (2002) https://doi.org/10.1117/12.452813
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

This paper presents the application of neural networks to chlorophyll-a and turbidity estimation using AVHRR data over the Gulf of Finland. Chlorophyll-a and turbidity are two major parameters in surface waters used for monitoring coastal water quality in the study. Since the Gulf of Finland is highly affected by the input from the rivers where have a high concentration of mineral suspended solids and nutrients, the coastal waters of the Gulf are optically dominated by absorption from both dissolved and particulate organic matters. Although AVHRR imagery can provide a synoptic view on surface water information of coastal areas, its quantitative use is still a difficult task in this study.

Citation Download Citation

Yuanzhi Zhang, Jouni Pulliainen, Sampsa Koponen, and Martti Hallikainen "Application of neural networks to AVHRR chlorophyll-a and turbidity estimation", Proc. SPIE 4488, Ocean Optics: Remote Sensing and Underwater Imaging, (14 January 2002); https://doi.org/10.1117/12.452813

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