Ensemble Neural Network Methods for Satellite-Derived Estimation of Chlorophyll a

Wayne H. Slade, Richard L. Miller, Habtom Ressom, Padma Natarajan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

In this paper, neural network-based methods incorporating ensemble learning techniques are presented that estimate chlorophyll a (chl a) concentration in the coastal waters of the Gulf of Maine (GOM). A dataset was constructed consisting of in situ chl a measurements from the GOM matched with satellite data from the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS). These data were used to develop models using diverse neural network ensembles for estimation of chl a concentration from satellite-retrieved ocean reflectances. Results indicate that the models are able to generalize across geographical and temporal variation, and are resilient to uncertainty such as that introduced by poor atmospheric correction, or radiance contributions from non-chl a components in case 2 waters.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages547-552
Number of pages6
Volume1
StatePublished - Sep 24 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period7/20/037/24/03

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Ensemble Neural Network Methods for Satellite-Derived Estimation of Chlorophyll a'. Together they form a unique fingerprint.

Cite this