Predicting the spatiotemporal chlorophyll-a distribution in the Sea of Japan based on SeaWiFS ocean color satellite data

We developed a new statistical spatiotemporal model for chlorophyll-a (chl-a) distribution over the Sea of Japan, derived from the satellite-based Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Because preliminary analysis showed that the SeaWiFS data exhibit anisotropy in space and autocorrelatio...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2006-04, Vol.3 (2), p.212-216
Main Authors: Kiyofuji, H., Hokimoto, T., Saitoh, S.-I.
Format: Article
Language:English
Subjects:
Anisotropic magnetoresistance
Aquaculture
Autocorrelation
Biological system modeling
Chlorophyll
data assimilation
Equations
forecasting
Mathematical models
Numerical prediction
Ocean color
Oceans
Predictive models
Satellites
Sea of Japan
Sea-viewing Wide Field-of-view Sensor
Sea-viewing Wide Field-of-view Sensor (SeaWiFS)
Sensor phenomena and characterization
Spatiotemporal phenomena
spatiotemporal statistical model
Springs
Summer
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Summary:We developed a new statistical spatiotemporal model for chlorophyll-a (chl-a) distribution over the Sea of Japan, derived from the satellite-based Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Because preliminary analysis showed that the SeaWiFS data exhibit anisotropy in space and autocorrelation in time, we propose a new spatiotemporal model for chl-a distribution and its predictor. Numerical prediction experiments applying the SeaWiFS data showed that the predictor could forecast chl-a distributions in summer and early fall well, although further changes in the model structure will be necessary to predict aspects of the spring and late fall blooms.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2005.861931