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Geographic Information System: GIS based Neural Network for Appropriate Parameter Estimation of Geophysical Retrieval Equations with Satellite Remote Sensing Data

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1 Author(s)
Arai, K. ; Dept. of Inf. Sci., Saga Univ., Saga

A method for appropriate geophysical parameter estimations in spatial and temporal domains with satellite remote sensing data based on neural network (NN) which is realized with GIS (it is referred to NN-GIS hereafter) is proposed. It is found that the proposed NN-GIS allow geophysical estimations with the most appropriate parameters for the areas and seasons in concern. One of the examples shows that SST estimation accuracy (0.245 K) is improved around 22.47% in comparison to the MCSST, typical regressive model based method (0.316 K) by applying the most appropriate parameters for the areas and seasons. It is also found that the proposed NN-GIS requires 2.63 times of computation resource in comparison to the existing regressive based methods.

Published in:

Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on

Date of Conference:

July 31 2006-Aug. 4 2006

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