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Proceedings Paper

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield
Author(s): Yoriko Kazama; Toshihiro Kujirai
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Paper Abstract

A method called a “spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model,” which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Paper Details

Date Published: 11 October 2014
PDF: 7 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390X (11 October 2014); doi: 10.1117/12.2069570
Show Author Affiliations
Yoriko Kazama, Hitachi, Ltd. (Japan)
Toshihiro Kujirai, Hitachi, Ltd. (Japan)

Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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