A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

Yoriko Kazama; Toshihiro Kujirai

doi:10.1117/12.2069570

11 October 2014 A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

Yoriko Kazama, Toshihiro Kujirai

Author Affiliations +

Proceedings Volume 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI; 92390X (2014) https://doi.org/10.1117/12.2069570
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

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.

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Yoriko Kazama and Toshihiro Kujirai "A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield", Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92390X (11 October 2014); https://doi.org/10.1117/12.2069570

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