Toshihiro Kujirai

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

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.

A method for enhancing spectral resolution of multispectral satellite imagery

It’s essential but challenging to retrieve spectral features as detailed as possible in current satellite imagery industry. In this research, based on the physical model of sensor response function, we present a method to recover the reflective spectrum at the front end of sensor in an iterative way and to greatly enhance the spectral details of satellite imagery. Our method is able to largely increase the cost-performance ratio of current satellite multispectral imagery and also reveals great potentials of satellite imagery in various disciplines.