Conjoint use of NIR and XRF spectroscopy in the field
Proximal soil sensing techniques are an efficient and effective way to gather information about the soil in the field. They are used to identify and map the areal variations of soil properties across the landscape and therefore to make inferences about the quality of the soils. These technologies can give us better soil data, which we mean data obtained more efficiently, so that a larger number of samples are analysed at lower costs and in less time. Spectroscopic techniques are now being used and explored as possible alternatives to enhance or replace conventional laboratory methods of soil analysis. This project will investigate proximal soil sensors for rapid measurement of soil properties in the field, particularly the conjoint use of a portable XRF (X Ray Fluorescence), and Near Infrared Spectroscopy
While we can collect detailed soil information at limited locations and interpolate the values across space and time, in some instances it would be more beneficial if we can directly measure soil information at a fine spatial scale (e.g. measurement every 10 metres). Proximal soil sensing acquires information about soil through the use of sensors that are placed in proximity to the soil in situ, which is in contrast to remote sensing. This project will investigate proximal soil sensors for rapid measurement of soil properties in the field, particularly the conjoint use of a portable XRF (X Ray Fluorescence), and Near Infrared Spectroscopy. These instruments offer us a growing range of soil data, thus they need to be integrated using a data fusion approach. This will be investigated with a firm ground in statistical theory, and at the same time exploiting the power of the computer to search for structure in these large data sets.
This project involves field, laboratory and modelling work. The student will acquire valuable knowledge and skills in the areas of soil spectroscopy and inference system. The student will also be expected to further develop skills in modern statistical and spatial data analysis and scientific publication.
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The opportunity ID for this research opportunity is: 1708
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