Proximal soil sensing for improved delineation of contaminated sites


There are many thousands of contaminated soil sites across urban and rural Australia. These sites contaminated with metals and/or organics pose a potential threat to human health. Detection and remediation of such sites are expensive. Through a novel proximal soil sensing method combined with data –fused soil inference and optimised sampling and mapping, it is possible to efficiently identify those areas of any site requiring remediation. This will reduce the barrier to detection and remediation considerably hastening the removal of this health risk.


Professor Alex McBratney, Dr Thomas Bishop, Dr Budiman Minasny

Research Location

Sydney Institute of Agriculture - Australian Technology Park

Program Type



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, in contrast to remote sensing. This work will investigate the use of proximal soil sensing in soil contamination assessment. Main objectives are the firstly the development of a soil inference system with associated measurement technology that can estimate a range of possible contaminants at any location. The aim will be to generate spatial soil information with a resolution that is required for remediation of contaminated sites.

Additional Information

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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proximal soil sensing, soil contamination, spatial statistics, uncertainty assessment

Opportunity ID

The opportunity ID for this research opportunity is: 1833

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