Soil reflectance spectroscopy is a well known technique to assess soil properties rapidly and quantitatively both in point (Spectroscopy) and image (Imaging Spectroscopy (IS)) domains. The quantitative approach has been developed in the past two decades by many researchers and many papers have been published on this subject in the scientific literature. Basically, the quantitative approach has been adopted from other disciplines (e.g. food, textile, pharmaceutical); whereas the mapping approach has been developed by incorporating spectroscopy with remote sensing means (IS).
Soil contamination is an ever growing concern. There is a great need for an objective, environmental friendly method to rapidly detect and monitor soil contaminants, both for the diagnosis of suspected contaminated areas, as well as controlling rehabilitation processes. Conventional methods for investigating soil contamination based on point sampling and chemical analysis are time consuming, relatively expensive and sometimes incorporate the use of non environmental-friendly chemicals. Using soil reflectance spectroscopy, several direct and indirect soil properties as well as soil contamination characteristics can be extracted and monitored efficiently.
Due to the vast amount of data in soil spectroscopy, either for point or image, data mining processes are needed. In this study we present the soil spectroscopy theory and its quantitative capabilities as well as the data mining methods of the soil spectra that are related to soil contamination. The contaminants that shall be inspected include petroleum hydrocarbons, heavy metals, acid mine drainage, pesticides, and vegetation stress as an indicator for soil contamination. We are reviewing all work done to assess these contaminations in soil either by spectroscopy or IS methods with a special emphasis on petroleum hydrocarbons contaminations.
The traditional chemistry based determination process of measuring petroleum