The ability to rapidly conduct in-situ chemical analysis of multiple samples of soil and other geological materials in the field offers many advantages over a traditional approach that involves collecting samples for subsequent examination in the laboratory. This study explores the application of complementary spectroscopic analyzers and a data fusion methodology for the classification/discrimination of >100 soil samples from sites across the United States. Commercially available, handheld analyzers for X-ray fluorescence spectroscopy (XRFS), Raman spectroscopy (RS), and laser-induced breakdown spectroscopy (LIBS) were used to collect data both in the laboratory and in the field. Following a common data pre-processing protocol, principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to build classification models. The features generated by PLSDA were then used in a hierarchical classification approach to assess the relative advantage of information fusion, which increased classification accuracy over any of the individual sensors from 80-91% to 94% and 64-93% to 98% for the two largest sample suites. The results show that additional testing with data sets for which classification with individual analyzers is modest might provide greater insight into the limits of data fusion for improving classification accuracy.

The presence of magnetic iron oxides in the soil can seriously hamper the performance of electromagnetic sensors for the detection of buried land mines and unexploded ordnance (UXO). Previous work has shown that spatial variability in soil water content and texture affects the performance of ground penetrating radar and thermal sensors for land mine detection. In this paper we aim to study the spatial variability of iron oxides in tropical soils and the possible effect on electromagnetic induction sensors for buried low-metal land mine and UXO detection. We selected field sites in Panama, Hawaii, and Ghana. Along several horizontal transects in Panama and Hawaii we took closely spaced magnetic susceptibility readings using Bartington MS2D and MS2F sensors. In addition to the field measurements, we took soil samples from the selected sites for laboratory measurements of dual frequency magnetic susceptibility and textural characteristics of the material. The magnetic susceptibility values show a significant spatial variation in susceptibility and are comparable to values reported to hamper the operation of metal detectors in parts of Africa and Asia. The absolute values of susceptibility do not correlate with both frequency dependence and total iron content, which is an indication of the presence of different types of iron oxides in the studied material.

Thermal sensors hold much promise for the detection of non-metallic landmines. However, the prediction of their thermal signatures depends on a large number of factors. In this paper, an analytical solution for temperature propagation through homogeneous and layered soils is presented to predict surface temperatures as a function of soil heat flux amplitude, soil texture, soil water content, and thermal properties and burial depth of the landmine. Comparison with the numerical model HYDRUS-2D shows that the relatively simple analytical solution proposed here is reasonably accurate. The results show that an increase in soil water content has a significant effect on the thermal signature, as well as on the phase shift of the maximum temperature difference. Different soil textures have relatively little effect on the temperature at the surface. The thermal properties of the mine itself can play a significant role. It is shown that for most soils 10 cm is the maximum burial depth to produce a significant thermal signature at the surface.

Accurate information on the distribution of sensible and latent heat fluxes as well as soil moisture is critical for evaluation of background characteristics. Since these fluxes are subject to rapid changes in time and space, it is nearly impossible to determine their spatial and temporal distributions over large areas from ground measurements alone. Therefore, prediction from remote sensing images is very attractive as it enables extensive area coverage and a high repetition rate. In this study, the Surface Energy Balance Algorithm for Land as implemented at New Mexico Tech (SEBAL^NM) is used to estimate sensible and latent heat fluxes in the White Volta Basin of Ghana, West Africa. The objectives are (i) to demonstrate a SEBAL^NM application in a part of the world were ground measurements are very scarce and (ii) to compare evapotranspiration (ET) maps obtained from Landsat and MODIS imagery, respectively. The results of this study demonstrate that SEBAL^NM can be applied for mapping sensible and latent heat fluxes as well as soil moisture over areas where few or no ground measurements are available using common satellite products (Landsat and MODIS).

The contrast in relative dielectric constant between landmines and the surrounding soil is one of the most important elements for radar detection purposes. For most geologic materials the relative dielectric constant lies within the range of 3-30, with dry sand at the lower end of this range at about 3-5. Nonmetallic landmines have a dielectric constant range of 3.2-9.8 whereas metallic landmines have a much higher relative dielectric constant. In previous work, literature data were used to compose a MATLAB model that determines whether or not field conditions are appropriate for use of GPR instruments. This model has been verified for dry and moist sand, silt, and clay soils in New Mexico. The objective of this paper is to validate this model over a wider range of soil texture and soil moisture conditions. Therefore, GPR measurements will be taken on experimental test facilities for landmine detection at Yuma Proving Grounds in Arizona and at the TNO Physics and Electronics Laboratory in The Netherlands. These facilities cover a wide range of soil textures from ferruginous sand to clay and peat as well as many levels of soil moisture.