Infrared imagers are capable of the detection of surface laid mines. Several sensor fused land mine detection systems make use of metal detectors, ground penetrating radar and infrared imagers. Infrared detection systems are sensitive to apparent temperature contrasts and their detection capabilities are inversely proportional to the amount of background clutter generated by local surface non- uniformities. This may result in spurious detections, or even cancellation of true detections in a post classification process. Sub-surface mines can be detected when buried not too deeply. Furthermore, soil type and soil water content will influence the detection result. For this reason experiments in various soil types, including vegetation, and soil circumstances are essential for understanding and improving the infrared detection capabilities. We have performed outdoor experiments with different types of soil and weather conditions. Several examples are described and analyzed. Data analysis shows the strong correlation of apparent temperature with thermocouple gradients and solar energy, as well as a correlation of local standard deviation with these parameters. Model based temperature contrasts are predicted for several mines in sandy soils, and these are compared with infrared imaging apparent temperature measurements and thermocouple data. The comparison results are quite good but also show the complexity of the thermal infrared data, in particular due to infrared clutter, diurnal variations, and sky reflectance contributions. Model predictions are made for the application of active heating methods also. Limitations of the model and potential future expansions based on evaluation of experiments are discussed. We discuss the potential use of modeling for thermal infrared detection and sensor fusion applications.