Although considerable uncertainty exists in disease prediction through remote sensing technology, particularly for vector-borne disease as discussed above, satellite technology has been applied with some success to predictive modeling for cases of hantavirus pulmonary syndrome (HPS). The 1993 outbreak of HPS in the southwestern United States was believed to be linked to environmental conditions and, in particular, to abnormally high rainfall that resulted in increased vegetation with a subsequent explosion in the rodent populations. Several research groups have subsequently modeled conditions that led to an HPS outbreak, with mixed success. Engelthaler et al. (14) looked at 10 years of data on monthly precipitation and daily ambient temperature in the Southwest region (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) in relation to HPS cases (1993) (1994) (1995) . They found that cases tended to cluster seasonally and temporally by biome type and elevation and only indirectly demonstrated a possible association between the 1992/1993 El Niño precipitation events and HPS. Glass et al. (15, 16) were also unable to make a definitive link with precipitation events in their analyses of HPS in the southwestern United States. They did, however, find a relationship between Landsat Thermatic Mapper (LTM) images recorded by satellite in 1992 and HPS risk the following year. LTM generates numbers that represent reflected light in 6 bands, 2 of which were associated with decreased risk and 1, in the mid-infrared range, with increased risk. The authors admit that considerable ground truthing is necessary to relate satellite imagery to the environmental variables being measured (i.e., vegetation, soil type, soil moisture) and their relation to rodent population dynamics.