frequencies: 19, 37, and 85 GHz with vertical and horizontal polarization and 22 GHz with only vertical polarization. All of these frequencies are in atmospheric window regions with the 22 and 85 GHz channels having weak water vapor absorption. The measurements consequently can be made through clouds. Various signatures among the seven channel measurements were used to identify surface types and calculate dynamic emissivity adjustments (reflections by surface type)(Basist et al 1998).

The primary difficulty in deriving surface temperature from passive microwave measurements is the variable emissivity associated with different surface types. For the microwave spectrum the emissivity of soil depends on its water and/or mineral content, as well as the effects of vegetation and surface roughness. Since the microwave emissivity is variable, the brightness temperature is not a function of surface temperature alone. Therefore, any algorithm that attempts to estimate surface temperature must first infer the particular surface condition for a microwave measurement, and either make appropriate emissivity adjustments to the microwave measurement, or filter the measurement if reliable adjustments are not currently possible. The approach used here assumes no a priori information about the surface conditions, allowing the satellite observations to provide a dynamic assessment of the surface type and current emissivity. The Basist Wetness Index (BWI) is simply the emissivity adjustment associated with water in the radiating surface. Surface wetness has strong correspondence with the upper level surface wetness at many locations (Basist et al. 2001). Moreover, wetness can originate from multiple sources (i.e. precipitation, snow melt, irrigation).

The difference between precipitation and surface wetness deserves some discussion. Precipitation captures the water falling through the air at any one moment. Surface wetness captures the stock of water in the top soil. Surface wetness consequently is affected by irrigation whereas precipitation is not. Further, surface wetness has a memory. It reflects not only the current weather but also recent history. Finally, surface wetness is also sensitive to the soil as some soil types can hold moisture better than others.

Three inputs from the DMSP satellites are used in this study: monthly climatologies for temperature, surface wetness, and polarization from the period January 1988 to 2002 for Brazil, India, and the United States. The spatial resolution is approximately 30 km. January temperatures for the United States had to be dropped because the satellite could not measure values accurately when the ground is covered by snow. Polarization provides a measure of the land use, as different surfaces (soils, crops, trees) reflect light differently. Generally the lower the polarization, the more vegetation covers the surface. Unfortunately, both water on the surface and bare ground increase polarization. Even with this potential confusion in the interpretation, the study allows us to test whether polarization can be used as a significant predictor We also rely on the Advanced Very High Resolution Radiometer (AVHRR) instrument on board NOAA-9, 11, 14 and 16 polar-orbiting spacecrafts. The data were collected from the NOAA/NESDIS archive using the Global Vegetation Index (GVI) product covering the period 1985-2001 (Kidwell 1997). The GVI is produced by sampling and mapping the AVHRR-based 4-km (global area coverage format, GAC) daily radiances in the visible-VIS (0.58-0.68 μm), near infrared- NIR (0.72-1.1 μm), and infrared-IR (10.3-11.3 and 11.5-12.5 μm) spectrums. These values were truncated to 8-bit 37