precision, and mapped to a (16 km<sup>2</sup> ) latitude/longitude grid. To minimize cloud effects, the maps were composited over a 7-day period by saving radiances for the day that had the largest difference between NIR and VIS (Kidwell 1997). The reflectances in the VIS and NIR and emission in the IR (CH4, 10.3-11.3 μm) were used for studying application of AVHRR indices as a proxy for climate and land use in Brazil and India.

The AVHRR-based radiances are known to have both inter-annual and intraannual noise due to variable illumination and viewing conditions, sensor degradation, satellite navigation and orbital drift, atmospheric and surface conditions, methods of data sampling and processing, communication and random errors (Gutman 1991). The initial processing included post launch calibration of VIS and NIR following Rao and Chen (1999), calculation of NDVI ([NIR-VIS]/[NIR+VIS]), and conversion of CH4 (methane) radiance to brightness temperature (BT). The latter measure was corrected for the nonlinear behavior of the sensor (Kidwell 1997). As the result, long-term noise was reduced substantially. Furthermore, temporal high frequency noise was completely removed from NDVI and BT annual time series using statistical smoothing techniques. The 1985-2000 climatology (extreme values) were then calculated (Kogan 1997, 2001).

The climatology of NDVI and BT was approximated by the multi-year extreme (maximum and minimum) values for each pixel and week. These extremes are known to characterize carrying capacity of land ecosystems and climate. It is important to emphasize that NDVI and BT are very unique parameters. NDVI characterizes the distribution of vegetation on the earth surface. NDVI values reflect the combined impact of moisture and thermal resources on vegetation and this impact is cumulative. Brightness temperature from the GVI data set characterizes the highest temperature in the diurnal cycle. These properties make the indices valuable for agricultural and water resource applications.

# **RESULTS**

The purpose of the following analysis is to compare the effectiveness of weather station versus satellite measurements of climate. We begin the analysis in the US examining the effectiveness of just the temperature measurements from the ground and satellite data sets. We regress April, July, and October average monthly temperature measurements and their squared values on US land values in Table 3 (columns 1 and 4). The coefficients are significant for both the linear and squared terms for all three months for both the weather station and the satellite data. However, the R-squared is slightly higher for the satellite data. The satellite data does a better job of describing the pattern of farmland values across the US landscape. Presumably, this is because it is doing a better job of measuring the actual temperatures in each county.

In the second analysis in Table 3 (columns 2 and 5), we add other climate measures as well, including precipitation for the weather stations, surface wetness for the satellite, and interannual variance for both. The coefficients of most of these additional measurements are significant. The overall R-squared for the satellite measurements remain slightly higher. In the third analysis in Table 3 (columns 3 and 6), we also include soils and economic control variables. Including these remaining variables improves the overall fit. Climate normals, however, continue to play a large role in explaining land values across the US. Overall, the satellite data continues to give slightly 38