the maximum temperature turns into a beneficial force. The results for Brazil are less compelling with January and July effects no different from zero. The results for April and October are almost significant. They suggest that maximum temperature has a negative effect in April (harvest season) and a positive effect in October (planting season).

In Table 7, we present the results for NDVI. The NDVI depends heavily on the inverse of maximum temperature. It consequently works in exactly the opposite direction as the maximum temperature variable. In India, that means positive coefficients for April and July and negative coefficients for January and October. Once again the interpretation for NDVI would be similar to the maximum temperature interpretation above. Higher NDVI indices in January and October are harmful but higher indices in April and July are beneficial. The results in Brazil are again weak except for July (winter), which is very significant. Again, a high NDVI in winter is a bad thing inferring that high maximum temperatures in winter are beneficial.

The results in Table 8 concern polarization, which also is a measure of vegetation cover. We tested polarization in Brazil, India, and the United States. Very few of the polarization coefficients are significant. April (fall) and July (winter) polarization are negative and positive respectively in Brazil. But in the United States, only October (fall) polarization is significant and it is positive. Polarization measurements are thus generally insignificant and the coefficients that are significant are inconsistent across countries.

# **CONCLUSION AND POLICY IMPLICATIONS**

This study compares satellite and weather station data with agricultural performance across the landscape. In Brazil, India, and the United States, we examine how well the various climate measures explain land values or net revenue per hectare across municipios, districts, and counties, respectively. Climate measures from both satellites and weather stations are closely comparable. Temperature measurements from the satellites slightly outperform the weather station temperatures. In this case, the satellite’s ability to see the entire landscape is a clear advantage over the scattered weather station observations. However, the weather stations are able to measure precipitation more accurately. In countries where precipitation is important, such as Brazil, these measures give the weather stations an advantage. In India, by contrast, the surface wetness measures of the satellites are especially effective, giving the satellite’s measures even more explanatory power. However, part of this advantage may actually be reflecting irrigation (since the wetness product senses all sources of liquid water, not just precipitation). Therefore, the wetness product is not just a measure of climate, it is also a reflection of farmer choices.

The study also tests the importance of additional measurements made by the satellites. Maximum temperature has a significant and interesting pattern in India. In warmer months, it increases stress on plants and reduces productivity. In cooler months, warmer maximum temperatures are helpful, possibly by reducing risk of frost and drying fall harvests. In Brazil, the pattern was less clear, having no effect in the winter or summer and reducing productivity in the fall but increasing it in the spring. Normalized difference vegetation indices were also explored. Maximum temperature is in the denominator of the NDVI, so the NDVI coefficients exhibit a pattern exactly opposite 41