Recent advances both in satellite sensors and algorithms have improved the scope for tracking key agricultural outcomes from space. The resulting datasets open up new pathways for understanding how farmers and their crops respond to changes, such as those in climate or air quality. In particular, the highly localized information from satellites exposes variation that is complementary to the typical sources of variation used for identification in panel models. Here we present an analysis of effects of ozone, particulate matter, sulfur dioxide, and nitrogen dioxide on maize and soybean in the United States. We show how gradients near powerplants allow more precise estimation of pollution effects, particularly for nitrogen dioxide. The estimates suggest that progress made on air quality in recent decades can explain a significant share of overall crop yield increases.