Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions (≥1 km), which may subsume significant heterogeneity in vegetation LUE (ϵn, g MJ−1) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at ≈1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km2 area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several ϵn formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of ϵn based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that ϵn of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the ϵn for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean ϵn was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign ϵnresulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in ϵn when implementing LUE algorithms at coarse resolution.