Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes.