To increase the utilization of satellite remote sensing data in precision agriculture, it is necessary to retrieve the most relevant variables from the satellite signals so that the retrievals can be directly utilized by agricultural management entities. The variables that make up the state vector description of existing crop growth models provide inherent relevance to on-farm decision making because they can be used to predict future crop status based on changing farm inputs. In this study, the information content of MODIS spectral surface reflectance measurements with respect to the state variables in the STICS crop growth model for maize is analyzed. Specifically, it is shown that the MODIS measurements can predict the state variables of an ensemble average of STICS crop growth simulations with R2 values of up to 0.75 using a bidirectional long short-term memory (BLSTM) network. The analysis is performed using a training, validation, test data division scheme typical in machine learning using county-median measurements from 36 counties across the United States; data from 2006, 2008, 2010, 2012, and 2014 is used for training, data from 2005, 2009, and 2011 is used for validation and data from 2007 and 2013 is used for testing. Significant correlation of the harvested organ biomass, subsurface soil water, and phenological stage state variables with the MODIS measurements is shown in this study, implying that the remote sensing signal can be used for significantly more than for retrieving the leaf area index.