In-season crop progress estimation using remote sensing and model-guided machine learning.

Accurate, in-season crop phenology estimation (CPE) using remote sensing-based (RS) machine learning methods is challenging due to limited ground-truth data. In this study, a biophysical crop model was used to guide neural network-based (NN) in-season CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations for corn across Iowa and Illinois in the US Midwest with in-season weather and historical information for planting and harvest. We investigated guiding the NN CPE method with growth stage (GSTD) and water stress factor (WSF) outputs from these simulations. Results show that guided NNs are able to estimate onset and progression of growth stages more accurately than an unguided baseline and a crop model-only method. GSTD guidance improved CPE during seasons when progress deviated from a regional average due to temperature, but was detrimental during seasons of delayed planting and harvest. WSF guidance improved CPE during seasons when planting and harvest were delayed by heavy rainfall, but performed less well during grainfill and mature stages. NN-based CPE guided by both GSTD and WSF provided the most accurate estimates for pre-emergence, emerged, silking, and grainfill stages, as well as lower root-mean-square error for the median stage transition date than reported in three full-season CPE studies. An accurate RS method for estimating planting could link DSSAT simulations to the current planting window and improve upon these results. This model-guided approach can be extended to other crops and regions to unlock in-season crop risk assessments that are directly linked to crop phenology.