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

Abstract

Accurate, in-season crop progress estimation (CPE) using remote sensing-based (RS) machine learning methods is challenging due to 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 ridge regression. GSTD guidance improved CPE during seasons when progress deviates from a regional average due to temperature, but were 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 compared to other methods. For silking, the most vulnerable growth stage to drought. Since the DSSAT simulations in this study are based upon historical planting information, the effectiveness of guidance was decreased by delayed in-season planting. An accurate RS method for estimating time of planting could link simulations to the current planting window and improve upon these results.