In‐season crop phenology using remote sensing and model‐guided machine learning

• Published in: Agronomy journal 2023-05, Vol.115 (3), p.1214-1236
• Main Authors: Worrall, George, Judge, Jasmeet, Boote, Kenneth, Rangarajan, Anand
• Format: Article
• Language: English
• ISSN: 0002-1962; 1435-0645
• DOI: 10.1002/agj2.21230

Accurate in‐season crop phenology estimation (CPE) using remote sensing (RS)‐based machine‐learning methods is challenging because of limited ground‐truth data. In this study, a biophysical crop model was used to guide neural network (NN)‐based, in‐season CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations for corn (Zea mays L.) across Iowa and Illinois in the U.S. 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 phenological 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 because of 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. Neural network‐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 RMSE 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. Core Ideas Remote sensing‐based CPE with machine learning is challenged by limited training data. DSSAT crop model outputs provide additional guidance to NN‐based CPE methods. An NN guided with GSTD and WSF from DSSAT outperforms unguided methods. GSTD improves CPE during seasons with abnormal temperatures; WSF improves CPE during rainfall‐driven delays. More accurate planting date estimates will improve crop model‐guided CPE during delayed seasons.