In-Season Corn Yield Prediction Using Satellite-Derived Solar-Induced Chlorophyll Fluorescence and Machine Learning Algorithms (opens in new tab)

Accurate in-season crop yield prediction is critical for timely agricultural decision-making, food security, and climate-resilient farm management. This study presents a framework for forecasting corn yield using only satellite-derived solar-induced chlorophyll fluorescence (SIF), a proxy for photosynthetic activity, as input to machine learning (ML) models. Biweekly SIF observations were collected from June to September over five growing seasons (2015–2020) for 210 corn-dominated counties in...