Automated and Scalable Corn and Sorghum Mapping Across Diverse Regions Using GEDI and Time-Series Sentinel-2 Imagery

Accurate and efficient mapping of crop spatial distribution is crucial for agricultural monitoring, yield prediction, and environmental sustainability.  In this study, we developed a novel workflow, GEDI-Guided Crop Mapping Framework (GGCMF), for high-resolution mapping of corn and sorghum by integrating GEDI data, Sentinel-2 imagery, and machine learning classifiers within the Google Earth Engine (GEE) platform. The GGCMF workflow begins by utilizing historical CDL crop type maps to extract canopy height and vertical structural differences from GEDI L2A Vector data, which are processed within a newly developed GEE-compatible framework.  This ensures minimal geolocation errors and allows the accurate differentiation of high- and low-vegetation classes (e.g., corn + sorghum vs. other crops).  Subsequently, Sentinel-2 imagery is employed to capture unique phenological and spectral features, enabling the generation of high-quality training samples for the fine-scale differentiation of corn and sorghum.

This automated approach was applied to multiple years (2019–2022) and regions (China and the U.S.), assessing its transferability and robustness.  Validation of corn classification achieved an average overall accuracy (OA) of 0.91, with strong correlations to independent labels, published mapping products (R² = 0.98), and official statistics (R² = 0.96).  The current results for corn show that the GGCMF method is not only highly accurate but also robust across different temporal and spatial scales. The integration of GEDI and Sentinel-2 data within GEE offers a cost-effective and scalable solution for mapping structurally distinct crops.  By leveraging GEDI's canopy height data for automatic labeling and combining it with Sentinel-2's high-resolution imagery, GGCMF presents a novel, automated workflow for crop mapping.  This approach has significant potential for large-scale agricultural monitoring, providing timely and reliable data to support sustainable agricultural management.