NDCI-mGMM: a novel and automated model for dynamic maize mapping during the growing season

Maize, as one of the most widely cultivated crops worldwide, is crucial for food security and livelihoods. Accurate dynamic maize mapping is essential for production forecasting and preharvest decision-making. However, current approaches remain limited by incomplete seasonal representation and poor model transferability across growth stages, highlighting the need for an automated and dynamic maize mapping method throughout the growing season. In this study, we first explored the spectral bands that maximally differentiate maize from other crops in terms of water content, chlorophyll levels, and canopy leaf structure during the growing season and proposed a novel normalized difference composite index (NDCI). Based on this index, an automated and dynamic maize identification method that does not rely on crop labels was constructed using a multitemporal Gaussian Mixture Model (GMM), referred to as NDCI-mGMM. The framework was evaluated in five representative maize-growing regions across China, the United States, and France, and further validated in 2021 at two United States sites (Iowa and Georgia) to assess its temporal transferability. Across these regions and years, NDCI-mGMM achieved overall accuracies of approximately 85%, demonstrating stable performance under varying phenological and environmental conditions. Compared with commonly used vegetation indices (Datt99, REP, LSWI, and CIgreen), the NDCI achieved higher F1-scores (by 4–49%) and improved maize separability. Moreover, NDCI-mGMM enabled early-season maize mapping during the tasseling stage—up to two months before harvest—with satisfactory accuracy (F1 ≥ 79%). As the method operates independently of crop labels, it provides a scalable and temporally transferable framework for in-season maize mapping and early-season area estimation in data-limited regions, thereby supporting timely crop monitoring for production forecasting and preharvest decision-making.