The seedling condition of winter wheat before overwintering affects its safe overwintering and final yield, and leaf age is an important indicator for evaluating the seedling condition. However, traditional leaf age surveys primarily rely on manual observation, which is not only time-consuming and labor-intensive but also makes large-scale and simultaneous observations difficult. Existing methods based on image segmentation are constrained by issues such as unclear leaf boundaries, leaf overlapping, and high computational complexity, limiting their large-scale application.
Against this backdrop, a research team led by Dr. Zhenhai Li from Shandong University of Science and Technology has proposed a method for monitoring winter wheat leaf age that integrates remote sensing phenological information, temporal variations in vegetation indices, and GDD (Fig. 1). Their study, made available online on May 28, 2026 in The Crop Journal , integrates remote sensing and meteorological data, providing new technical support for the monitoring and precise management of winter wheat seedling condition on a regional scale and laying the foundation for cross-regional application and long-term monitoring.
Based on MODIS remote sensing data and ERA5-Land reanalysis temperature data, the research team first extracted the emergence date of winter wheat and then used the emergence date, GDD, and NDVI change rate (β) to construct a physiologically-based GDD leaf age model (LA GDD ) and a random forest-based leaf age model (LA RF ) to estimate the leaf age of winter wheat before overwintering.
To evaluate model performance, the researchers used field-measured leaf age samples for validation and found that the LA RF model, which integrates multi-source data, significantly improved the accuracy of leaf age monitoring. It achieved an R² of 0.67 and an RMSE of 0.56 leaves, which was significantly better than the LA GDD model that relied solely on GDD. “The model demonstrated good adaptability and stability in major winter wheat planting areas of Shandong Province, providing a new technical solution for regional-scale winter wheat seedling monitoring,” says corresponding author Dr. Zhenhai Li.
The researchers also found that the emergence date is a key factor affecting the leaf age of winter wheat before overwintering. Winter wheat that emerges earlier can accumulate more GDD before overwintering, thereby achieving a higher leaf age and a better seedling condition. “This phenomenon was particularly evident in 2021. The continuous rainfall in Shandong Province that year led to widespread delays in sowing and emergence, ultimately resulting in a generally lower leaf age across the province,” explains Li. “This further illustrates the important role of sowing and emergence dates in the development of seedling condition.”
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Contact Author:
Zhenhai Li
E-mail address: lizh323@126.com
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The Crop Journal
Computational simulation/modeling
Not applicable
Monitoring of overwintering leaf age by integrating phenological, temporal, and thermal data: An indicator for assessing winter wheat seedling condition
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.