What question did this study set out to answer?

This research aims to monitor the leaf age of winter wheat using phenological, temporal, and thermal data, enhancing crop management.

May 31, 2026Open Access

Monitoring of overwintering leaf age by integrating phenological, temporal, and thermal data: An indicator for assessing winter wheat seedling condition

Puntos clave

This research aims to monitor the leaf age of winter wheat using phenological, temporal, and thermal data, enhancing crop management.
Utilized MOD09Q1 images to reconstruct NDVI time series using cubic spline interpolation and Savitzky-Golay filter.
Applied dynamic threshold method to extract winter wheat emergence date and compute GDD during the growing period.
Developed GDD-based and Random Forest-based models for monitoring leaf age during the overwintering stage.
Dynamic threshold method provided high accuracy for extracting emergence date (bias = 0.47 days, R² = 0.77, RMSE = 3.93 days).
LA RF model outperformed LA GDD model with improved monitoring accuracy (training set R² = 0.85, RMSE = 0.39 leaves; validation set R² = 0.67, RMSE = 0.56 leaves).
Leaf age distribution indicates stability in southern and northern Shandong regions during pre-wintering, with lower ages on the Jiaodong Peninsula but positive seedling development.

Resumen

Leaf age is one of the important parameters for describing crop growth stages. In actual production, cultivation methods can be adjusted in a timely manner according to leaf age, which can promote rapid seedling transformation and lay a solid foundation for achieving high yields of winter wheat. However, most existing image segmentation methods focus on leaf recognition and lack consideration for the impact of growing degree days (GDD) on winter wheat growth. This study takes Shandong province as the research area. Firstly, based on MOD09Q1 images, the normalized difference vegetation index (NDVI) time series was reconstructed using cubic spline interpolation and Savitzky-Golay (S-G) filter. Secondly, the dynamic threshold method was used to extract the winter wheat emergence date, the NDVI slope (β) was calculated from the emergence date to the monitoring date, and the GDD during this period was calculated based on ERA5-Land reanalysis temperature data. Finally, a GDD-based leaf age (LA GDD ) model and a Random Forest-based leaf age (LA RF ) model were constructed separately to monitor winter wheat leaf age at the start of the overwintering stage. The research results indicate that compared with the phenological observation data in Shandong province, the winter wheat emergence date extracted using the dynamic threshold method has high accuracy (bias = 0.47 days (d), R 2 = 0.77, and RMSE = 3.93 d). Compared with the LA GDD model ( R 2 = 0.59, RMSE = 0.68 leaves), the LA RF model significantly improves leaf age monitoring accuracy and performs well in both the training and validation sets, showing strong correlation with measured data (training set: R 2 = 0.85, RMSE = 0.39 leaves, validation set: R 2 = 0.67, RMSE = 0.56 leaves). In terms of spatial distribution, the leaf age in the southern and northern regions of Shandong province is relatively stable during the pre-wintering growth stage, with an overall higher leaf age of over 5 leaves in most years. However, the Jiaodong Peninsula has relatively lower leaf ages, but the seedlings are also developing well. In terms of temporal distribution, the leaf age is generally lower in 2021, while the leaf age in other years is moderate, and the region has favorable seedling conditions. This study integrates remote sensing phenological information, vegetation dynamic parameters, and meteorological factors to monitor winter wheat leaf age on a regional scale. The proposed model demonstrates high accuracy and stability, providing scientific support for seedling assessment and precise field management at the pre-overwintering stage.

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