Monitoring of Winter Wheat Leaf Age Based on Remote Sensing and ECMWF Reanalysis Data

Leaf age is one of the important parameters describing the growth stage of crops. In actual production, timely adjustment of cultivation methods based on leaf age can promote rapid transformation of seedling conditions, laying a solid foundation for achieving high yield of winter wheat. This study takes Qingdao, Binzhou, and Heze in Shandong Province as the research areas. The Normalized Difference Vegetation Index (NDVI) time series data and the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis temperature data from 2019 to 2023 were used to construct a leaf age-accumulated temperature relationship model for winter wheat leaf age monitoring based on its physiological characteristics. The results showed that compared with the observation data, the coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of winter wheat sowing date from 2019 to 2023 was 0.73, the bias was –2.46 days (d), and the root mean square error (RMSE) was 4.74 d. The sowing date shows a trend of gradually advancing with increase of latitude. According to the leaf age-accumulated temperature relationship model, the accuracy of monitoring leaf age was R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.75, bias = 0.03 leaves, and RMSE = 0.26 leaves. The overall seedling situation was well from 2019 to 2023. Due to the impact of precipitation in 2021, the delayed harvest of summer maize led to the late sowing of winter wheat, resulting in weaker seedling conditions. The results of this study further validate the feasibility of the leaf age-accumulated temperature relationship model in monitoring the leaf age of winter wheat at a large regional scale.