Work

Early-season estimation of winter wheat sowing date: Integration of dynamic climate windows and phenological indicators into machine learning models

Year: 2026

Type: article

Abstract: Climate change and the drawbacks of traditional monitoring techniques pose challenges to efficient crop phenology management, making accurate winter wheat sowing date estimation crucial for agricultural optimization. We present a machine learning framework for estimation of winter wheat sowing dates using high-resolution early-season remote sensing. It uses the Normalized Difference Greenness Index (NDGI) from Sentinel-2 data to detect crop emergence. A dynamic climate window extracts pre- and post-emergence environmental variables, and machine learning models estimate sowing dates. Evaluated for Henan province, China, during the 2024 growing season, the framework achieved an R 2 of 0.82, supporting high-resolution spatial mapping. This approach provides a reliable and scalable tool for large-scale sowing date monitoring, supporting climate-resilient agricultural management and data-driven farming decisions.

Source: The Crop Journal

DOI: 10.1016/j.cj.2026.03.002