@article{Li2026, 
author = {Jianlong Li and Kai Tang and Zeteng Li and Dameng Yin and Laigang Wang and Cong Wang and Xuehong Chen and Jin Chen},
title = {Early-season estimation of winter wheat sowing date: Integration of dynamic climate windows and phenological indicators into machine learning models},
year = {2026},
journal = {The Crop Journal},
volume = {14},
number = {3},
pages = {1039-1050},
keywords = {Sowing date, Phenology, Winter wheat, Machine learning, Early-season, Sentinel-2},
url = {https://www.sciopen.com/article/10.1016/j.cj.2026.03.002},
doi = {10.1016/j.cj.2026.03.002},
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 R2 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.}
}