Machine Learning-Based Mapping of Irrigated Farmland Dynamics in the Lower Yellow River Basin

Accurate, high-resolution irrigation-related spatial information is paramount to diverse applications, including water resources management, food security, and agricultural planning. To address this need, our study leveraged machine learning algorithms and integrated multi-source data to extract and analyze land use types and spatiotemporal dynamics of irrigated farmland across provinces in the lower reaches of the Yellow River Basin over the 2008–2022 period. The results indicate that cultivated land remained dominant and largely stable, although localized losses occurred in peri-urban areas due to urban expansion. Construction land increased significantly, particularly in Shandong where it expanded by more than 15%, while forest and grassland areas grew under national ecological programs. The Random Forest (RF) algorithm achieved robust performance in identifying irrigated farmland, with overall accuracy exceeding 85% and regression with statistical irrigation data yielding R2 values above 0.9 over the past 15 years at the city level. Spatiotemporal analysis showed strong variability in Henan, with irrigated area declining by 8–12% during drought years and recovering in wetter years, while Shandong experienced relative stability but a gradual 5% decline since 2015, driven by groundwater depletion and stricter regulation. The findings suggest irrigation expansion has reached near-saturation, given stable cultivated land and continuous improvements in water use efficiency. Future strategies should prioritize water use efficiency, water saving technologies, and equitable allocation to ensure sustainable agricultural development.