Accurate identification of irrigated farmland is crucial for optimizing water resource allocation

improving irrigation efficiency

monitoring agricultural drought

guiding hydraulic engineering operations

and promoting food security and sustainable water use. This study focuses on the entire region of Uxin Banner

Inner Mongolia

an arid–semiarid agro-pastoral ecotone. Differences between irrigated and non-irrigated farmland are compared and examined from four dimensions－moisture dynamics

energy dynamics

vegetation physiology

and vegetation phenology－leading to 10 key factors selected across four categories. We retrieve these factors from multi-source remote sensing data

and construct a comprehensive feature index library comprising 43 indicators. Then

we select the optimal feature subset through feature-importance evaluation

and use it to train a Random Forest (RF) model for irrigated farmland identification. Application to the 2019-2024 Uxin Banner data for analysis of the spatiotemporal variations shows that the model achieves an overall accuracy of 95.38% and a Kappa coefficient of 0.9128

demonstrating its strong capability of irrigated farmland extraction. In Uxin Banner

the irrigated area was 812.2733 km2 in 2024

mainly distributed in its southern and southwestern regions. Over the five years of 2019-2024

the irrigated area increased first and then decline with a turning point occurring in 2022. The new framework in this study—mechanism analysis

factor identification

remote sensing inversion

and model identification—would help identify irrigated farmland and promote interpretable machine learning applications.