Map of irrigated cropland in northern China at 30m resolution from 1990 to 2020

High-resolution irrigated cropland maps are essential for optimizing agricultural productivity and managing freshwater resources. China has the world’s largest irrigated cropland area, but rapid irrigation expansion in recent decades has exacerbated regional water stress. We develops an automated machine learning framework to map irrigated croplands over the past four decades at 30 m resolution across northern China, a region that accounts for more than 70% of China’s irrigated cropland and is highly susceptible to water shortages. The main contributions of our framework include: (1) delineating irrigated areas by analyzing the time-series characteristics of irrigated crops by constructing a composite irrigation performance index based on greenness, humidity, and temperature, and refining the sample pool with two specific irrigated crop types; (2) incorporating synthetic phenological features and a 30 m surface temperature dataset to capture the cooling effect of irrigation; (3) leveraging 98,697 Landsat scenes to train 673 localized random forest models per time period to enhance classification accuracy across diverse regions. Validation results indicate that the overall accuracy of the generated irrigated cropland maps ranges from 0.78 to 0.85. Produced maps show strong agreement with statistical data and outperform three existing irrigation products. The 30m spatial resolution map of irrigated cropland also captures the loss of irrigated cropland around the urban, highlighting the increased competition for regional water resources. By minimizing reliance on statistical data and integrating irrigation-induced cooling effects and phenological variations, our approach improves mapping accuracy and provides a valuable resource for regional irrigation planning and water resource management.Coordinate system EPSG:4326Value 0: non-cropland; Value 1: non-irrigated; Value 2: irrigated