Annual winter wheat mapping dataset in China from 2001 to 2020

Area of Science:

• Agricultural Science
• Remote Sensing
• Geospatial Analysis

Background:

• Winter wheat is a global staple, with China producing 19% of the world's supply.
• Accurate, long-term winter wheat distribution data is vital for food security and spatiotemporal pattern analysis.
• Existing remote sensing maps lack the required spatial resolution, temporal coverage, and scale for China.

Purpose of the Study:

• To develop and validate a high-resolution, long-term winter wheat distribution dataset for China.
• To address the gap in available remote sensing data for winter wheat cultivation.
• To support national food security initiatives and agricultural research.

Main Methods:

• Developed a phenology-based algorithm integrating three key winter wheat growing features.
• Calculated planting probability using the integrated variable.
• Utilized a fusion dataset to produce winter wheat maps at 30-m resolution from 2001 to 2020.

Main Results:

• Generated a long-term (2001-2020) winter wheat map dataset for China at 30-m resolution.
• Achieved high accuracy with user's (91.17%), producer's (90.92%), and overall (91.6%) accuracies based on field samples.
• Demonstrated good correlation between mapped winter wheat areas and agricultural statistical data at municipal and county levels.

Conclusions:

• The phenology-based algorithm effectively identifies winter wheat locations with high accuracy.
• The produced dataset provides a valuable resource for understanding winter wheat cultivation in China.
• The high-resolution, long-term maps are critical for food security assessment and agricultural monitoring.