Crop classification map of China's Hetao Plain from 2000 to 2024

Using the Landsat Level 2 surface reflectance data in the Google Earth Engine (GEE) platform, we generated 30-meter resolution crop classification maps (wheat, maize, sunflower, and melon-vegetables) for China's Hetao Plain from 2000 to 2024. We used a phenology-assisted supervised remote sensing method for crop classification. Initial training samples are extracted using NDVI time-series decision rules, followed by spatial filtering to optimize sample quality. A random forest model is then applied to achieve high-accuracy crop classification. Validation based on field survey points shows an overall accuracy exceeding 90% and a Kappa coefficient greater than 0.88, confirming the effectiveness and reliability of the approach.