Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China

• Attained 87.61% accuracy in mapping abandoned cropland at 30-m resolution. • Multi-crop and phenology-based semi-automated method enhances sample collection. • A novel temporal segmentation method was developed for cropland transforming. • Rainfed cropland in arid regions is most prone to abandonment. • Slope more significantly affects cropland abandonment in semi-arid zones. At the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This research evaluates the effectiveness of an innovative remote sensing method for producing 30-meter-resolution long-term maps of abandoned and reclaimed croplands in Inner Mongolia, China, using a temporal segmentation approach developed with Google Earth Engine. The method integrates ground sample collection of major crops and inactive cropland with Normalized Difference Vegetation Index (NDVI) analysis during key growth stages, enabling precise classification of cultivation status. By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. The Temporal Segmentation of Abandoned and Reclaimed Cropland (TSARC) method created time series maps of abandoned and reclaimed cropland at a 30-meter resolution, achieving an overall accuracy of 87.61%. The proposed remote sensing methodology reveals spatiotemporal trends in abandonment rates across arid and semi-humid regions, offering valuable insights for agricultural and environmental management in Inner Mongolia. Considering regional climatic, hydrological, and phenological conditions improves sample collection efficiency and cropland status monitoring. While designed for northern China, this method is also applicable to other single-season agricultural regions for varied agricultural land use monitoring.