<b>A 10-meter annual cropland activity map and dataset of abandonment and reclaimed cropland</b>

Abstract

Amid growing global food security concerns and frequent armed conflicts, real-time monitoring of abandoned cropland is essential for strategic planning and crisis management. This study develops a method to map abandoned cropland accurately, crucial for maintaining the food supply chain and ecological balance. Utilizing Sentinel-1/2 satellite data, we employed multi-feature stacking and machine learning to create a dataset tracking annual cropland activity. A novel temporal segmentation algorithm was developed to annually extract cropland abandonment and reclamation patterns, using sliding time windows over several years. This research differentiates cropland states—active cultivation, unstable fallowing, continuous abandonment, and reclamation—providing continuous, regional-scale maps with 10-meter resolution. The dataset supports land planning, environmental monitoring, agricultural economics, and food security assessments, along with social science research, decision-making, and advancing technology in land use tracking and real-time monitoring.</p