Mapping of Hay-Harvesting Grasslands Using Harmonized Landsat Sentinel-2 Time Series and Deep Learning in Temperate Steppe

Natural hay-harvesting grasslands are essential for sustaining livestock through winters in semi-arid temperate steppes. However, limited historical records and the absence of systematic spatial data hinder regional management. Moreover, although threshold-based and machine learning approaches have demonstrated effectiveness in monitoring mowing in Western Europe, their applicability in Eurasian semi-arid steppes remains uncertain. Therefore, we aimed to develop an approach for mapping natural hay-harvesting grasslands in semi-arid temperate steppes. Specifically, based on optical satellite time-series data, we employed a time-series classification architecture, light inception with boosting technique (LITE), to identify hay-harvesting grasslands. Results of our experiments, conducted in a typical region in Inner Mongolia, demonstrated that our approach can generate high-quality hay-harvesting grassland maps, with the testing accuracy of 92.06% ( ${F}1$ -score) and 90.65% [Overall accuracy (OA)]. Furthermore, we applied the gradient-weighted class activation mapping (Grad-CAM) technique to interpret the decision-making process of the deep learning model. Our findings showed that LITE concentrated intensively on time steps surrounding mowing dates, underscoring its superiority in mapping the geospatial extent of hay-harvesting grasslands and its potential for extracting temporal information related to hay harvesting. This study offers valuable insights for developing fine-scale hay-harvesting grassland inventories in semi-arid temperate steppe, an area long overlooked in existing research, thereby supporting the sustainable management of grassland resources, and promoting the advancement of smart herding practices.