Highlights What are the main findings? By combining remote sensing imaging principles with machine learning techniques, we produced 30 m terrace maps (1990-2020) for the Loess Platea, revealing significant spatiotemporal variations in terrace expansion. We quantified the sediment reduction resulting from terrace construction, revealing an average 49.75% decrease in soil erosion across the Loess Plateau. What are the implications of the main findings? This study provides a robust framework for long-term monitoring of terrace dynamics, thereby offering a scientific basis for precision terrace management and sustainable land-use planning on the Loess Plateau. This study demonstrates the critical role of terrace engineering in soil and water conservation, providing quantitative evidence to support the optimization of erosion control and agricultural productivity strategies on the Loess Plateau.Highlights What are the main findings? By combining remote sensing imaging principles with machine learning techniques, we produced 30 m terrace maps (1990-2020) for the Loess Platea, revealing significant spatiotemporal variations in terrace expansion. We quantified the sediment reduction resulting from terrace construction, revealing an average 49.75% decrease in soil erosion across the Loess Plateau. What are the implications of the main findings? This study provides a robust framework for long-term monitoring of terrace dynamics, thereby offering a scientific basis for precision terrace management and sustainable land-use planning on the Loess Plateau. This study demonstrates the critical role of terrace engineering in soil and water conservation, providing quantitative evidence to support the optimization of erosion control and agricultural productivity strategies on the Loess Plateau.Abstract Terraces are the main engineering of soil erosion control on the Loess Plateau, offering measures for sediment reduction and water conservation, as well as the potential for increasing agricultural productivity. Over the years, large-scale terrace construction has been undertaken; however, the management has been inadequate, especially in terms of long-term monitoring and mapping. Moreover, the sediment reduction effect of terrace construction is not yet fully understood. Therefore, this study utilizes Landsat series data, integrating remote sensing imaging principles with machine learning techniques to achieve long-term temporal sequence mapping of terraces at a 30 m spatial resolution on the Loess Plateau. The sediment reduction effect brought about by terrace construction on the Loess Plateau is quantified using a sediment reduction formula. The results show that Elevation (Ele.), red band (R), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-infrared Reflectance of Vegetation (NIRv) are key parameters for remote sensing identification of terraces. These five remote sensing variables explain 88% of the terrace recognition variance. Coupling the Random Forest classification model with the LandTrendr algorithm allows for rapid time-series mapping of terrace spatial distribution characteristics on the Loess Plateau. The producer's accuracy of terrace identification is 93.49%, the user's accuracy is 93.81%, the overall accuracy is 88.61%, and the Kappa coefficient is 0.87. The LandTrendr algorithm effectively removes terraces affected by human activities. Terraces are mainly distributed in the southeastern Loess areas, including provinces such as Gansu, Shaanxi, and Ningxia. Over the past 30 years, the terrace area on the Loess Plateau has increased from 0.9790 million hectares in 1990 to 9.8981 million hectares in 2020. The sediment reduction effect is particularly notable, with an average reduction of 49.75% in soil erosion across the region. This indicates that terraces are a key measure for soil erosion control in the region and a critical strategy for improving farmland productivity. The data from this study provides scientific evidence for soil erosion control on the Loess Plateau and enhances the precision of terrace management.