<b>STTM-RS: An Explainable Machine Learning and Cloud-Based Remote Sensing Framework for Monitoring Terrace Degradation and Restoration in Fragile Mountain Landscapes</b>

<br><b>Tools and Platforms Used</b><br>The experimental workflow employed ArcGIS Pro, Google Earth Engine, and Google Colaboratory for data preprocessing, spatial analysis, classification, visualization, and post-analysis. These platforms enabled efficient and scalable processing of multi-source geospatial datasets across broad spatial and temporal extents.<br> <b>Supplementary Materials Statement</b><br>The supplementary materials support the manuscript titled <i>“An Explainable Machine Learning and Cloud-Based Remote Sensing Framework for Monitoring Terrace Degradation and Restoration in Fragile Mountain Landscapes.”</i> They include JavaScript and Python scripts used in cloud-based environments (Google Earth Engine and Google Colaboratory) for key components of the workflow, including advanced data preprocessing, machine learning model development, hyperparameter tuning, SHAP-based interpretability, and figure reproduction. The supplementary files also include the main extended experimental results. Together, these materials support full reproducibility of the proposed Spatiotemporal Terrace Mapping using Remote Sensing (STTM-RS) framework.