Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies - Jayamini, Kalpani; Alahacoon, Niranga; Amarnath, Giriraj (International Water Management Institute, 2025)
The Training Guide presents a practical, step-by-step approach for high-resolution paddy mapping using open-source Earth Observation (EO) data and geospatial technologies. Targeted at technical officers, researchers, and analysts, the guide demonstrates how different platforms such as Google Earth Engine, Google Colab, QGIS, and Python can deliver reliable rice extent maps and seasonal monitoring using Sentinel-1 Synthetic Aperture Radar (SAR). The guide details the full workflow: creating and linking a GEE Cloud Project, authenticating service accounts in Colab, preprocessing time-series SAR data, extracting indices (e.g., mRVI), treating outliers, classifying start-peak-harvest stages, and performing validation and accuracy assessments with ground observations. Practical chapters explain module dependencies, asset uploads, and reproducible notebook execution, while the Rice Mapping Dashboard section describes an interactive Streamlit tool for time-series analysis, outlier detection, seasonal mapping, and monitoring. Hands-on examples and a downloadable notebook help users run analyses, visualize outputs, and save work to Google Drive. By translating satellite signals into actionable agricultural intelligence, the guide empowers institutions to improve seasonal planning, yield estimation, hazard assessment, and evidence-based decision-making.