Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies
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 E...
| Main Authors: | , , |
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| Format: | Manual |
| Language: | Inglés |
| Published: |
International Water Management Institute
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/179729 |
| _version_ | 1855528262645579776 |
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| author | Jayamini, Kalpani Alahacoon, Niranga Amarnath, Giriraj |
| author_browse | Alahacoon, Niranga Amarnath, Giriraj Jayamini, Kalpani |
| author_facet | Jayamini, Kalpani Alahacoon, Niranga Amarnath, Giriraj |
| author_sort | Jayamini, Kalpani |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Manual |
| id | CGSpace179729 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Water Management Institute |
| publisherStr | International Water Management Institute |
| record_format | dspace |
| spelling | CGSpace1797292026-01-14T02:09:16Z Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies Jayamini, Kalpani Alahacoon, Niranga Amarnath, Giriraj rice spatial data technology monitoring training materials 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. 2025-12-16 2026-01-13T07:46:15Z 2026-01-13T07:46:15Z Manual https://hdl.handle.net/10568/179729 en Open Access application/pdf International Water Management Institute CGIAR Climate Action Program Jayamini, K.; Alahacoon, N.; Amarnath, G. 2025. Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Climate Action Program. 22p. |
| spellingShingle | rice spatial data technology monitoring training materials Jayamini, Kalpani Alahacoon, Niranga Amarnath, Giriraj Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title | Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title_full | Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title_fullStr | Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title_full_unstemmed | Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title_short | Training guide: advancing paddy mapping using open-source earth observation data and geospatial technologies |
| title_sort | training guide advancing paddy mapping using open source earth observation data and geospatial technologies |
| topic | rice spatial data technology monitoring training materials |
| url | https://hdl.handle.net/10568/179729 |
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