A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale
Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and i...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/138723 |
| _version_ | 1855525605901074432 |
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| author | Owusu, Afua Kagone, S. Leh, Mansoor Velpuri, Naga Manohar Gumma, Murali K. Ghansah, Benjamin Thilina-Prabhath, Paranamana Akpoti, Komlavi Mekonnen, Kirubel Tinonetsana, Primrose Mohammed, I. |
| author_browse | Akpoti, Komlavi Ghansah, Benjamin Gumma, Murali K. Kagone, S. Leh, Mansoor Mekonnen, Kirubel Mohammed, I. Owusu, Afua Thilina-Prabhath, Paranamana Tinonetsana, Primrose Velpuri, Naga Manohar |
| author_facet | Owusu, Afua Kagone, S. Leh, Mansoor Velpuri, Naga Manohar Gumma, Murali K. Ghansah, Benjamin Thilina-Prabhath, Paranamana Akpoti, Komlavi Mekonnen, Kirubel Tinonetsana, Primrose Mohammed, I. |
| author_sort | Owusu, Afua |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2 ), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes. |
| format | Journal Article |
| id | CGSpace138723 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1387232025-12-08T09:54:28Z A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale Owusu, Afua Kagone, S. Leh, Mansoor Velpuri, Naga Manohar Gumma, Murali K. Ghansah, Benjamin Thilina-Prabhath, Paranamana Akpoti, Komlavi Mekonnen, Kirubel Tinonetsana, Primrose Mohammed, I. farmland remote sensing irrigated farming rainfed farming frameworks agricultural water management land use land cover models datasets Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2 ), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes. 2024-02 2024-01-31T14:06:19Z 2024-01-31T14:06:19Z Journal Article https://hdl.handle.net/10568/138723 en Open Access Elsevier Owusu, Afua; Kagone, S.; Leh, Mansoor; Velpuri, Naga Manohar; Gumma, M. K.; Ghansah, Benjamin; Thilina-Prabhath, Paranamana; Akpoti, Komlavi; Mekonnen, Kirubel; Tinonetsana, Primrose; Mohammed, I. 2024. A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale. International Journal of Applied Earth Observation and Geoinformation, 126:103607. [doi: https://doi.org/10.1016/j.jag.2023.103607] |
| spellingShingle | farmland remote sensing irrigated farming rainfed farming frameworks agricultural water management land use land cover models datasets Owusu, Afua Kagone, S. Leh, Mansoor Velpuri, Naga Manohar Gumma, Murali K. Ghansah, Benjamin Thilina-Prabhath, Paranamana Akpoti, Komlavi Mekonnen, Kirubel Tinonetsana, Primrose Mohammed, I. A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title | A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title_full | A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title_fullStr | A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title_full_unstemmed | A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title_short | A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale |
| title_sort | framework for disaggregating remote sensing cropland into rainfed and irrigated classes at continental scale |
| topic | farmland remote sensing irrigated farming rainfed farming frameworks agricultural water management land use land cover models datasets |
| url | https://hdl.handle.net/10568/138723 |
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