Owusu, A and Kagone, S and Leh, M and Velpuri, N M and Gumma, M K and Ghansah, B and Thilina-Prabhath, P and Akpoti, K and Mekonnen, K and Tinonetsana, P and Mohammed, I (2023) A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale. International Journal of Applied Earth Observation and Geoinformation, 126. pp. 1-15. ISSN 1569-8432
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Abstract
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.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Resilient Farm and Food Systems |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Cropland partitioning, Budyko model, Irrigated cropland, Rainfed cropland, Accuracy assessment |
| Subjects: | Others > Remote Sensing |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 18 Jan 2024 04:00 |
| Last Modified: | 18 Jan 2024 04:00 |
| URI: | http://oar.icrisat.org/id/eprint/12380 |
| Official URL: | https://doi.org/10.1016/j.jag.2023.103607 |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | The authors would like to acknowledge the land use/landcover dataset providers and developers for producing and making their datasets free of charge. This study is part of the International Water Management Institute’s Digital Innovations for Water Secure Africa (DIWASA) project. We gratefully acknowledge The Leona M. and Harry B. Helmsley charitable trust for their financial support. The author’s views are their own and do not necessarily reflect the views of the funding agency but do represent the views of the U.S. Geological Survey. We thank two anonymous reviewers and the editor for providing valuable feedback to improve the quality of this publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the authors or the U.S. Government. |
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