Abstract
Irrigated area mapping with improved accuracy is essential to enhance the monitoring and management of the limited water resources of a country. This study compared machine learning algorithms, classification and regression trees (CART), gradient tree boost (GTB), random forest (RF), and support vector machine (SVM) on the Google Earth Engine platform. The study used Sentinel-2 multispectral and Sentinel-1 synthetic aperture radar satellite data to classify smallholder irrigated areas during the 2021/22 irrigation season. The methods' accuracy was evaluated relative to the inputs and agroecology. The accuracy has been improved by incorporating monthly SAR and vegetation indices data. RF has been found to be the consistent classifier in different agroecological zones and inputs with overall accuracy (OA) of 0.89, followed by SVM and GTB with OA 0.88 and 0.87 at the watershed level respectively. The lowest OA (0.86) and Kappa coefficient (0.82) values were obtained using the CART algorithm. The normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference red edge (NDRE), and normalized difference water index (NDWI) were found to be important in mapping irrigated areas.
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Data availability
The data that supports the findings of this study is available from the corresponding author upon reasonable request.
Code availability
The codes generated or used can be found at https://github.com/Yilkalg3/Irrigated-Area-Mapping.
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Acknowledgements
The authors would like to thank Africa Center of excellent for water management, Addis Ababa University for the support to conduct this research.
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This study was carried out with the support of the Africa Center of Excellent for Water Management, Addis Ababa University.
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MYG, conceptualized the study, conducted data analyses, and wrote the manuscript; AT, framed the design and participated in the review, editing, and supervision; CAD, conceptualized the study, participated in the review, editing, and supervision; HAT, provided ground truth data and participated in review and editing; and MDA, participated in review and editing. All authors have read and agreed to the published version of the manuscript. MDA, participated in the review and editing.
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Mekonnen, Y.G., Alamirew, T., Chukalla, A.D. et al. Comparison of Google Earth Engine Machine Learning Algorithms for Mapping Smallholder Irrigated Areas in a Mountainous Watershed, Upper Blue Nile Basin, Ethiopia. J Indian Soc Remote Sens (2024). https://doi.org/10.1007/s12524-024-01846-w
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DOI: https://doi.org/10.1007/s12524-024-01846-w