Irrigation is critical for intensifying and expanding agriculture in sub-Saharan Africa (SSA).Policymakers increasingly use remote sensing-based techniques to identify previously unknown irrigated areas. As smallholder irrigation practices in SSA vary widely depending on the type of crops, plot sizes, irrigation methods and landscapes, how maps are made that depict their extent becomes more important. We have identified methodological choices in at least eight essential domains for classification or irrigated agriculture sampling design, labelling protocol sets of classes, field data collection, predictor variables, algorithm adequacy, input variables, accuracy assessment, map seasonality, and code and data sharing.This study demonstrates and systematises how these choices affect classification in a reporting framework. We found that none of the reviewed articles sufficiently documented all classification steps when applying the framework. Although the reasons for not reporting are unknown, the lack of explicitly made choices hampers a proper evaluation of irrigated agriculture's extent, particularly smallholder irrigation. Ultimately, this may reinforce the impression that smallholder irrigation is irrelevant because it does not appear on maps.Finally, we conclude that sharing extensively documented irrigation mapping methodologies promotes the adoption of best practices across different regions or countries. Policymakers and practitioners can learn from successful experiences and avoid repeating mistakes made in other contexts. This approach advances irrigation practices worldwide by fostering collaboration and knowledge exchange.