{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T10:35:39Z","timestamp":1773916539771,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000193","name":"International Development Research Centre (IDRS)","doi-asserted-by":"publisher","award":["109039"],"award-info":[{"award-number":["109039"]}],"id":[{"id":"10.13039\/501100000193","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Resilience BV","award":["109039"],"award-info":[{"award-number":["109039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques is challenging due to its small and scattered areas and heterogenous cropping practices. A study was conducted to examine the impact of sample size and composition on the accuracy of classifying irrigated agriculture in Mozambique\u2019s Manica and Gaza provinces using three algorithms: random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Four scenarios were considered, and the results showed that smaller datasets can achieve high and sufficient accuracies, regardless of their composition. However, the user and producer accuracies of irrigated agriculture do increase when the algorithms are trained with larger datasets. The study also found that the composition of the training data is important, with too few or too many samples of the \u201cirrigated agriculture\u201d class decreasing overall accuracy. The algorithms\u2019 robustness depends on the training data\u2019s composition, with RF and SVM showing less decrease and spread in accuracies than ANN. The study concludes that the training data size and composition are more important for classification than the algorithms used. RF and SVM are more suitable for the task as they are more robust or less sensitive to outliers than the ANN. Overall, the study provides valuable insights into mapping smallholder irrigated agriculture in sub-Saharan Africa using remote sensing techniques.<\/jats:p>","DOI":"10.3390\/rs15123017","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T03:30:03Z","timestamp":1686281403000},"page":"3017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evaluating the Effect of Training Data Size and Composition on the Accuracy of Smallholder Irrigated Agriculture Mapping in Mozambique Using Remote Sensing and Machine Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0815-9932","authenticated-orcid":false,"given":"Timon","family":"Weitkamp","sequence":"first","affiliation":[{"name":"Resilience BV, 6703 AA Wageningen, The Netherlands"},{"name":"Water Resource Management (WRM) Department, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5819-0981","authenticated-orcid":false,"given":"Poolad","family":"Karimi","sequence":"additional","affiliation":[{"name":"IHE Delft, 2611 AX Delft, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Foody, G., Pal, M., Rocchini, D., Garzon-Lopez, C., and Bastin, L. 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