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This study compares five feature selection methods (mRMR, ReliefF, Boruta, RFE, HSIC\u2010Lasso) with three tree\u2010based classifiers (RF, XGBoost, LightGBM) for LULC classification in Kahramankazan, Ankara, T\u00fcrkiye. Combining Sentinel\u20101, Sentinel\u20102, topographic, climatic, and socioeconomic data yielded 240 candidate features. Hyperparameters were tuned with Optuna under spatial cross\u2010validation, and SHAP was used to interpret feature contributions. HSIC\u2010Lasso + LightGBM achieved the highest F1 (0.9655) and OA (0.9657) using only 31 features, 84% fewer than Boruta + RF, which reached a comparable F1 with 196 features. McNemar's test with Holm\u2013Bonferroni correction confirmed equivalence with the best Boruta variants and superiority over the mRMR\u2010 and ReliefF+RF baselines. HSIC\u2010Lasso also completed the pipeline 16.3 times faster than Boruta, supporting compact embedded selection in multi\u2010source LULC workflows.<\/jats:p>","DOI":"10.1111\/tgis.70293","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T07:34:54Z","timestamp":1779521694000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explainable and Optimized Machine Learning Framework for Multisensor Remote Sensing\u2010Based Land Use and Land Cover Classification"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0609-8032","authenticated-orcid":false,"given":"Halil \u0130brahim","family":"G\u00fcnd\u00fcz","sequence":"first","affiliation":[{"name":"Department of Geomatics Engineering, Faculty of Engineering Aksaray University  Aksaray T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1362-8206","authenticated-orcid":false,"given":"Osman","family":"Orhan","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, Faculty of Engineering Mersin University  Mersin T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1050-3088","authenticated-orcid":false,"given":"Seda Nur Gamze","family":"Hamal","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, Faculty of Engineering Mersin University  Mersin T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9458-2261","authenticated-orcid":false,"given":"Semih","family":"Ekercin","sequence":"additional","affiliation":[{"name":"Antalya Bilim University  Antalya T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.191"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/15481603.2019.1650447"},{"key":"e_1_2_9_4_1","doi-asserted-by":"crossref","unstructured":"Akiba T. S.Sano T.Yanase T.Ohta andM.Koyama.2019.\u201cOptuna: A Next\u2010Generation Hyperparameter Optimization Framework.\u201dProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining KDD \u201819 2623\u20132631.https:\/\/doi.org\/10.1145\/3292500.3330701.","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22228750"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21103399"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2021.100515"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/app152111437"},{"key":"e_1_2_9_10_1","unstructured":"Bondarenko M. 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