Tehran University of Medical Sciences Journal of Arthropod-Borne Diseases 2322-1984 20 2 2026 08 15 1949 1949 Gholmreza Hassanpour Center for Research of Endemic Parasites of Iran, Tehran University of Medical Sciences, Tehran, Iran Fahimeh Youssefi Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran Awat Dehghan Department of Vector Biology and Control of Diseases, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Hossein Keshavarz Center for Research of Endemic Parasites of Iran, Tehran University of Medical Sciences, Tehran, Iran, Department of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Ahmad Ali Hanafi-Bojd Department of Vector Biology and Control of Diseases, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran, Zoonoses Research Center, Tehran University of Medical Sciences, Tehran, Iran 2026 05 11 2026 08 09 Background: Reliable mapping of malaria occurrence requires validation across environmentally distinct years and care­ful separation of model-derived probability from transmission intensity. This study developed a multi-sensor remote sensing and machine-learning framework for mapping annual malaria case occurrence in southeastern Iran. Methods: Annual median composites of Soil Water Index (SWI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were generated from Sentinel-1, Sentinel-2 and Landsat 8/9 at 30m resolution. After quality control, 82, 168, 775 and 483 malaria case locations were retained for the Solar Hijri years 1400–1403 (2021–2025). Five spatially balanced pseudo-absence realizations were generated annually at a 1:1 ratio with a 300m exclu­sion buffer. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were evaluated through complete four-fold leave-one-year-out validation with nested tuning. Performance, bootstrap confidence intervals, uncertainty, Getis–Ord Gi* clusters and SHAP (SHapley Additive exPlanations) were assessed. Results: Across 20 outer evaluations, RF achieved ROC-AUC 0.685±0.045, PR-AUC 0.693±0.040, balanced accuracy 0.624±0.026 and Brier score 0.230±0.015. XGBoost achieved ROC-AUC 0.687±0.044, PR-AUC 0.691±0.043, bal­anced accuracy 0.625±0.033 and Brier score 0.231±0.015. Year-specific ROC-AUC ranged from 0.624 to 0.726. LST was the dominant predictor (48.27%), followed by NDVI (37.04%) and SWI (14.69%). Only 3.46–6.10% of pixels ex­ceeded probability 0.70. Mean uncertainty was low (0.030–0.032) but spatially heterogeneous. Conclusion: Both algorithms showed moderate, comparable performance with year-dependent variation. Maps provide relative probabilities for prioritizing field surveillance, not transmission estimates. https://jad.tums.ac.ir/index.php/jad/article/view/1949 https://jad.tums.ac.ir/index.php/jad/article/download/1949/736