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Bare-soil mapping is essential for agricultural monitoring, land-surface characterization, and environmental modelling, supporting applications such as soil organic carbon (SOC) estimation, evapotranspiration retrieval, erosion assessment, and land degradation monitoring. However, accurate detection of exposed soil remains challenging due to spectral confusion with sparse vegetation and crop residues, strong seasonal variability, and the heterogeneous structure of agricultural landscapes. The Harmonized Landsat–Sentinel (HLS) dataset, providing 30 m spatial resolution and a 5-day revisit cycle, offers new opportunities for multi-temporal bare-soil mapping. This study develops an automated workflow combining spectral indices and a machine-learning algorithm (Maximum Entropy, MaxEnt) to map bare soil across agricultural regions using HLS surface reflectance imagery. Multiple indices capturing vegetation–soil contrasts were employed, including NDVI, BSI, NDMI, SAVI/GSAVI, NBR, EVI, and DBSI. High-confidence bare-soil pixels were first identified using a rule-based approach with strict thresholds (e.g., NDVI < 0.2 and BSI > 0.4–0.7), which minimized commission errors and generated reliable presence samples for model calibration. To improve generalization across different day-of-year (DOY) mosaics, these samples were integrated into a presence-background modelling framework using the MaxEnt algorithm (maxnet). Background samples were constrained to non-bare conditions (e.g., NDVI ≥ 0.3). Model performance was evaluated using AUC, Kappa, sensitivity, and specificity, while permutation importance and jackknife analyses quantified predictor contributions. The model achieved an AUC of 0.9 and a Kappa value of 0.7, indicating strong discriminative ability and substantial agreement. NDVI and BSI were identified as the most influential predictors. The resulting products include DOY-specific bare-soil probability maps, binary masks, and aggregated bare-soil frequency maps, providing a robust and scalable framework for long-term agricultural and soil-related applications.