Detecting forest tree species distribution using satellite imagery with deep-learning models is essential for effective forest management. While sufficient training samples are crucial for developing deep-learning-based tree species classification models, creating these training samples requires significant resources. Therefore, understanding the optimal balance between model accuracy and training sample size is essential for efficient resource allocation. Here, we determined the optimal training sample size for forest tree species classification using Sentinel-2 imagery and the U-Net model. The study area comprised the Seoul–Gyeonggi region of South Korea, where the nine dominant tree species were selected for classification. We utilized multi-temporal Sentinel-2 imagery, incorporating spectral, vegetation, and textural features. Optimal points were identified using Locally Estimated Scatterplot Smoothing (LOESS) regression. The maximum overall accuracy reached 61%, with 90% and 95% of the maximum accuracy with training sample sizes of 2.37%–2.67% and 4.42%–5.89%, respectively. The congeneric Pinus and Quercus groups had major confusion, with species-specific F1-scores ranging from 0.40 (Robinia pseudoacacia) to 0.75 (Pinus koraiensis). These results provide practical guidelines for efficient resource allocation in tree species classification. Rather than pursuing excessive data collection beyond the optimal point, integrating multiple sensor types can overcome existing limitations and enhance classification accuracy.

Forest type maps require periodic revision, yet field inspection of every stand is impractical. This study tested whether stand-level disagreement between a Sentinel-2 U-Net classifier and the Republic of Korea’s forest type map could support screening for revision. A seven-class U-Net was trained using seasonal Sentinel-2 composites from 2025, and pixel predictions were aggregated to forest stands. Disagreeing stands passing dominance and confidence thresholds were flagged and assessed by blinded interpreters using high-resolution basemaps supplemented by Sentinel-2 composites. Stand-level agreement with the map was 0.69 by stand count and 0.76 when area-weighted. In the primary analysis, high-precision-class flags were mismatched in 41.3% of interpreted stands (29.7% when population-weighted), compared with 7.7% of controls. Test-set class precision, the pre-specified prioritization criterion, did not distinguish more reliable flags in this analysis. An exploratory partition by flag type yielded weighted rates of corresponding mismatches of 80.9% for stocking-status flags and 26.0% for species-dominance flags, representing 12.3- and 2.4-fold enrichment over their respective region-wide baselines. Approximately 35% of flagged stands were estimated to be mismatches, and the flags captured approximately 7.5% of estimated mismatches. These findings support model–map disagreement as a potential tool for prioritizing stand verification in the study region.