## 4 Conclusions  
In this study, we evaluated the performance of cloud detection using a deep learning model, focusing on whether TOA reflectance was applied or not. Although our evaluation was relatively straightforward, the results demonstrated that incorporating TOA reflectance--commonly used as an input parameter in conventional cloud mask approaches--significantly enhanced the detection of cloud classes. The main contribution of this research lies in proposing a novel input data configuration that can improve model performance in cloud detection. Through the comprehensive analysis of various dataset combinations, we confirmed that the TOA+NDVI combination generally outperforms other configurations. This combination achieved particularly high accuracy and precision, effectively minimizing false detections. While its recall value was relatively low--resulting in more undetected cases--the highest recorded F1-score highlights that it strikes a strong balance overall. In contrast, the RGBN+NDVI combination exhibited limited performance, underscoring that some data configurations may be inherently constrained by their input characteristics. Class-by-class evaluations further supported these  
Figure 8: The selected images of the cloud masks for each channel combination case (from (**a**–**f**)) and corresponding ground true labels (**g**). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**group1** & **group2** & \multicolumn{2}{c}{**Clear Sky**} & \multicolumn{2}{c}{**Thick Cloud**} & \multicolumn{2}{c}{**Thin Cloud**} & \multicolumn{2}{c}{**Cloud Shadow**} \\  & **MD** & **p-adj** & **MD** & **p-adj** & **MD** & **p-adj** & **MD** & **p-adj** \\ \hline RGB & RGBNNDVI & 0 & 0 & \(-\)0.0327 & 0 & \(-\)0.089 & 0 & 0.0228 & 0 \\ RGB & TOA & 0.008 & 0 & \(-\)0.0812 & 0 & \(-\)0.058 & 0 & 0.0212 & 0 \\ RGB & TOANDVI & 0.004 & 0 & \(-\)0.0861 & 0 & _0.0018_ & _0.58_ & \(-\)0.0148 & 0 \\ RGBN & RGBNNDVI & \(-\)0.01 & 0 & 0.0204 & 0 & _\(-\)0.001_ & _0.9495_ & \(-\)0.0194 & 0 \\ RGBN & TOA & 0.001 & 0 & \(-\)0.0281 & 0 & 0.0294 & 0 & \(-\)0.0209 & 0 \\ RGBN & TOANDVI & 0 & 0 & \(-\)0.033 & 0 & 0.0895 & 0 & \(-\)0.057 & 0 \\ RGBNNDVI & TOA & 0.011 & 0 & \(-\)0.0485 & 0 & 0.0304 & 0 & _\(-\)0.0016_ & _0.858_ \\ RGBNNDVI & TOANDVI & 0.007 & 0 & \(-\)0.0534 & 0 & 0.0905 & 0 & \(-\)0.0376 & 0 \\ TOA & TOANDVI & 0 & 0 & \(-\)0.0049 & 0 & 0.0601 & 0 & \(-\)0.036 & 0 \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._findings. The TOA+NDVI combination delivered superior results for both the dense and thin cloud classes, reflecting the physical properties of clouds more accurately, even when their boundaries were ambiguous. Meanwhile, the NGR combination performed well in detecting cloud shadows, indicating that different channel configurations may better capture distinct atmospheric and environmental nuances. In conclusion, our study identifies the TOA+NDVI combination as a highly effective dataset configuration for cloud detection across a range of classes. Because the effectiveness of each combination depends on the underlying data characteristics and the selected variables, choosing the most suitable configuration for a specific class is essential. Generalizability can be enhanced through the application of diverse sensors. Future research should explore other model architectures, deep learning techniques, and variations in atmospheric conditions or temporal factors to further enhance cloud detection accuracy. These findings not only advance the field of cloud detection but can also be applied to other remote sensing domains. For instance, this approach could be valuable in disaster monitoring and response, enabling more efficient and error-free cloud detection, which makes it an important application. By using TOA or surface reflectance inputs, rather than relying solely on DN values, improved performance may be achieved in various Earth observation applications [29; 30]. Conceptualization, J.Y. ; methodology, J.Y., H.O. and S.K. ; software, H.O., S.K., D.H., Y.L. and E.D. ; writing--original draft preparation, J.Y.