## 3 Results
Data from 2023 and 2022 (low Atm-I), agreed closely, as do the index values between CMAC and LaSRC for 2023 and 2022, confirming that true surface reflectance between these two dates was consistent, that surface reflectance for the 2021 smokey condition was likely equivalent to 2022 and 2023, and that the experimental design was appropriate. As in Test 1, the clean 2022 indices can be treated as having been calculated from the true surface reflectance for the calculation of % error for the comparison to the smoke-affected indices of 2021. These calculations applied the equation presented in the Materials and Methods (% error = (value \(-\) standard)/standard \(\times\) 100). The calculated CMAC  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**TOAR**} & \multicolumn{3}{c}{**CMAC**} & \multicolumn{3}{c}{**LaSRC**} \\ \hline
**Penticton** & Atm-I & NDVI & NDBI & NDGI & NDVI & NDBI & NDGI & NDVI & NDBI & NDGI \\ \hline
2021 & 1002 & 0.741 & 0.584 & 0.641 & 0.899 & 0.910 & 0.831 & 0.840 & 0.847 & 0.756 \\ \hline
2022 & 807 & 0.851 & 0.709 & 0.747 & 0.913 & 0.937 & 0.841 & 0.913 & 0.908 & 0.834 \\ \hline
2023 & 841 & 0.827 & 0.691 & 0.726 & 0.898 & 0.926 & 0.835 & 0.883 & 0.878 & 0.810 \\ \hline  & **2022 v. 2021 error** & \(\mathbf{-13\%}\) & \(\mathbf{-18\%}\) & \(\mathbf{-14\%}\) & \(\mathbf{-2\%}\) & \(\mathbf{-3\%}\) & \(\mathbf{-1\%}\) & \(\mathbf{-8\%}\) & \(\mathbf{-7\%}\) & \(\mathbf{-9\%}\) \\ \hline  & & \multicolumn{3}{c}{**TOAR**} & \multicolumn{3}{c}{**CMAC**} & \multicolumn{3}{c}{**LaSRC**} \\ \hline
**Kelowna** & Atm-I & NDVI & NDBI & NDGI & NDVI & NDBI & NDGI & NDVI & NDBI & NDGI \\ \hline
2021 & 1133 & 0.691 & 0.522 & 0.591 & 0.909 & 0.909 & 0.839 & 0.820 & 0.847 & 0.725 \\ \hline
2022 & 805 & 0.839 & 0.692 & 0.735 & 0.902 & 0.930 & 0.835 & 0.904 & 0.902 & 0.824 \\ \hline
2023 & 816 & 0.824 error remained consistent under conditions of increasing Atm-I and even showed a slight reduction. From these results, CMAC correction can therefore be judged as reliable for applications utilizing NDVI or similar indices, AI feature extraction and various other machine analyses. Likewise, error propagation in the visible L8/9 bands was found to be lower than LaSRC for low reflectance in Test 1 (Figure 10). This stability is reflected in CMAC's superior performance for the Test 3 indices generated under moderate-to-extreme levels of Atm-I. The high degree of error of the TOAR data in Table 2 provides strong quantitative evidence for why surface reflectance retrieval is important. Given that far more smallsat images are now being generated than is practicable for analyst attention, accurate AC assumes a critical role to prepare image data for automated analyses. If performed correctly, a by-product of an AC processing step is image clarity. While existing methods can already achieve this result, they require analyst-protocol software and considerable attention to produce admittedly approximate output [29]. CMAC can provide both in one near-real-time operation upon image download. An independent GIS-based application is planned for fine tuning severely impacted images, is an application described in the context of extreme Atm-I that affected Payette, Idaho and Davenport Iowa, USA, examples in the image gallery that supports Test 4. This secondary processing could be reserved for application to the most severely atmospherically affected images where tolerances for SR retrieval can be overwhelmed by the associated uncertainties. This application would incorporate the calibrated CMAC relationships adjusted by the analyst. From repeated data correction and observation of the magnitude of Atm-I generated by the CMAC process, it is apparent that the extreme Atm-I levels measured in and around Vernon, BC can be expected to be episodic and due directly to coherent smoke plumes relatively close to the wildfire.