## 3 Results
A comparison of the average CV% distributions shows that CMAC performed better at the lowest reflectance levels. CMAC provides higher precision for the low reflectance of the red band and for the moderately high levels of NIR, both forming the indices for assessing vegetation performance for precision agriculture, measuring carbon cycling [25; 26] and potentially for AI feature extraction [27; 28]. The slightly higher dispersion (\(\sim\)1-2%) observed in the central portions of the CMAC-corrected visible band distributions may result from the relatively cursory L8/9 calibration. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & & **Blue** & & & & & & & **Red** & & & **NIR** \\  & & **Band 02** & & & & & & & & **Band 04** & & & **Band 05** & \\ \hline
**Percentile** & **TOAR** & **CMAC** & **LaSRC** & **TOAR** & **CMAC** & **LaSRC** & **TOAR** & **CMAC** & **LaSRC** & **TOAR** & **CMAC** & **LaSRC** \\ \hline
1 & 5.8\% & 7.1\% & 25.0\% & 6.3\% & 6.3\% & 8.6\% & 6.7\% & 6.6\% & 7.6\% & 4.4\% & 4.7\% & 4.7\% \\ \hline
3 & 4.6\% & 5.3\% & 10.7\% & 5.6\% & 5.1\% & 7.0\% & 5. The SR retrieval error for each image was estimated from the average distribution of the five lowest Atm-I images. This low Atm-I average was treated as the best available estimate of the true SR distribution. These calculations were employed individually in each QIA as the standard for error estimation of each percentile in the statistical distributions. To reduce the potential for biasing the comparison, the CMAC low Atm-I average was used as the standard for CMAC error estimation, and the LaSRC low Atm-I average was used for LaSRC error estimation. Like CV%, % error is a statistic normalized through division by average values; hence, it can be combined for the five SoCal QIAs for an evaluation of the overall error of the two methods. In Figure 9, % error estimates from each QIA were pooled and plotted according to their percentile rank. Figure 9 demonstrates that the accuracy achieved using the CMAC provisional v1.1-I calibration is roughly equivalent to the accuracy of LaSRC. The error distributions for CMAC shown in Figure 9 closely approximate the error distributions measured for S2 SR retrieval in Figure 14 of our earlier paper [7]. To further investigate how error may affect the lower end of reflectance that is critical for AI feature extraction and vegetation indices, the absolute values of the estimated error were averaged for the reflectance distribution percentiles between 1% and 20% to create one index value representing the error for each image-QIA combination (n = 155 per band). These statistics were then pooled across the five QIAs and plotted according to the QIA median Atm-I (Figure 10). The degree of data scatter increases with Atm-I. Regression lines for the low-reflectance index values in Figure 10 allow a statistical comparison of low-reflectance error from the clouds of points. In all four bands, LaSRC low-reflectance error was greater than CMAC judged in this manner. This disparity was greatest in the blue band, less in the green band, and even less but still present for the red band. This same pattern was also apparent in the CMAC-to-Sen2 Cor comparison in paper 1, but more pronounced, especially for the blue band. A direct comparison of the same test reported in paper 1 indicates that LaSRC is more stable at the low end of Atm-I than Sen2 Cor. Given the results of paper 1, this testing indicates that CMAC is more stable and accurate than Sen2 Cor and LaSRC. The results from the error analysis that are summarized in Figures 9 and 10 here, and presented in paper 1, provide conclusive proof that the lumped-sum approach for determining atmospheric effects upon SR retrieval employed through Atm-I in CMAC has no discernible negative effect upon accuracy, at least in comparison to LaSRC and Sen2 Cor output.