## 4 Discussion
LaSRC uses ancillary data from MODIS [6] to represent atmospheric effects. Instead, CMAC uses scene statistics to bypass the need for ancillary data to enable image processing immediately upon download. This difference, plus the economy for data processing in the CMAC workflow, may support multiple ISR objectives. For example, a real-time edge application with CMAC residing in-satellite and lagging by several seconds can correct an image as it is scrolled into memory by the pushbroom scanner. A digital image cleared of haze could then be transmitted to first responders or warfighters with only seconds of latency. LaSRC and CMAC SR retrievals are compared here for 31 images obtained from a commonly encountered range of atmospheric effects. A comparison of the results showed that the CMAC and LaSRC SR retrievals were virtually identical for low-to-moderate levels of Atm-I. SR retrieval was also studied for moderate-to-extreme levels of Atm-I through comparisons of British Columbia images from three consecutive years under comparable environmental conditions and roughly commensurate SR distributions. Two images captured clear conditions and the third was impacted by a range of Atm-I wildfire smoke. Extracted data for the four VNIR bands processed by CMAC and LaSRC for the three years were combined into three normalized difference indices. These indices demonstrated that CMAC SR retrieval remains competent to support machine analysis and AI as Atm-I transitions from low to extreme effects. Judged through the nine index values calculated, CMAC contained \(-3\%\) to \(2\%\) error (average 0.4%). Comparatively, LaSRCerrors indicate a bias for increasing under correction at moderate-to-extreme levels of Atm-I, varying from \(-6\%\) to \(-20\%\) (average \(-9.9\%\)). Although it is well known that atmospheric correction is a necessary step in image processing for most applications, smallsat data purveyors may be tempted to omit this step if they find application of the RadTran AC and the harmonization pathway to be complex, inconvenient, or to provide insufficient accuracy. However, the overwhelming volume of images currently generated by smallsats can only be evaluated through automated machine analysis workflows and/or AI, and this data overload will only grow as more imaging smallsats are brought online. Against the need for automated image analysis, the results presented in Table 2 offer the best argument for why AC is a crucial step in image processing: data left as uncorrected TOAR incorporated significant uncertainty that increased the apparent error 13% to 38% as Atm-I increased. Depending upon the application, such high levels of error are likely unacceptable. Test results of CMAC for the 30 m pixel, L8/9 data and higher-resolution 10 m pixel S2 data, provided confirmation that CMAC accuracy and precision will increase as pixel spatial resolution increases. This enhancement is due to higher granularity for Atm-I assessment and reduced pixel-mixing effects. As can be appreciated from the results of these analyses, current state-of-the-art LaSRC functions relatively well for images that are not highly affected by the atmosphere, but it is limited for images affected by higher levels of Atm-I. However, with each passing year, climate change is intensifying and causing increasing wildfire incidence and magnitude [32]. Smoke haze can cover most of North America for weeks at a time [33] and elevated wildfires are now occurring over large pan-boreal regions [34]. Wildfire smoke has a significant impact upon all applications of satellite imagery, a ready example being precision agriculture's reliance upon vegetation indices that are highly affected by variable atmospheric effects [35], a finding corroborated by the Test 3 NDVI results. The intent for CMAC development has been its application to serve the smallsat industry. Likewise, LaSRC, widely regarded as state-of-the-art in AC, has been tasked for SR retrieval from smallsat data [5] through a radiance harmonization pathway [5; 9].