## 4 Discussion  
CMAC is a new method for atmospheric correction with no touchpoint for comparison to existing methods except through an evaluation of AC performance. While the CMAC workflow bears little resemblance to radiative transfer-based methods such as LaSRC, it could not have been developed without the prior pioneering work in surface reflectance retrieval that developed it for Landsat [30] and applications applied to Sentinel 2 [31]. Likewise, CMAC R&D was only made possible through analyses of copious high-quality imagery from the European Space agency's Sentinel 2 program and the U.S. Geological Survey and NASA-supported Landsat program. CMAC is based upon observations of the response of atmospherically transmitted reflectance. The empirical line method was adapted, inverted and adjusted to become the CMAC conceptual model that estimates a linear relationship expressing the deviation of surface reflectance to TOAR reflectance. The CMAC conceptual model is a linear representation of the effect from an atmospheric state upon the reflectance of all pixels, dark to bright, in the visible-through-NIR spectrum. Precedence exists in the remote sensing literature for such an approach [14]. CMAC provides spatial estimates of atmospheric effect that are calculated by a model that applies scene statistics to estimate an atmospheric index, Atm-I. Through this model, Atm-I magnitude represents the blue band reflectance of hypothetical DDV plus the backscatter from the atmosphere. Atm-I values form a grayscale whose brightness provides a spatially discrete scalar to adjust the CMAC conceptual model. The combination of this conceptual model and the Atm-I model constitute the workflow that retrieves SR from TOAR. A third step calibrates each band's sensor response to Atm-I. Calibration measures the slopes and offsets for the dynamic linear responses driven by Atm-I that exist for each sensor band. The calibration workflow results in curves of slope and offset to Atm-I that can then translate the Atm-I grayscale into a linear expression for atmospheric effect upon any pixel's TOAR, from dark to bright. These three separate parts allow CMAC to reverse the atmospheric effect and retrieve an SR estimate individually for each band of each pixel of a calibrated satellite using only two inputs: TOAR and the Atm-I that caused it. The CMAC calibration for L8/9 was derived from single points per band in application of master-proxy calibration that extrapolates single precise points for the range of Atm-I that can be encountered. The extrapolation was guided by the shapes of master curves of the slope and offset of each band driven by Atm-I that form families of curves of similar shape that nest together and stack in Cartesian space according to their wavelength. The L8/9 data were treated as the proxy while the highly calibrated S2 families of curves were treated as the master. This calibration generated the CMAC v1.1L software's SR retrieval from the L8/9 TOAR presented in this paper. Presently, calibration is applied vicariously through fitting slopes and offsets of multiple images to match known SR distributions. While this procedure was approached with programmatic steps, because such vicarious fitting incorporates uncertainty, multiple images were applied and the resulting slopes, offsets and Atm-I parameters were averaged to control the uncertainty. When fully operational, CMAC will, instead, apply a calibration target to achieve highly precise and fully automated calibration for potential application to an unlimited field of smallsats. The efficiency of this workflow is expected to ensure that high volumes of smallsats can be calibrated and periodically recalibrated to counter the inevitable but unpredictable episodic degradation of sensor response that is the consequence of the orbital environment [11]. The accepted state-of-the-art LaSRC software was applied by the Landsat program and served as a milestone for judging the performance of CMAC SR retrieval.