###### Abstract  
Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Att-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (\(n=9\)) was 0-3% for CMAC, 6-20% for LaSRC, and 13-38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires. atmospheric correction; smallsat; harmonization; Landsat; Sentinel 2; scene statistics; near real time; LaSRC; Sen2 Cor +
Footnote †: journal: Applied Sciences  
this simplification: in repeated tests, CMAC is proving accurate and reliable in near real time across a wide range of environments, including low-spectral-diversity deserts and water bodies [7]. To ensure maximum data utility, AC can be performed in or near real time upon image acquisition. CMAC was introduced in an earlier article by [PERSON] et al. [7], referred to here as \"paper 1\". CMAC is a radical departure for AC because it does not apply RadTran methods that have been the primary approach for automated satellite imagery [4]. After decades of intensive effort, two RadTran software applications that are considered the state of the art and stand out as accounting for the vast majority of AC-treated imagery are Land Surface Reflectance Code (LaSRC) of the Landsat program [6] and Sen2 Cor of the European Space Agency's Sentinel 2 program [8]. Data from the sensors of Landsat 8 and 9 and Sentinel 2 have been proposed as the basis for RadTran application for AC of smallsat data, and of these two satellite systems, LaSRC can be taken to represent the best output possible for AC of smallsat data using RadTran [5,9]. LaSRC and Sen2 Cor can be regarded as representative of the current RadTran state of the art. As a continuation for comparison to RadTran, CMAC is evaluated here using the same analyses and metrics applied in paper 1. This workflow employed statistical metrics of precision and accuracy as well as the general performance of processing time, repeatability, reliability, and ease for smallsat application. Additionally, qualitative image-to-image comparisons of CMAC and LaSRC corrections were made using criteria of clarity, color balance and lack of induced artifacts resulting from the correction. Before CMAC, RadTran provided the only mathematical basis for fully automated software to retrieve SR from satellite imagery. RadTran application requires radiance calibration for each spectral band that is not directly measurable in smallsates because onboard equipment to do so is omitted to optimize weight and size for economical launch in flocks. Further complicating this problem are variable changes to the sensor response of each spectral band that are well known to occur in the orbital environment [10,11].