Three Papers that Introduce and Evaluate CMAC Atmospheric Correction Offered through RESOLV™
RESOLV is SaaS applying the Closed-form Method for Atmospheric Correction (CMAC) for surface reflectance conversion of all visible through near-infrared wavelengths. Application to hyperspectral data will be developed through a next-gen program.
CMAC is an algorithm developed in part through National Science Foundation Small Business Innovation Research Phase I and II awards. CMAC is built upon crucial relationships between the scatter and absorption of light from atmospheric transmission.
Although atmospheric changes are highly variable, two properties predictably affect reflectance: under increasing atmospheric aerosol loading, scatter will increase dark target reflectance and absorbance will decrease bright target reflectance. CMAC measures, then reverses this atmospheric effect to deliver surface reflectance efficiently, rapidly, and accurately using a conceptual model with precedence in a highly regarded 1985 remote sensing paper.
The supplementary materials below include image lists, shapefiles, and Excel spreadsheets to verify the accuracy of each paper’s results and conclusion.
How is CMAC different from radiative transfer?
- The mathematical solution produces stable, accurate and reliable results.
- Uses statistics, alone, so works upon image download from the satellite.
- Produces surface reflectance for all environments including the ocean.
- Is software that can be rapidly calibrated for application to any VNIR satellite.
- Accommodates the serious effects from forward scatter.
- Available as RESOV-t for routine terrestrial correction and as RESOLV-w in a beta version for surface reflectance correction over the ocean.
Paper 1 - Published in Applied Sciences Volume 13(10) 5-22-2023
Closed-Form Method for Atmospheric Correction (CMAC) of Smallsat Data Using Scene Statistics
David Groeneveld and Tim Ruggle sand Bo-Cai Gao
(Abstract)
High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance.
Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency’s Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands.
To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor.
Paper 2 - Published in Applied Sciences Volume 13(23) 11-23-2024
An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics
David Groeneveld and Tim Ruggles
(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 (Atm-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.
Paper 3 - Published in Remote Sensing Volume 16, 6-19-2024
Landsat-8/9 Atmospheric Correction Reliability Using Scene Statistics
David Groeneveld and Tim Ruggle sand Bo-Cai Gao
(Abstract)
Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, Closed-form Method for Atmospheric Correction (CMAC) developed for smallsat application, provides surface reflectance derived solely from scene statistics.
In a prior paper, CMAC closely agreed with LaSRC software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC