We summarize our recent journal paper here introducing CMAC software and comparing it to Sen2Cor for atmospheric correction of Sentinel-2 data. Similarly, a topic paper comparing CMAC to LaSRC software of Landsat applies the same methods. Sen2Cor and LaSRC are industry standards responsible for virtually all surface reflectance processing, either directly or through transference to smallsats.

This paper recaps analyses comparing CMAC and LaSRC software performance for atmospheric correction of Landsat 8 and 9 images. The L8 and L9 sensor responses are virtually identical, so were not differentiated in this analysis. LaSRC corrected images applied by Landsat were downloaded from Earth Explorer. CMAC applies a completely different workflow than Sen2Cor and LaSRC. With no common methodology touchpoints, CMAC is best evaluated through comparative performance with existing methods.

As a first order approximation, color balance and clarity can verify that atmospheric correction results are close to true surface reflectance. If so, the resulting image will be clear with natural appearing colors. Such comparisons between CMAC and LaSRC have repeatedly shown wide differences that would hypothetically result from systematic divergence of surface reflectance estimates. An example of results for atmospherically corrected Landsat 8 data is the region near Lake Newell, Alberta, Canada. The disparity between the color balance of the CMAC and LaSRC corrections indicated that their output was significantly different. Although the CMAC version looks appropriate, the question was asked, which is correct? This question was examined using reflectance distributions between high and low spectral diversity AOIs.

The term “atmospheric effect” is used as a general term for
how light is changed through its interaction with Earth’s atmosphere. Existing atmospheric correction software, for example LaSRC for correction of Landsat, apply ancillary data generated by another satellite, MODIS (Moderate Resolution Imaging Spectroradiometer).

This requirement for ancillary data is a significant impediment for smallsat applications because MODIS data have coarse spatial resolution; ancillary data need to be processed before becoming available, thereby delaying processing the data of interest and may be obtained at a time differing by up to hours from the image capture by the smallsat.

CMAC development began with a unique discovery: the changes in cumulative distribution functions (CDFs) from top-of-atmosphere reflectance (TOAR) occur in a structured pattern when comparing clear and hazy images for an area of interest (AOI) across short time spans. Increasing haze causes reflectance CDFs to rotate counterclockwise, and for decreasing haze, rotate clockwise. We called this the “pinwheel effect” due to the rotation of the CDF from the changing atmospheric effects.

A first indication of forward scatter was observed while we were developing the Atm-I model. The resulting grayscale output systematically showed locations that the model indicated were affected to a greater degree than would have been expected. Bright rooftops, rock outcrops, bare soil and water bodies were portrayed much brighter in the Atm-I grayscale than would be expected were backscatter from atmospheric particles the sole mechanism at work.

NDVI (normalized difference vegetation index) and similar indices represent plant activity mathematically to express vigor that directly translates to photosynthetic production , carbon uptake, yield, etc. For agriculture, NDVI is the most commonly used of many indices because it is both simple and reliable: NDVI = (NIR – Red) / (NIR + Red). Nearly all vegetation indices apply red and near infrared (NIR) bands to evaluate plant vigor. The red band experiences the least atmospheric scatter in the visible wavelengths, and NIR even less. Vigorous plants absorb nearly all red light to support photosynthesis; down to about 2-3% reflectance, while NIR is highly reflected from healthy plants.

CMAC was developed for smallsats in a two-part process that first maps the atmospheric effect in each image and then reverses it to deliver surface reflectance. Through our work developing CMAC, we have learned useful methods and applications. Two robust and simple applications are described here.

In our commitment to excellence, RESOLV’s method has undergone rigorous comparative analysis against established atmospheric correction software. RESOLV has been benchmarked against LaSRC and Sen2Cor and has successfully been applied to high-resolution smallsat data.

RESOLV’s completely new approach starts by mapping the atmospheric effect across each image as a grayscale input to an algorithm that is based on observations of the behavior of light after transmission through Earth’s atmosphere. This algorithm inverts and adjusts the accepted empirical line method resulting in a closed-form equation that provides lightning-fast calculations. Through this structure, RESOLV delivers the most accurate estimates of surface reflectance possible for any image anywhere.

Key findings from the comparison include:

• Proven Accuracy: RESOLV consistently delivers high levels of accuracy in surface reflectance data, often surpassing other methods in challenging atmospheric conditions.
• Designed Efficiency: RESOLV’s intricate design ensures rapid data processing, delivering near real-time surface reflectance data of impeccable quality.
• Future-Ready Versatility: Beyond mere proof-of-concept, RESOLV has exhibited its adaptability, seamlessly integrating with a diverse range of smallsats.