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The force behind RESOLV is Dr. David Groeneveld, whose interest in satellite imaging traces back to the inception of the first Landsat program in the mid-1970’s. David’s innovative applications for satellite reflectance imaging have resulted in high-profile groundbreaking projects: multiple regional water resource assessments, a precision irrigation application sold to Monsanto/Climate Corp, an ecological model to predict vegetation responses for Sandia Labs/ U.S. DOE, and a monitoring program for dust control measures for the City of Los Angeles and the managing Air Pollution Control District that has been in use, now, for over two decades.
With RESOLV, David’s on a mission solving atmospheric correction, the major impediment for growth of the smallsat image industry.
CMAC versus Sen2Cor Software Surface Reflectance from Sentinel-2 Data
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.
CMAC versus LaSRC Software
for Landsat Data
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.
Reliability and Stability of CMAC Compared to LaSRC
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.
Join us as we explore atmospheric correction of satellite images and how this unlocks the data for precision applications.
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.