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In many applications, ground-leaving reflectance data is needed to fully understand the desired phenomenology in the obtained observations. Measured sensor data is altered by spectrally dependent effects due to the atmosphere and are commonly removed using an atmospheric compensation algorithm. Most existing atmospheric compensation algorithms are specifically designed to operate on single image products. We present a simple atmospheric compensation algorithm that takes advantage of multiple temporal images over a single site for an improved surface reflectance result. The algorithm identifies psuedo-invariant feature pixels common across an entire time series and adjusts the compensation for an improved result over single image compensation. Initial results show our temporal approach outperforms Sen2Cor, improves reflectance retrieval accuracy of Sentinel-2 products by 3.5% percent, and yields an overall accuracy of 5.6% percent relative to RadCalNet ground truth.
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Michael Gartley, Emmett Ientilucci, James Albano, Sarvani Bhamidi, "Temporally Adjusted Atmospheric Compensation (TAAC) for space-based multispectral imagery," Proc. SPIE 12232, Earth Observing Systems XXVII, 122320K (30 September 2022); https://doi.org/10.1117/12.2646380