Correcting atmospheric effects on hyperspectral optical satellite scenes is paramount to ensuring the accuracy of derived bio-geophysical products. The open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was first initiated in 2016 and has now been extended to provide a comprehensive assessment of atmospheric processors of space-borne imaging spectroscopy missions (EnMAP and PRISMA) over land surfaces. The exercise contains 90 scenes, covering stations of the Aerosol Robotic Network (AERONET) for assessing aerosol optical depth (AOD) and water vapour (WV) retrievals, as well as stationary networks (RadCalNet and HYPERNETS) and ad hoc campaigns for surface reflectance (SR) validation. AOD, WV, and SR retrievals were assessed using accuracy, precision, and uncertainty metrics. For AOD retrieval, processors showed a range of uncertainties, with half showing overall uncertainties of <0.1 but going up to uncertainties of almost 0.4. WV retrievals showed consistent offsets for almost all processors, with uncertainty values between 0.171 and 0.875 g/cm2. Average uncertainties for SR retrievals depend on wavelength, processor, and sensor (uncertainties are slightly higher for PRISMA), showing average values between 0.02 and 0.04. Although results are biased towards a limited selection of ground measurements over arid regions with low AOD, this study shows a detailed analysis of similarities and differences of seven processors. This work provides critical insights for understanding the current capabilities and limitations of atmospheric correction algorithms for imaging spectroscopy, offering both a foundation for future improvements and a first practical guide to support users in selecting the most suitable processor for their application needs.
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RBINS Staff Publications 2025
Atmospheric scattering occurs over a horizontal scale of several kilometers. This results in influence from neighboring surface features on the signal recorded over a given position, reducing contrast and the accuracy of quantitative retrievals of surface reflectance from satellite imagery. This atmospheric blurring, or adjacency effect, must be accounted for when both contrast in surface reflectance and magnitude of atmospheric scattering are significant. Taking into account the adjacency effect is of particular importance for aquatic remote sensing of inland and coastal waters due to the high contrast between water and different land cover types, as well as the small spatial scale of most inland water bodies. In this paper, we present a physics-based processor to retrieve surface reflectance over all surface types, regardless of the subscene composition and sensor waveband configuration. The processor is implemented in the free and open source ACOLITE software and is composed of two modules: (1) TSDSF for the estimation of aerosol properties and (2) RAdCor for the retrieval of surface reflectance. We demonstrate the performance of the TSDSF $+$ RAdCor processor for the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2A and 2B over a set of small ($\lt1\;\rm km^2$) inland waters in Belgium, and compare the performance with other common processors for these sensors, including C2RCC, POLYMER, Sen2Cor, iCOR, ACOLITE/DSF, and LaSRC. For clear sky matchups, the relative deviation againstin situ data in the visible wavebands ranged between 6% and 18% for OLI, and between 14% and 31% for MSI, except for the MSI waveband centered at 443 nm where the relative deviation was 70%. In the near-infrared wavebands, the relative deviation varied from 70% to 150%, with the exception of the MSI waveband centered at 704 nm, for which the performance was 17%. Overall, the new processor outperformed the other evaluated processors in the visible range, with the exception of the MSI waveband centered at 443 nm, and was outperformed by C2RCC and POLYMER in the near-infrared wavebands. Recommendations on how to use TSDSF and RAdCor in ACOLITE are provided.
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RBINS Staff Publications 2025
