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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233790
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Article

Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land

by
Noelle Cremer
1,*,
Kevin Alonso
2,
Georgia Doxani
1,
Adam Chlus
3,
David R. Thompson
3,
Philip Brodrick
3,
Philip A. Townsend
3,4,
Angelo Palombo
5,
Federico Santini
5,
Bo-Cai Gao
6,
Feng Yin
7,8,
Jorge Vicent Servera
9,
Quinten Vanhellemont
10,
Tobias Eckert
11,
Paul Karlshöfer
11,
Raquel de los Reyes
11,
Weile Wang
12,
Maximilian Brell
13,
Aime Meygret
14,
Kevin Ruddick
10,
Agnieszka Bialek
15,
Pieter De Vis
15 and
Ferran Gascon
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1
SERCO SpA c/o European Space Agency (ESA), European Space Research Institute (ESRIN), Largo Galileo Galilei 1, 00044 Frascati, Italy
2
European Space Agency (ESA), European Space Research Institute (ESRIN), Largo Galileo Galilei 1, 00044 Frascati, Italy
3
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
4
Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI 53706, USA
5
Institute of Methodologies for Environmental Analysis, National Research Council, Area della Ricerca di Potenza Contrada S. Loja, Zona Industriale C.P. 27, 85050 Tito Scalo, Italy
6
Remote Sensing Division, Naval Research Laboratory, Washington, DC 20375, USA
7
Department of Geography, University College London, Gower Street, London WC1E 6BT, UK
8
National Centre for Earth Observation, Space Park Leicester, Leicester LE4 5SP, UK
9
Magellium, 1 Rue Ariane, 31520 Ramonville-Saint-Agne, France
10
Operational Directorate Natural Environment, Royal Belgian Institute for Natural Sciences (RBINS), Vautierstraat 29, 1000 Brussels, Belgium
11
Imaging Spectroscopy Department, Remote Sensing Technology Institute, German Aerospace Center (DLR), Münchener Str. 20, 82234 Weßling, Germany
12
NASA Ames Research Center, Moffett Field, CA 94035, USA
13
HySpex by NEO (Norsk Elektro Optikk), Østensjøveien 34, 0667 Oslo, Norway
14
Centre National d’Etudes Spatiales, 18 Av. Edouard Belin, 31400 Toulouse, France
15
National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
 
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Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3790; https://doi.org/10.3390/rs17233790
Submission received: 21 October 2025 / Revised: 11 November 2025 / Accepted: 12 November 2025 / Published: 21 November 2025
(This article belongs to the Section Atmospheric Remote Sensing)
Versions Notes

Abstract

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.
Keywords: atmospheric correction; inter-comparison; EnMAP; PRISMA; surface reflectance; aerosol optical depth; water vapour; hyperspectral; imaging spectroscopy atmospheric correction; inter-comparison; EnMAP; PRISMA; surface reflectance; aerosol optical depth; water vapour; hyperspectral; imaging spectroscopy

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MDPI and ACS Style

Cremer, N.; Alonso, K.; Doxani, G.; Chlus, A.; Thompson, D.R.; Brodrick, P.; Townsend, P.A.; Palombo, A.; Santini, F.; Gao, B.-C.; et al. Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land. Remote Sens. 2025, 17, 3790. https://doi.org/10.3390/rs17233790

AMA Style

Cremer N, Alonso K, Doxani G, Chlus A, Thompson DR, Brodrick P, Townsend PA, Palombo A, Santini F, Gao B-C, et al. Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land. Remote Sensing. 2025; 17(23):3790. https://doi.org/10.3390/rs17233790

Chicago/Turabian Style

Cremer, Noelle, Kevin Alonso, Georgia Doxani, Adam Chlus, David R. Thompson, Philip Brodrick, Philip A. Townsend, Angelo Palombo, Federico Santini, Bo-Cai Gao, and et al. 2025. "Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land" Remote Sensing 17, no. 23: 3790. https://doi.org/10.3390/rs17233790

APA Style

Cremer, N., Alonso, K., Doxani, G., Chlus, A., Thompson, D. R., Brodrick, P., Townsend, P. A., Palombo, A., Santini, F., Gao, B.-C., Yin, F., Servera, J. V., Vanhellemont, Q., Eckert, T., Karlshöfer, P., de los Reyes, R., Wang, W., Brell, M., Meygret, A., ... Gascon, F. (2025). Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land. Remote Sensing, 17(23), 3790. https://doi.org/10.3390/rs17233790

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