Cremer et al. (2025) Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-11-21
• Authors: Noelle Cremer, Kevin Alonso, Georgia Doxani, Adam Chlus, David R. Thompson, Philip G. Brodrick, Philip A. Townsend, Angelo Palombo, Federico Santini, Bo‐Cai Gao, Feng Yin, Jorge Vicent, Quinten Vanhellemont, Tobias Eckert, Paul Karlshöfer, R. de los Reyes, Weile Wang, Maximilian Brell, Aimé Meygret, Kevin Ruddick, Agnieszka Białek, Pieter De Vis, Ferran Gascon
• DOI: 10.3390/rs17233790

Research Groups

Not explicitly stated, but involves an inter-comparison exercise (ACIX) of seven different atmospheric processors from various research groups.

Short Summary

This study extends the ACIX benchmark to comprehensively assess atmospheric correction processors for EnMAP and PRISMA imaging spectroscopy missions over land, evaluating their accuracy, precision, and uncertainty in retrieving aerosol optical depth, water vapour, and surface reflectance against ground truth data.

Objective

• To provide a comprehensive assessment of atmospheric processors for space-borne imaging spectroscopy missions (EnMAP and PRISMA) over land surfaces, specifically evaluating the accuracy, precision, and uncertainty of aerosol optical depth (AOD), water vapour (WV), and surface reflectance (SR) retrievals.

Study Configuration

• Spatial Scale: Distributed across land surfaces, covering 90 scenes at various ground station networks (AERONET, RadCalNet, HYPERNETS) and ad hoc campaign sites.
• Temporal Scale: The exercise was initiated in 2016 and has been extended, covering a period of data collection for the 90 scenes since then.

Methodology and Data

• Models used: Seven distinct atmospheric correction processors (specific names not provided).
• Data sources:
  • Satellite: EnMAP and PRISMA space-borne imaging spectroscopy missions.
  • Observation (Ground Truth):
    • Aerosol Optical Depth (AOD) and Water Vapour (WV): Aerosol Robotic Network (AERONET).
    • Surface Reflectance (SR): RadCalNet, HYPERNETS, and ad hoc campaigns.

Main Results

• AOD Retrieval: Processors showed a range of uncertainties; half exhibited overall uncertainties less than 0.1, while others reached nearly 0.4.
• WV Retrieval: Consistent offsets were observed for almost all processors, with uncertainty values ranging from 1.71 kg/m² to 8.75 kg/m².
• SR Retrieval: Average uncertainties varied between 0.02 and 0.04, depending on wavelength, processor, and sensor, with PRISMA showing slightly higher uncertainties.
• Bias: The results are biased towards a limited selection of ground measurements, primarily over arid regions with low AOD.

Contributions

• Provides a detailed analysis of the similarities and differences among seven atmospheric correction processors for imaging spectroscopy.
• Offers critical insights into the current capabilities and limitations of atmospheric correction algorithms.
• Establishes a foundation for future improvements in atmospheric correction methodologies.
• Serves as a practical guide to assist users in selecting the most suitable processor for their specific application needs.

Funding

Not explicitly stated in the provided text.

Citation

@article{Cremer2025Atmospheric,
  author = {Cremer, Noelle and Alonso, Kevin and Doxani, Georgia and Chlus, Adam and Thompson, David R. and Brodrick, Philip G. and Townsend, Philip A. and Palombo, Angelo and Santini, Federico and Gao, Bo‐Cai and Yin, Feng and Vicent, Jorge and Vanhellemont, Quinten and Eckert, Tobias and Karlshöfer, Paul and Reyes, R. de los and Wang, Weile and Brell, Maximilian and Meygret, Aimé and Ruddick, Kevin and Białek, Agnieszka and Vis, Pieter De and Gascon, Ferran},
  title = {Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17233790},
  url = {https://doi.org/10.3390/rs17233790}
}