| 04 May 2026
Intercomparison of nighttime aerosol optical depth retrievals from both reflectance-based and city light-based methods using VIIRS DNB data
Abstract. Using observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), two different nighttime aerosol optical depth (AOD) retrieval methods were evaluated and inter-compared. The first approach is a lunar reflectance-based retrieval method, using reflected moonlight in a manner similar to daytime retrievals. The second approach utilizes changes in light patterns over regions with artificial light sources due to the upward diffusion of light by aerosol particles. Both retrieval methods were implemented over Dakar, Senegal for 2017 and 2018. Retrievals from both approaches were evaluated against ground-based solar and lunar AErosol RObotic NETwork (AERONET) data, as well as daytime AOD retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, impacts of using the Miller and Turner lunar model for estimating Top-Of-Atmosphere (TOA) lunar spectral flux for AOD retrievals were also studied. Findings suggest that while both retrieval methods show skill in retrieving nighttime AOD by qualitatively identifying over-ocean aerosol plume locations and quantitatively comparing with solar and lunar retrieved AERONET data, cloud contamination and variations in lunar properties are factors that need to be carefully quantified in future studies for accurate nighttime aerosol retrievals using VIIRS DNB data. This study suggests that there are sampling issues from both approaches, but the combined use of both retrieval methods can increase the sampling rate for nighttime aerosol retrievals by more than 50 %.
Paper Review: Intercomparison of Nighttime AOD Retrievals from VIIRS DNB Data
This paper evaluates and intercompares two nighttime AOD retrieval methods, a lunar reflectance-based approach and a city light-based approach, using VIIRS DNB data over Dakar, Senegal for 2017–2018. Both methods are validated against AERONET and compared with MODIS, demonstrating qualitative skill in identifying aerosol plumes. The work is timely and significant, as nighttime aerosol retrieval remains an underserved area, and the finding that combining both methods increases nighttime sampling by over 50% is a practically valuable contribution to the remote sensing community. The paper is well-written, well-organized, and the multi-source validation framework is solid.
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Major Issues
The most critical weakness is the extremely small coincident sample size, only 8 valid retrieval pairs across two years, which fundamentally limits the statistical credibility of the intercomparison. The authors should consider extending the time period or expanding to additional sites to build a more meaningful dataset. Additionally, the relationship between the lunar reflectance method presented here and the previously published algorithm from Wang et al. (2020) and Zhou et al. (2021, 2024) is never clearly established, raising concerns that whether what is presented here shows the similar quality as what is published. Finally, while moon phase is acknowledged as a major uncertainty source, its systematic impact on retrieval count and bias for each method is never quantified, a dedicated analysis of retrieval performance as a function of lunar illumination fraction is strongly recommended.
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Minor Issues
Wang, J., Zhou, M., Xu, X., Roudini, S., Sander, S.P., Pongetti, T.J., Miller, S.D., Reid, J.S., Hyer, E. and Spurr, R., 2020. Development of a nighttime shortwave radiative transfer model for remote sensing of nocturnal aerosols and fires from VIIRS. Remote sensing of environment, 241, p.111727.; Zhou, M., Wang, J., Chen, X., Gomes, J., Levy, R.C. and Miller, S.D., 2024, January. Link Day and Night: A Deep Learning Framework to Retrieve Global Nighttime Aerosol Optical Depth from VIIRS DNB. In 104th Annual AMS Meeting 2024 (Vol. 104, p. 435849