the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Apr 2026
An AI Based Algorithm for Retrieving Aerosol Optical Depth and Single Scattering Albedo Using All-Sky Imager Observations
Abstract. Accurate measurement of aerosol optical properties is critical for understanding their radiative and environmental impacts. Currently, the most accurate retrieval of aerosol properties comes from the multi-channel surface sun photometer, but with relatively high cost and deployment/maintenance requirements. Here we develop a novel AI based method for retrieving daytime aerosol optical parameters, namely aerosol optical depth (AOD) and single scattering albedo (SSA) using images acquired by All-Sky Imagers (ASI). Surface based AOD and SSA retrievals from surface sun photometers are used as the training targets. Algorithm training and retrievals were performed for two sites in East China and Central US respectively. Independent validation against ground-based measurements demonstrated high consistency between the ASI-retrieved and sun photometer measured AOD and SSA, with Pearson correlation coefficients (r) exceeding 0.86 for AOD across all wavelengths at both sites and Root Mean Square Errors (RMSE) below 0.25. For SSA, r values reached 0.67 at the Beijing_PKU site and 0.84 at the SGP site, with RMSE remaining below 0.09 across all spectral channels, demonstrating the feasibility of simultaneous AOD and SSA retrieval from low-cost all-sky imagers. This method not only overcomes the high computational cost associated with traditional radiative transfer iterative algorithms, but also provides great potential for denser surface aerosol measurements by leveraging the low-cost and easy-maintenance advantages of the all-sky imager.
Competing interests: The authors declare that a patent application related to the method described in this manuscript has been filed by Peking University (Application No. CN202511448418.3). The authors have no other competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 14 Jul 2026)
- RC1: 'Comment on egusphere-2026-2019', Anonymous Referee #1, 09 Jun 2026 reply
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RC2: 'Comment on egusphere-2026-2019', Anonymous Referee #2, 25 Jun 2026
reply
This study developed a novel method to retrieval aerosol optical depth and single scattering properties from low cost sky cameras. It serves as a potentially useful supplement for the more sophisticated sun photometer approach. The manuscript is also in general clearly presented and well constructed, which is a great contribution to the community by providing AOD and SSA simultaneously. I suggest several points need further clarification before it can be accepted by AMT.
Specific comments:
- The manuscript states that previous studies mainly focused on AOD, while this study additionally retrieves SSA. This is an important point and should be emphasized more clearly in the Introduction and Summary. It would be helpful to briefly compare the present method with previous ASI-based AOD studies in terms of input features, retrieval targets, validation strategy, and computational efficiency.
- The feature set includes RGB means, RBR, SZA, and RGB values sampled along an annulus. These choices are reasonable, but the manuscript could better explain how each feature relates to aerosol extinction, scattering, or absorption. This would make the AI model less like a black box and help readers understand why the method can retrieve both AOD and SSA.
- The SSA retrieval is an important contribution, but its performance varies strongly by site and wavelength. For example, the Beijing SSA correlation decreases at longer wavelengths. The manuscript notes the narrow dynamic range of SSA values, which is reasonable, but a more detailed discussion would be useful. The authors could consider showing the SSA value distributions, sample sizes, or uncertainty ranges by wavelength to support this interpretation.
- The chronological split within each month is a good attempt to reduce temporal leakage. However, because aerosol conditions can be strongly autocorrelated, adjacent observations may still be similar. The authors may consider adding a more stringent sensitivity test, such as day-based, week-based, or event-based splitting, to demonstrate that the model is not mainly learning local short-term persistence.
- The manuscript suggests that the method can help establish dense aerosol observation networks and bridge gaps between satellites and ground stations. This is a promising direction, but the current validation is limited to two sites and selected time periods. I suggest slightly moderating these claims and presenting them as future potential, pending further validation across more environments, seasons, instruments, and aerosol types.
- The reported correlation, RMSE, and MAE provide useful validation metrics, but additional uncertainty analysis would strengthen the manuscript. For example, the authors could discuss how retrieval errors depend on solar zenith angle, aerosol loading, season, image brightness, or instrument type. This would be particularly helpful if the method is intended for future low-cost aerosol monitoring networks across diverse environments.
Citation: https://doi.org/10.5194/egusphere-2026-2019-RC2
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Review for “An AI Based Algorithm for Retrieving Aerosol Optical Depth and Single Scattering Albedo Using All-Sky Imager Observations" by Ni et al.
This study presents a machine learning (XGBoost) approach to retrieve Aerosol Optical Depth (AOD) and Single Scattering Albedo (SSA) from all-sky imager (ASI) data, using collocated AERONET measurements as training targets. The method is tested at two contrasting sites (Beijing, China and Southern Great Plains, USA) and demonstrates promising skill, particularly for AOD. The work addresses an important gap—low-cost, high-temporal-resolution aerosol monitoring—and offers a clear advancement over traditional radiative transfer-based methods by avoiding iterative inversion.
Overall, the manuscript is well-structured, the methodology is sound, and the results are presented clearly. However, several issues—ranging from methodological transparency to the interpretation of SSA performance—should be addressed before publication.
Major comments:
Minor Comments
The caption for Figure 1 lists (a)-(d) but the text refers to (a-c) and (d-f) in the following paragraph. Please check and harmonize the labels. Also, the source attribution for (c,d) is given, but not for (a,b) – add "source: authors" for clarity.
Section 2.3 mentions "iterative threshold segmentation" (citing Anon, 1978; Gonzalez and Faisal, 2019) but does not specify the actual threshold criterion (e.g., Otsu? fixed percentile?). Given that cloud masking quality directly affects sky radiance extraction, please provide the specific algorithm or pseudo-code, or at least cite a more recent and relevant ASI cloud masking method.
The authors apply a 5-minute smoothing to the ASI retrievals in Figure 6 but then report statistics on unsmoothed data. This is acceptable, but the magnitude of high-frequency noise should be characterized (e.g., standard deviation of the difference between smoothed and unsmoothed). Without this, readers cannot assess whether the fine-scale variability in Figure 6a is signal or noise.
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