An AI Based Algorithm for Retrieving Aerosol Optical Depth and Single Scattering Albedo Using All-Sky Imager Observations

Ni, Heyang; Li, Jing; Chang, Liang; Dong, Yueming; Du, Guanghao; Li, Muqian; Li, Qiurui; Liu, Guanyu; Sun, Yuebo; Tian, Angnuo; Yue, Sheng; Zhang, Chongzhao; Zhang, Zhenyu

doi:10.5194/egusphere-2026-2019

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https://doi.org/10.5194/egusphere-2026-2019

Preprints

21 Apr 2026

| 21 Apr 2026

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

An AI Based Algorithm for Retrieving Aerosol Optical Depth and Single Scattering Albedo Using All-Sky Imager Observations

Heyang Ni, Jing Li, Liang Chang, Yueming Dong, Guanghao Du, Muqian Li, Qiurui Li, Guanyu Liu, Yuebo Sun, Angnuo Tian, Sheng Yue, Chongzhao Zhang, and Zhenyu Zhang

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.

Received: 09 Apr 2026 – Discussion started: 21 Apr 2026

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.

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Heyang Ni, Jing Li, Liang Chang, Yueming Dong, Guanghao Du, Muqian Li, Qiurui Li, Guanyu Liu, Yuebo Sun, Angnuo Tian, Sheng Yue, Chongzhao Zhang, and Zhenyu Zhang

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RC1:
'Comment on egusphere-2026-2019', Anonymous Referee #1, 09 Jun 2026 reply
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:
While the method has been applied to two independent sites, Beijing and SGP, I noticed different versions of AERONET data were used. Specifically, the authors state that Level 2.0 SSA retrievals at Beijing are too sparse due to strict quality control, so they use Level 1.5 instead. But Level 1.5 SSA certainly has larger uncertainty, the authors should explain how this uncertainty impact the retrieved SSA results, and if possible, provide some quantified estimation of the retrieved SSA uncertainty.

The practice to simultaneous retrieve AOD and SSA from ASI is indeed novel. Butthe SSA performance—especially at Beijing (r as low as 0.33 at 1020 nm, Fig. 4 and Table 1)—is relatively weak. I wonder how this performance compares with existing SSA products, perhaps from satellites or other remote sensing techniques? Such comparison is important in understanding the capability and limitions of ASI in retrieving aerosol properties.

Minor Comments
1. Inconsistent figure labeling in caption of Figure 1

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.
3. Lack of detail on the cloud and lens flare masking algorithm

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.
4. The diurnal cycle analysis (Section 3.3) would benefit from a quantification of the 5-minute smoothing effect.

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.
5. Typographical and formatting issues
Line 30: "Almuantar" should be "almucantar"

Line 135: "XGBoost" appears twice in close succession – rephrase for readability.

Table 1: The column headers "440nm, 675nm..." lack spaces (use "440 nm" for consistency with text).

Line 195: "alucantar" → "almucantar"

Reference "Anon, 1978" – please replace with the actual author names (it is a well-known paper by Ridler and Calvard or similar).

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Citation: https://doi.org/10.5194/egusphere-2026-2019-RC1

Heyang Ni, Jing Li, Liang Chang, Yueming Dong, Guanghao Du, Muqian Li, Qiurui Li, Guanyu Liu, Yuebo Sun, Angnuo Tian, Sheng Yue, Chongzhao Zhang, and Zhenyu Zhang

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Short summary

This study develops a new artificial intelligence method to retrieve daytime aerosol optical parameters using low-cost All-Sky Imagers. By training a model with high-precision data, we successfully estimated the aerosol optical depth and single scattering albedo simultaneously. Our results show high accuracy across different global regions. This approach is much faster than traditional complex calculations, making it easier to deploy dense observation networks to improve climate research.


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