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Peer-reviewedCC BYEnvironmental monitoring and assessment

Gap filling in long-term AOD time series using Iterative Singular Spectrum Analysis at a tropical AERONET site.

  • Tiago Schmidt1,2
  • Rafael da Silva Palácios3,4
  • Amaury de Souza5
  • Leone Francisco Amorim Curado3
5 affiliations
  1. 1Department of Teaching, Federal Institute of Education, Science and Technology of Mato Grosso, Rua das Avencas, Sinop, 78550-178, Mato Grosso, Brazil. [email protected].
  2. 2Graduate Program in Environmental Physics, Federal University of Mato Grosso, Avenida Fernando Corrêa da Costa, Cuiabá, 78060-900, Mato Grosso, Brazil. [email protected].
  3. 3Graduate Program in Environmental Physics, Federal University of Mato Grosso, Avenida Fernando Corrêa da Costa, Cuiabá, 78060-900, Mato Grosso, Brazil.
  4. 4Institute of Geosciences, Federal University of Pará, Rua Augusto Corrêa, Belém, 66075-110, Pará, Brazil.
  5. 5Physics Department, Federal University of Mato Grosso do Sul, Avenida Costa e Silva, Campo Grande, 79070-900, Mato Grosso do Sul, Brazil.
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Abstract

Aerosol optical depth (AOD) is widely used in studies related to atmospheric monitoring, climate variability, and air quality, with the Aerosol RObotic NETwork (AERONET) providing high-quality records. However, AERONET AOD time series frequently contain observational gaps associated with cloud cover, especially in tropical regions characterized by strong hydrological seasonality. In this study, the performance of Iterative Singular Spectrum Analysis (ISSA) was evaluated for gap-filling an incomplete AOD time series from the AERONET Cuiabá-Miranda station (AOD-AERONET) in the Central-West region of Brazil. Daily AOD-AERONET data between 2005 and 2024 were used, with approximately 47% of the daily observations identified as gaps and showing a predominantly seasonal distribution. The application of ISSA to the gap-filling of the AOD series involved the evaluation of different initialization and cross-validation strategies using model performance metrics, as well as different reconstruction parameters. The results showed that the climatological initialization, based on the Day of the Year (DOY) mean, outperformed the global-mean initialization, producing smaller errors and greater temporal consistency of the reconstruction, and that the cross-validation design plays an important role in the selection of optimal parameters.

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Cite

BibTeXRIS
BibTeX
@article{schmidt2026filling,
  title = {{Gap filling in long-term AOD time series using Iterative Singular Spectrum Analysis at a tropical AERONET site.}},
  author = {Tiago Schmidt and Rafael da Silva Palácios and Amaury de Souza and Leone Francisco Amorim Curado},
  journal = {Environmental monitoring and assessment},
  year = {2026},
  month = oct,
  doi = {10.1007/s10661-026-16067-6},
  url = {https://europepmc.org/article/MED/42957834},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Gap filling in long-term AOD time series using Iterative Singular Spectrum Analysis at a tropical AERONET site.
AU  - Tiago Schmidt
AU  - Rafael da Silva Palácios
AU  - Amaury de Souza
AU  - Leone Francisco Amorim Curado
PY  - 2026
DA  - 2026/10/30
JO  - Environmental monitoring and assessment
DO  - 10.1007/s10661-026-16067-6
UR  - https://europepmc.org/article/MED/42957834
AB  - Aerosol optical depth (AOD) is widely used in studies related to atmospheric monitoring, climate variability, and air quality, with the Aerosol RObotic NETwork (AERONET) providing high-quality records. However, AERONET AOD time series frequently contain observational gaps associated with cloud cover, especially in tropical regions characterized by strong hydrological seasonality. In this study, the performance of Iterative Singular Spectrum Analysis (ISSA) was evaluated for gap-filling an incomplete AOD time series from the AERONET Cuiabá-Miranda station (AOD-AERONET) in the Central-West region of Brazil. Daily AOD-AERONET data between 2005 and 2024 were used, with approximately 47% of the daily observations identified as gaps and showing a predominantly seasonal distribution. The application of ISSA to the gap-filling of the AOD series involved the evaluation of different initialization and cross-validation strategies using model performance metrics, as well as different reconstruction parameters. The results showed that the climatological initialization, based on the Day of the Year (DOY) mean, outperformed the global-mean initialization, producing smaller errors and greater temporal consistency of the reconstruction, and that the cross-validation design plays an important role in the selection of optimal parameters.
SN  - 0167-6369
SN  - 1573-2959
ER  -