Articles | Volume 19, issue 11
https://doi.org/10.5194/amt-19-3687-2026
https://doi.org/10.5194/amt-19-3687-2026
Research article
 | 
04 Jun 2026
Research article |  | 04 Jun 2026

Synergistic Fusion of Aerosol Optical Depth over India from multi-sensor satellite retrievals with ground-based measurements

Shiba Shankar Gouda, Mukunda M. Gogoi, and S. Suresh Babu

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Cited articles

Babu, S. S., Manoj, M. R., Moorthy, K. K., Gogoi, M. M., Nair, V. S., Kompalli, S. K., Satheesh, S. K., Niranjan, K., Ramagopal, K., Bhuyan, P. K., and Singh, D.: Trends in aerosol optical depth over Indian region: Potential causes and impact indicators, J. Geophys. Res.-Atmos., 118, 11794–11806, https://doi.org/10.1002/2013JD020507, 2013. 
Bai, K., Li, K., Shao, L., Li, X., Liu, C., Li, Z., Ma, M., Han, D., Sun, Y., Zheng, Z., Li, R., Chang, N.-B., and Guo, J.: LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics, Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, 2024. 
Baisad, K., Chutsagulprom, N., and Moonchai, S.: A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression, Mathematics, 11, 4799, https://doi.org/10.3390/math11234799, 2023. 
Basart, S., Pérez, C., Cuevas, E., Baldasano, J. M., and Gobbi, G. P.: Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations, Atmos. Chem. Phys., 9, 8265–8282, https://doi.org/10.5194/acp-9-8265-2009, 2009. 
Brereton, R. G. and Lloyd, G. R.: Support Vector Machines for classification and regression, Analyst, 135, 230–267, https://doi.org/10.1039/b918972f, 2010. 
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Short summary
This study presents fused aerosol optical depth (AOD) from a combination of single-view and multi-angle space-borne sensors with ground-based observations across India using Universal Kriging (UK) and a novel hybrid Residual Kriging–Machine Learning (RK-ML) approach. Both methods improve aerosol representation compared to individual datasets. UK-based fused maps highlight the need for better ground coverage, addressed by the RK-ML approach under data-sparse conditions.
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