Bethania L. Lanzaco, Luis E. Olcese , Gustavo G. Palancar, Beatriz M. Toselli

  • INFIQC - CONICET / CLCM / Departamento de Fisicoquímica. Facultad de Ciencias Químicas. Universidad Nacional de Córdoba. Ciudad Universitaria, 5000 Córdoba, Argentina

Received: May 29, 2015
Revised: September 14, 2015
Accepted: November 13, 2015
Download Citation: ||https://doi.org/10.4209/aaqr.2015.05.0375  

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Cite this article:
Lanzaco, B.L., Olcese, L.E., Palancar, G.G. and Toselli, B.M. (2016). A Method to Improve MODIS AOD Values: Application to South America. Aerosol Air Qual. Res. 16: 1509-1522. https://doi.org/10.4209/aaqr.2015.05.0375


HIGHLIGHTS

  • The method is based on machine learning techniques (ANN and SVR).
  • The obtained AOD values better reproduce AERONET measurements in South America.
  • In more than 90% of the cases the residuals were lower than the MODIS error.
  • The systematic deviations and the outliers of MODIS measurements were corrected.

 

ABSTRACT


We present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs.

The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation.

The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed.

Considering the results as a whole, this method showed to be a valuable tool to enhance MODIS AOD retrievals, especially for locations with systematic deviations.


Keywords: Support Vector Regression; Artificial Neural Networks; AOD satellite retrieval; MODIS AOD bias correction; AERONET


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