OPEN ACCESS

Articles online

A Method to Improve MODIS AOD Values: Application to South America

Category: Optical/Radiative Properties and Remote Sensing

Volume: 16 | Issue: 6 | Pages: 1509-1522
DOI: 10.4209/aaqr.2015.05.0375
PDF | RIS | BibTeX

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

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


Related Article

An Improved Aerosol Optical Depth Map Based on Machine-Learning and MODIS Data: Development and Application in South America

Bethania L. Lanzaco, Luis E. Olcese , Gustavo G. Palancar, Beatriz M. Toselli
Volume: 17 | Issue: 6 | Pages: 1623-1636
DOI: 10.4209/aaqr.2016.11.0484
PDF

Thirteen Years of Aerosol Radiative Forcing in Southwestern Iberian Peninsula

Maria A. Obregón , Maria J. Costa, Ana M. Silva, Antonio Serrano
Accepted Manuscripts
DOI: 10.4209/aaqr.2017.05.0159
PDF

Particle Size Distribution of Soot from a Laminar/Diffusion Flame

Jian Wu, Linghong Chen , Jianwu Zhou, Xuecheng Wu, Xiang Gao, Gérard Gréhan, Kefa Cen
Volume: 17 | Issue: 8 | Pages: 2095-2109
DOI: 10.4209/aaqr.2017.06.0216
PDF

Dynamic Monitoring of the Strong Sandstorm Migration in Northern and Northwestern China via Satellite Data

Qinghua Su, Lin Sun , Yikun Yang, Xueying Zhou, Ruibo Li, Shangfeng Jia
Article In Press
DOI: 10.4209/aaqr.2016.12.0600
PDF
;