Name
Abstract | ||
|---|---|---|
This paper describes a data mining approach for improving the accuracy of aerosol retrieval algorithms. The approach was applied on 1,722 collocated MODIS and AERONET observations over the western part of the continental United States. Neural networks were trained to predict AERONET Aerosol Optical Thickness (AOT) using attributes derived from observations made by MODIS instrument onboard the TERRA satellite. The results showed that neural networks provide more accurate retrievals than the operational MODIS algorithm. A study of differences between neural networks and the MODIS algorithm revealed useful information that can help domain scientists improve quality of the MODIS algorithm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IGARSS.2006.635 | 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 |
Keywords | DocType | ISSN |
aerosols, retrieval, MODIS, AERONET, data mining, neural networks, decision trees | Conference | 2153-6996 |
Citations | PageRank | References |
2 | 0.46 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Han | 1 | 20 | 3.23 |
| Zoran Obradovic | 2 | 1110 | 137.41 |
| Zhanqing Li | 3 | 35 | 13.98 |
| Slobodan Vucetic | 4 | 637 | 56.38 |