Spatio-temporal data fusion for very large remote sensing datasets
Developing global maps of carbon dioxide (CO2) mole fraction (in units of parts per million) near the Earth’s surface can help identify locations where major amounts of CO2 are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse e...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
16 May 2014
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| In: |
Technometrics
Year: 2014, Jahrgang: 56, Heft: 2, Pages: 174-185 |
| ISSN: | 1537-2723 |
| DOI: | 10.1080/00401706.2013.831774 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/00401706.2013.831774
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| Verfasserangaben: | Hai Nguyen, Matthias Katzfuss, Noel Cressie & Amy Braverman |
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| 520 | |a Developing global maps of carbon dioxide (CO2) mole fraction (in units of parts per million) near the Earth’s surface can help identify locations where major amounts of CO2 are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse effect of atmospheric CO2. Existing satellite remote sensing data do not provide measurements of the CO2 mole fraction near the surface. Japan’s Greenhouse gases Observing SATellite (GOSAT) is sensitive to average CO2 over the entire column, and NASA’s Atmospheric InfraRed Sounder (AIRS) is sensitive to CO2 in the middle troposphere. One might expect that lower-atmospheric CO2 could be inferred by differencing GOSAT column-average and AIRS mid-tropospheric data. However, the two instruments have different footprints, measurement-error characteristics, and data coverages. In addition, the spatio-temporal domains are large, and the AIRS dataset is massive. In this article, we describe a spatio-temporal data-fusion (STDF) methodology based on reduced-dimensional Kalman smoothing. Our STDF is able to combine the complementary GOSAT and AIRS datasets to optimally estimate lower-atmospheric CO2 mole fraction over the whole globe. Further, it is designed for massive remote sensing datasets and accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. This article has supplementary material online. | ||
| 650 | 4 | |a EM algorithm | |
| 650 | 4 | |a Fixed rank smoothing | |
| 650 | 4 | |a Kalman filter | |
| 650 | 4 | |a Multivariate geostatistics | |
| 650 | 4 | |a Spatial random effects model | |
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| 700 | 1 | |a Braverman, Amy |e VerfasserIn |4 aut | |
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