Estimating monthly China XCO2 based on DQ-1 data and spatiotemporal heterogeneous filtering model
Satellite remote sensing is vital for global CO2 monitoring, yet active remote sensing suffers from discontinuous coverage despite overcoming passive technique limitations in cloudy or low-light conditions. Although geostatistical and machine-learning methods have been applied to XCO2 reconstruction...
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| Główni autorzy: | , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
Taylor & Francis Group
2026-01-01
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| Seria: | International Journal of Digital Earth |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.tandfonline.com/doi/10.1080/17538947.2026.2668778 |
| Etykiety: |
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