Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data and machine learning
High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks. However, current carbon satellites have a narrow swath and uneven observation points, making it difficult to obtain seamless and full-coverage data. We propose a novel method combining extreme gradi...
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| Autores principales: | , , , , , , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Elsevier
2024-09-01
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| Colección: | International Journal of Applied Earth Observations and Geoinformation |
| Materias: | |
| Acceso en línea: | http://www.sciencedirect.com/science/article/pii/S1569843224004710 |
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