Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: a case study in Jiangsu province, China
With the continuous rise in global carbon dioxide concentrations, accurately assessing regional carbon stocks has become essential for achieving carbon neutrality. Traditional models, reliant on extensive experimental data, are costly and impractical for large-scale applications. Methods based on la...
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| Hoofdauteurs: | , , , , , , |
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| Formaat: | Artikel |
| Taal: | Engels |
| Gepubliceerd in: |
Taylor & Francis Group
2025-12-01
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| Reeks: | International Journal of Digital Earth |
| Onderwerpen: | |
| Online toegang: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2534008 |
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