| Abstrakt: |
In the global ecosystem, wetlands are vital carbon sinks, playing a crucial role in absorbing greenhouse gases such as carbon dioxide and mitigating global warming. Accurate estimation of wetland carbon content is essential for research on wetland carbon sinks. However, the carbon cycle of wetlands is complex, and the carbon sinking of wetlands is affected by climate, topography, water level conditions, vegetation types, soil types, and other factors. This has caused significant challenges in the estimation of wetland carbon sinks. In current studies, most research has focused on the impact of individual factors on wetland carbon sinks, often ignoring the interaction between various factors, which further leads to uncertainty in wetland carbon measurements. This paper aims to elucidate the process of the wetland carbon cycle, summarize the factors affecting wetland carbon sinks, and explore the interplay between various factors and their influence on wetland carbon sinks, aiming to provide theoretical support for the study of wetland carbon sinks. Additionally, this paper reviews the advantages and disadvantages of current wetland carbon measurement methods, proposes research directions for combining machine learning methods, identifies existing difficulties in current wetland carbon measurement, and offers suggestions to serve as a reference for future wetland carbon sink estimation and wetland management.Highlights: The potential and significance of wetland carbon sinks are explained. The advantages and disadvantages of current wetland carbon measurement techniques are summarized. The prospective method for integrating machine learning into integrated wetland carbon measurement is proposed. [ABSTRACT FROM AUTHOR] |
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