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Wetland carbon storage plays an essential role in the global carbon cycle. However, in recent decades, intensive human activities and rapid urbanization have reduced wetland C stocks in the Guangdong&#x2013;Hong Kong&#x2013;Macau Greater Bay Area (GBA). Long-term assessment of carbon storage in the wetland ecosystems of the GBA is needed for promoting regional sustainable development. Therefore, we proposed a framework integrating the integrated valuation of ecosystem services and tradeoffs (InVEST) model and water inundation frequency, which was retrieved via multiple index water detection rule based on Google Earth Engine platform, to estimate yearly carbon storage in wetlands across the GBA at a 30-m spatial resolution during 1995&#x2013;2020. The incorporation of water inundation frequency in estimating carbon storage in wetlands presents features of long time series and higher temporal frequency. The results showed that: first, the carbon storage of wetlands estimated by the InVEST model decreased from 33.38 to 16.64 Tg&#x00B7;C between 1995 and 2020 in the GBA, mainly owning to the loss of paddy fields. Second, there is a significant exponentially decreasing relationship between water inundation frequency and carbon storage density within the potential wetland extent, with the <italic>R</italic><sup>2</sup> reaching 0.998, which denotes huge potential for estimating wetland carbon storage; third, annual wetland carbon storage estimated were divided into three main stages: a rapidly increasing period (1995&#x2013;2004), a falling period (2005&#x2013;2013), and a slightly fluctuating period (2015&#x2013;2020). This research may provide references for supporting decision-making in implementing wetland-related sustainable development and carbon-neutrality policies in the GBA.

ieee journal of selected topics in applied earth observations and remote sensing

Assessing and Characterizing Carbon Storage in Wetlands of the Guangdong&#x2013;Hong Kong&#x2013;Macau Greater Bay Area, China, During 1995&#x2013;2020