Search

Methane (CH4) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH4 is emitted from storage tanks. It is crucial to spatially explore the CH4 emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH4 emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on high spatial resolution images (1–2m) from the GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan-3 satellites over cities in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset over 92 typical cities in China. The dataset can be accessed at https://zenodo.org/records/10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14,461 storage tanks, wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation referring to the collected high spatial resolution images, historical high spatial resolution images of Google Earth, and field survey. The inventory comprises storage tanks having various distribution patterns in different cities. Spatial consistency analysis with CH4 emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH4 emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH4 release and aid in climate change mitigation efforts. Our proposed dataset STD will foster the accurate estimation of CH4 released from storage tanks for CH4 control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements. [ABSTRACT FROM AUTHOR]