Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas DOI Creative Commons
Lu Fan, Yong Wan,

Yongshou Dai

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11100 - 11100

Published: Nov. 28, 2024

Methane (CH4) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from oil and industry amounted 3.586 million tons, representing 13.24% total ranking second among all emission sources. To effectively control in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on concept fusion, targeting high-emission areas such as fields. The aim construct an XCH4 dataset that meets requirements for high resolution, wide coverage, accuracy. Initially, products GOSAT satellite TROPOMI sensor are matched both spatially temporally. Subsequently, variables longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), month incorporated. Using local random forest (LRF) resulting product combines accuracy with coverage data. On basis, ΔXCH4 derived using GF-5. Combined GFEI prior inventory, high-precision output by LRF redistributed grid areas, producing 1 km resolution product, thereby constructing high-precision, high-resolution regions. Finally, challenges emerged were discussed summarized, it was envisioned that, future, advancement technology algorithms, would be possible obtain more accurate datasets concentration apply range fields, expectation significant contributions could made reducing combating change.

Language: Английский

Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas DOI Creative Commons
Lu Fan, Yong Wan,

Yongshou Dai

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11100 - 11100

Published: Nov. 28, 2024

Methane (CH4) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from oil and industry amounted 3.586 million tons, representing 13.24% total ranking second among all emission sources. To effectively control in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on concept fusion, targeting high-emission areas such as fields. The aim construct an XCH4 dataset that meets requirements for high resolution, wide coverage, accuracy. Initially, products GOSAT satellite TROPOMI sensor are matched both spatially temporally. Subsequently, variables longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), month incorporated. Using local random forest (LRF) resulting product combines accuracy with coverage data. On basis, ΔXCH4 derived using GF-5. Combined GFEI prior inventory, high-precision output by LRF redistributed grid areas, producing 1 km resolution product, thereby constructing high-precision, high-resolution regions. Finally, challenges emerged were discussed summarized, it was envisioned that, future, advancement technology algorithms, would be possible obtain more accurate datasets concentration apply range fields, expectation significant contributions could made reducing combating change.

Language: Английский

Citations

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