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SHORT COMMUNICATION
Reconstruction on High-Resolution XCO2
Spatiotemporal Distribution in Sichuan
Province Using ResNet-LSTM Model
1
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute,
Chengdu 610095, China
2
College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
Submission date: 2024-08-11
Final revision date: 2024-09-23
Acceptance date: 2024-10-13
Online publication date: 2024-12-04
Corresponding author
Han Zhang
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
KEYWORDS
CO2 concentrationmulti-source satellite data fusionResNet-LSTMhigh-resolution spatiotemporal distributionSichuan province
TOPICS
ABSTRACT
Sichuan Province plays a crucial role in China’s efforts to achieve carbon neutrality, making it
essential to accurately assess the spatial and temporal distribution of carbon dioxide (CO2) concentrations
in the region. This study develops a ResNet-LSTM model to address the spatiotemporal fusion of multisource
satellite data, specifically integrating CO2 dry air column-averaged mole fraction (XCO2) data
from GOSAT, OCO-2, and OCO-3 satellites. By reconstructing the daily spatiotemporal distribution
of XCO2 at a 1 km resolution for Sichuan Province from 2015 to 2022, the model fills gaps in satellite
observations caused by meteorological conditions and other factors. The results demonstrate significant
improvements in accuracy, with the ResNet-LSTM model achieving an R² value of 0.97, outperforming
traditional models like XGBoost and Random Forest. The high-resolution XCO2 data provides a robust
foundation for validating local emission inventories and supports the formulation of scientifically sound
carbon reduction strategies. This study contributes to regional and national carbon neutrality efforts
by offering valuable insights into carbon emission dynamics and promoting sustainable low-carbon
development.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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