## 2 Methodology and Data
### High Precision and Wide Range XCH4 Fusion Dataset Construction
Additionally, the challenge of spatiotemporal matching, due to differences in satellite observation times and orbits, poses a critical issue that can lead to inconsistencies and gaps in the fused datasets, affecting their reliability. The computational demands associated with processing high-resolution data further complicate the task, requiring substantial resources and advanced computational techniques to perform detailed matched filtering and redistribution operations over large areas. Moreover, the uncertainty inherent in model selection and optimization adds another layer of difficulty, as different models may yield varying results depending on their approach to data handling, making it challenging to ensure that the models accurately reflect real-world conditions. Compounding these issues is the scarcity of ground-based validation data, particularly in regions with high oil and gas emissions, where the limited distribution of TCCON and other ground stations hampers the ability to robustly assess the accuracy of satellite-derived products. Looking ahead, advances in multi-source data fusion algorithms and the deployment of next-generation hyperspectral satellites are expected to significantly improve the spatiotemporal resolution and coverage of XCH\({}_{4}\) datasets. The development of advanced machine learning techniques, such as deep learning and transfer learning, can enhance the capability to integrate diverse satellite datasets more effectively. These algorithms can learn complex patterns and relationships between different data sources, enabling more accurate and reliable XCH\({}_{4}\) datasets. On the satellite technology front, upcoming missions like NASA's MethaneSAT and the European Space Agency's Sentinel-5P and future Sentinel-5 satellites promise higher resolution and more frequent observations of methane concentrations globally. These satellites are equipped with advanced sensors that offer improved spectral and spatial resolution, significantly enhancing the precision of methane monitoring efforts. Collaboration with international methane monitoring networks will also be crucial. Integrating satellite observations with data from ground-based networks like the Total Carbon Column Observing Network (TCCON) and incorporating measurements from drones and aircraft can fill spatial and temporal gaps in satellite data. This integration provides high-resolution local observations essential for identifying emission hotspots and validating satellite-derived products. These developments will enhance the capacity for real-time methane monitoring, particularly in oil and gas regions. The integration of satellite data with ground-based observations, drones, and aircraft will also provide richer validation datasets, further increasing the reliability and precision of the fused products. Global collaboration and data sharing will be crucial in establishing standardized and unified XCH\({}_{4}\) datasets, which can be applied across various sectors beyond the oil and gas industry, including agriculture and waste management. Such datasets will be instrumental in supporting global efforts to monitor and reduce methane emissions, contributing to broader climate change mitigation strategies. As technology continues to advance and collaborative efforts expand, the construction of high-resolution XCH\({}_{4}\) datasets through multi-source satellite fusion will play an increasingly pivotal role in achieving global carbon reduction goals. By leveraging enhanced fusion algorithms, next-generation satellites, and comprehensive observational networks, the research community can significantly contribute to global efforts aimed at reducing methane emissions and combating climate change. As technology continues to advance and collaborative efforts expand, the construction of high-resolution XCH\({}_{4}\) datasets through multi-source satellite fusion will play an increasingly pivotal role in achieving global carbon reduction goals.