Remote Sensing Approaches for Methane Greenhouse Gas Monitoring: A Review of Satellite and Airborne Techniques

Background and Objectives: Monitoring greenhouse gases, particularly methane, is of great importance due to its impact on climate change and global warming. Methane, after carbon dioxide, is the second most significant greenhouse gas resulting from human activities. Due to its high ability to absorb and retain heat, methane plays a crucial role in increasing global temperatures. Studies indicate that over a 20-year period, methane's warming effect is approximately 84 times greater than that of carbon dioxide. Therefore, identifying emission sources and accurately monitoring methane at local, regional, and global scales is essential for developing effective strategies to mitigate climate change. There are two main approaches to methane monitoring: the bottom-up method, which relies on local measurements and emission inventories, and the top-down method, which is based on remote sensing and inverse modeling. The top-down method, utilizing remote sensing technologies such as satellite and airborne sensors, enables the identification and analysis of methane concentrations on large scales. This method is particularly valuable in areas where ground-based data is insufficient. In this study, remote sensing technologies used for methane monitoring, including thermal infrared sensors, shortwave infrared spectroscopy, and LiDAR, are examined, and their advantages and limitations are analyzed.



Materials and Methods: The aim of this study was to investigate methane monitoring methods through top-down approach and remote sensing data. For this purpose, extensive library studies have been conducted to identify different technologies of satellite and airborne sensors. These technologies include thermal infrared, shortwave infrared, and lidar sensors. In this study, the combination of data obtained from different sensors has been investigated in order to improve the accuracy and reduce the uncertainties associated with each method.



Results and Discussion: The results of this study show that remote sensing technologies, particularly shortwave infrared and LiDAR, are powerful tools for large-scale methane monitoring. Thermal infrared and shortwave infrared sensors, due to their high spectral sensitivity, can identify and measure methane concentration in the atmosphere. However, they face challenges such as spectral interference of methane with other atmospheric gases, including water vapor and carbon dioxide, which affect measurement accuracy. On the other hand, LiDAR, due to its ability to provide three-dimensional data and directly measure methane concentration, offers higher accuracy compared to other remote sensing methods. However, its high cost and the need for advanced equipment are among the limitations of this method. One of the main challenges in using these technologies is the spatial and temporal resolution limitations of satellite sensors. Although these sensors enable large-scale methane monitoring, they face difficulties in detecting point-source emissions, such as small gas leaks from oil and gas facilities. Environmental and atmospheric barriers also pose challenges in methane monitoring using remote sensing. Cloud cover, aerosols, and atmospheric water vapor can absorb and scatter radiation in infrared bands, reducing measurement accuracy. Additionally, inconsistencies in inverse modeling parameters, such as atmospheric temperature and pressure, can increase systematic errors in estimating methane concentration. To mitigate these challenges, the integration of multi-source data from satellite, airborne, and ground-based sensors has been proposed.



Conclusion: Methane monitoring using the top-down approach and remote sensing data, particularly through satellite and airborne systems, plays a crucial role in global surveillance of methane emissions and assessing its impact on climate change. Technologies such as shortwave infrared, thermal infrared, and LiDAR allow for the identification of emission sources, estimation of methane concentration, and large-scale monitoring. However, these methods face challenges such as spatial and temporal resolution limitations, susceptibility to atmospheric conditions, and complexities in data modeling. The development of more advanced sensors, improvements in radiative transfer models, and the integration of machine learning-based technologies in remote sensing data processing can help reduce these challenges and enhance the accuracy of methane monitoring. Ultimately, the combination of multi-source data from satellite, airborne, and ground-based sensors, along with improved data analysis algorithms, can enhance monitoring accuracy, improve emission source identification, and provide more precise estimates of methane emissions.