Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing

Abstract

Atmospheric dispersion modeling is a critical tool in environmental research, offering insights into spatial and temporal patterns of pollutants. This study introduces an innovative approach leveraging remote sensing technology to analyze and predict methane (CH₄) levels, specifically focusing on Qatar. Utilizing data from the Sentinel-5P satellite, captured through the Tropospheric Monitoring Instrument (TROPOMI), this research presents a detailed examination of methane concentrations. The methodology includes generating daily, monthly, and yearly average images, alongside Sobel gradient images to enhance the analysis of daily and monthly variations. A thresholding technique is applied to each month's data to identify critical methane concentration levels. Moreover, the study extends to forecasting methane levels for the latter half of 2024 and the entirety of 2025. These predictions are rigorously validated by comparing the predicted methane concentrations with observed data, resulting in a Root Mean Square Error (RMSE) that underscores the model's predictive accuracy. The R-squared (R²) value further demonstrates the model's robustness, particularly in scenarios where conventional prediction methods would be hampered by incomplete datasets. This research not only advances the understanding of methane dynamics in arid regions but also illustrates the potential of remote sensing as a cost-effective alternative to traditional data-intensive approaches. The accompanying Python code, detailed in the Appendix, is made publicly available to facilitate further research and application in similar environmental studies.