High-Resolution Daily XCH<sub>4</sub> Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data

High-Resolution Daily XCH<sub>4</sub> Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data

Atmospheric methane (CH<sub>4</sub>) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH<sub>4</sub> is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This...

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Bibliographic Details
Main Authors: Mohamad M. Awad, Saeid Homayouni
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Atmosphere
Subjects:
XCH<sub>4</sub>
monitoring
CNN
global
remote sensing
prediction
Online Access:https://www.mdpi.com/2073-4433/16/7/806
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Summary:Atmospheric methane (CH<sub>4</sub>) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH<sub>4</sub> is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH<sub>4</sub>) is a crucial indicator for assessing atmospheric CH<sub>4</sub> levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH<sub>4</sub> concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).
ISSN:2073-4433

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