Machine-Learning Emulation of Satellite Greenhouse Gas Retrievals: Stability over Time

2026-06-08

AI Summary

This study addresses the high computational cost of traditional satellite-based greenhouse gas inversion algorithms, which hinders real-time applications, and the limited temporal stability of existing machine learning surrogates in cross-temporal prediction. To overcome these challenges, the authors propose a time-augmented machine learning proxy model and present the first systematic evaluation of temporal generalization capability in inversion models. They demonstrate that incorporating temporal features significantly improves long-term prediction accuracy for column-averaged methane (XCH₄) and carbon dioxide (XCO₂) concentrations. Experiments using GOSAT satellite and TCCON ground-based observations show that the time-enhanced Lasso regression model not only outperforms more complex neural networks but also achieves prediction errors comparable to the inherent discrepancies between GOSAT and TCCON measurements, highlighting its exceptional cross-temporal stability.

Abstract

Retrieval algorithms are used to estimate atmospheric concentrations of greenhouse gases (GHGs), such as carbon dioxide (CO2) and methane (CH4), by solving inverse problems from high-spectral-resolution satellite radiance measurements. However, these algorithms are computationally expensive, which makes real-time estimation at scale difficult. Machine-learning models have therefore been proposed as fast emulators of retrieval algorithms. Most existing studies, however, evaluate them only on test data from the same period as the training data. We study the stability over time of such emulators using data from the Greenhouse Gases Observing SATellite (GOSAT). We show that prediction accuracy generally deteriorates when the test period moves away from the training period. We also show that including time as an input feature substantially improves XCH4 prediction for Lasso and neural-network models. Among the methods considered, a simple Lasso model performs as well as or better than more complex methods such as neural networks, and yields more stable predictions over time. We further validate the results using the Total Carbon Column Observing Network (TCCON), a ground-based observation network. On the TCCON-matched dataset, the time-augmented Lasso achieves errors against TCCON that are comparable to the disagreement between GOSAT and TCCON for both XCO2 and XCH4.

Problem

• machine learning emulation
• satellite greenhouse gas retrievals
• temporal stability
• XCO2
• XCH4

Innovation

• machine learning emulation
• temporal stability
• greenhouse gas retrieval
• Lasso regression
• satellite remote sensing