Wind-Robust Methane Source-Rate Inversion from Remote-Sensing Plume Imagery: Soft Physics Guidance Versus Hard IME Coupling.
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| Title: | Wind-Robust Methane Source-Rate Inversion from Remote-Sensing Plume Imagery: Soft Physics Guidance Versus Hard IME Coupling. |
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| Authors: | Dong, Quanyi1 (AUTHOR), Duan, Sining1,2 (AUTHOR), Chen, Zhigang1,3 (AUTHOR), Li, Yue2,4 (AUTHOR), Zhao, Shuhe1,3 (AUTHOR) zhaosh@nju.edu.cn, Ye, Fanghong2,4 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1992. 23p. |
| Subjects: | Remote sensing, Large eddy simulation models, Windstorms |
| Abstract: | Highlights: What are the main findings? The simplified hard IME-style forward pathway is highly sensitive to wind perturbations and can produce unstable predictions under the tested stochastic wind-noise protocol. Soft physics guidance remains competitive under clean benchmark inputs and modestly improves plume-aware spatial consistency. What are the implications of the main findings? Within this LES-based benchmark, physical knowledge is more robust when used as a calibratable soft prior than as the simplified hard log-additive coupling tested here. The results provide benchmark-level design evidence for airborne and satellite stand-off methane-plume quantification workflows, but real-scene transfer still requires validation. Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The simplified hard IME-style forward pathway is highly sensitive to wind perturbations and can produce unstable predictions under the tested stochastic wind-noise protocol. Soft physics guidance remains competitive under clean benchmark inputs and modestly improves plume-aware spatial consistency. What are the implications of the main findings? Within this LES-based benchmark, physical knowledge is more robust when used as a calibratable soft prior than as the simplified hard log-additive coupling tested here. The results provide benchmark-level design evidence for airborne and satellite stand-off methane-plume quantification workflows, but real-scene transfer still requires validation. Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121992 |
