Physics-constrained transfer learning with a spectral-fidelity-preserving model for satellite remote sensing applications
- 1China Meteorological Administration (Beijing, China)
Abstract
Accurate spectral transformation across satellite sensors with similar but different spectral response functions (SRFs) is essential for applying the same retrieval algorithms. A novel physics-constrained transfer learning (TL) framework is developed for transferring satellite radiance observations across different sensors while preserving physical consistency. It integrates a core Spectral-Fidelity-Preserving (SFP) model based on extensive radiative transfer simulations, allowing broad adaptability for radiance transformation under diverse satellite observational conditions. Sensitivity experiments demonstrate the robustness of the TL framework relating to radiometric calibration uncertainties, particularly in infrared (IR) channels, and further highlight the critical role of SRF similarity between sensors. We further establish practical SRF-similarity thresholds – an allowable scaling-factor range and central-wavenumber-shift limits that are stricter for IR window channels – that support reliable radiance transfer (see Sect. 3.2 for details). Application to radiance observations from Fengyun-4A/B (FY-4A/B) geostationary (GEO) satellites explicitly indicates that the TL approach improves retrieval accuracy for key geophysical parameters such as cloud amount profile and quantitative precipitation estimation, when compared with results obtained without applying TL. Thus, the TL approach enhances cross-satellite data consistency and provides a practical tool for operational satellite data applications (e.g., transferring algorithms developed for FY-4A to FY-4B without operational interruption). It is worth noting that the FY-4A/FY-4B experiments presented here demonstrate retrieval-model transfer, validated against independent downstream satellite products. Beyond retrieval-model transfer, the framework can also support direct radiometric calibration transfer by propagating an absolute calibration reference at the radiance level between different spaceborne imagers.
Versions
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BibTeX
@article{min2026physicsconstrained,
title = {{Physics-constrained transfer learning with a spectral-fidelity-preserving model for satellite remote sensing applications}},
author = {Min Min and Qiang Yu and Jun Li and Han Lin and Yunheng Xue and Xinran Xia and Di Di and Bo Li and Peng Zhang},
journal = {Atmospheric Chemistry and Physics},
year = {2026},
month = oct,
doi = {10.5194/acp-26-14205-2026},
url = {https://acp.copernicus.org/articles/26/14205/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Physics-constrained transfer learning with a spectral-fidelity-preserving model for satellite remote sensing applications
AU - Min Min
AU - Qiang Yu
AU - Jun Li
AU - Han Lin
AU - Yunheng Xue
AU - Xinran Xia
AU - Di Di
AU - Bo Li
AU - Peng Zhang
PY - 2026
DA - 2026/10/09
JO - Atmospheric Chemistry and Physics
DO - 10.5194/acp-26-14205-2026
UR - https://acp.copernicus.org/articles/26/14205/2026/
AB - Accurate spectral transformation across satellite sensors with similar but different spectral response functions (SRFs) is essential for applying the same retrieval algorithms. A novel physics-constrained transfer learning (TL) framework is developed for transferring satellite radiance observations across different sensors while preserving physical consistency. It integrates a core Spectral-Fidelity-Preserving (SFP) model based on extensive radiative transfer simulations, allowing broad adaptability for radiance transformation under diverse satellite observational conditions. Sensitivity experiments demonstrate the robustness of the TL framework relating to radiometric calibration uncertainties, particularly in infrared (IR) channels, and further highlight the critical role of SRF similarity between sensors. We further establish practical SRF-similarity thresholds – an allowable scaling-factor range and central-wavenumber-shift limits that are stricter for IR window channels – that support reliable radiance transfer (see Sect. 3.2 for details). Application to radiance observations from Fengyun-4A/B (FY-4A/B) geostationary (GEO) satellites explicitly indicates that the TL approach improves retrieval accuracy for key geophysical parameters such as cloud amount profile and quantitative precipitation estimation, when compared with results obtained without applying TL. Thus, the TL approach enhances cross-satellite data consistency and provides a practical tool for operational satellite data applications (e.g., transferring algorithms developed for FY-4A to FY-4B without operational interruption). It is worth noting that the FY-4A/FY-4B experiments presented here demonstrate retrieval-model transfer, validated against independent downstream satellite products. Beyond retrieval-model transfer, the framework can also support direct radiometric calibration transfer by propagating an absolute calibration reference at the radiance level between different spaceborne imagers.
SN - 1680-7324
ER -