Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 19
Articles | Volume 26, issue 19
https://doi.org/10.5194/acp-26-14205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-14205-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 19
Research article
 | 
09 Oct 2026
Research article |  | 09 Oct 2026

Physics-constrained transfer learning with a spectral-fidelity-preserving model for satellite remote sensing applications

Min Min, Qiang Yu, Jun Li, Han Lin, Yunheng Xue, Xinran Xia, Di Di, Bo Li, and Peng Zhang

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Short summary
A physics‑constrained transfer learning framework enables accurate radiance transformation across satellite sensors with different spectral response functions (SRFs). It integrates a Spectral‑Fidelity‑Preserving model from radiative transfer. Tests confirm robustness to calibration uncertainties and define SRF constraints. FY‑4A/B application improves cloud and precipitation retrievals, enabling operational algorithm transfer.
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