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

Abstract

Abstract. Accurate spectral transformation across satellite sensors with similar but different spectral response functions (SRFs) are 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. Specifically, the scaling factor between the SRFs of the target and reference channels should be constrained within the range of 0.5 – 1.5. Meanwhile, the shift in central wavenumber should remain below 200 cm⁻¹ for visible or near IR channels, and more strictly below 20 cm⁻¹ for infrared window channels (e.g., 10.80 µm). Applying 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 those without applying TL. Thus, the TL approach enhances cross-satellite data consistency and provides a practical tool for operational satellite data applications (e.g., adopt algorithms of F-4A to FY-4B without operational interruption).