Overall Assessment

This manuscript presents a physics-constrained transfer learning framework centered on a Spectral-Fidelity-Preserving model for transferring satellite radiance observations between sensors with similar but non-identical spectral response functions. Its key innovation is to derive transfer coefficients from radiative-transfer-simulated and SRF-convolved radiance pairs, rather than relying solely on empirical regression using satellite matchups. The sensitivity analysis in Section 3 is comprehensive, combining distributional similarity metrics, fitting diagnostics, and analytical uncertainty propagation to assess the joint effects of SRF differences and calibration uncertainty. The two case studies in Section 4, cloud amount profile retrieval using CLANN and quantitative precipitation estimation using an UNet framework, further demonstrate that models trained on FY-4A data can be transferred to FY-4B observations without retraining. The proposed quantitative thresholds for SRF scaling and central-wavenumber shifts are also practically useful for satellite calibration and cross-sensor algorithm transfer. Overall, the manuscript is well designed and technically sound, but several issues should still be addressed before publication.

Specific Comments

1. The direction implied by "target" and "reference" is not fully consistent across sections. Eq. (3) treats *y* (the output) as the target-sensor radiance and *x* (the input) as the reference-sensor radiance, whereas Section 4.1 calls the FY-4B input the "target" channel and the FY-4A-equivalent output "reference" — the reverse assignment. The authors themselves note in Section 3.3 that Figure 9 uses the opposite convention from Figure 3. Stating one consistent convention in Section 2.2, and flagging it explicitly wherever it is deliberately reversed, would resolve this.
2. Readers may expect "physics-constrained" to mean a physical penalty term added to a loss function, but here physical consistency is instead built into the training data itself, via the radiative-transfer simulations (Section 2.1) and the SRF-convolution step (Eq. 4). Stating this explicitly would also clarify why the framework reduces SRF-driven bias between sensors. A brief note on why R² ≥ 0.95 and *n* ≤ 4 were chosen in Section 2.2, and how many simulated pairs enter each channel's fit, would also help.
3. The manuscript touches on three related applications — spectral harmonization, retrieval-model transfer, and radiometric calibration transfer — without clearly distinguishing them. The FY-4A/FY-4B experiments here mainly demonstrate the first two, while the calibration-transfer claim relies more on the earlier FY-3D/MERSI-II study. A short clarification in the Abstract and Conclusions would make this distinction, and the manuscript's practical claims, more precise.
4. Eqs. (17)–(21) and (A20)–(A21) propagate an assumed perturbation in the target channel through a fixed transformation, while uncertainty in the fitted coefficients, SRF characterization, and RTM simulations is implicitly treated as negligible. "Bias," "error," and "uncertainty" are also used somewhat interchangeably. A short paragraph near Eq. (17) or in Section 3.3 stating the scope of this derivation would be enough to resolve this, without a new analysis.
5. The SRF-similarity guidance (scaling factor 0.5–1.5; wavenumber shift below 200 cm⁻¹ for VIS/NIR, below 20 cm⁻¹ for IR window channels) is repeated in near-identical wording in the Abstract, Section 3.2, and Discussion point (2). Keeping the full statement in Section 3.2 and shortening the other two to a brief cross-reference would reduce this redundancy.
6. In Section 4.1, the sentence "Consequently, previous work did not attempt to develop an FY-4B/AGRI-based CLANN retrieval model" appears twice in immediate succession, each followed by a similar remark. This reads as a leftover from editing; removing the duplicate would tighten the paragraph.
7. In Section 3.3, the sentence "Fig. 4 further indicates that when the radiometric calibration bias becomes large (e.g., reaching 6%)..." appears to reference the wrong figure: Figure 4 shows simulated SRF adjustments, whereas the 1%–6% error range described matches Figure 9's caption instead. The authors are encouraged to verify and correct this cross-reference.
8. In Eq. (15), "Upper Fence" is defined as Q25 − 1.5×IQR and "Lower Fence" as Q75 + 1.5×IQR, which is the reverse of standard convention (upper fence = Q75 + 1.5×IQR; lower fence = Q25 − 1.5×IQR). The authors are encouraged to correct this labeling.
9. There is a date discrepancy between the text and Figure 11: Section 4.2 gives Case 1 as "11 June 2024" and the extrapolation period as ending in "July 2024," while the Figure 11 caption gives "11 July 2024" and an end date of "August 2024." The authors are encouraged to reconcile these dates.
10. In Section 4.2, the Multi-Task UNet model is attributed to "Jaegle et al., 2021," which in the reference list corresponds to the Perceiver architecture rather than a U-Net. The authors are encouraged to verify this citation.
11. The same ~13.30 μm channel is labeled "Channel 15" (FY-4B convention) in the Figure 3 caption but "Channel 14" (FY-4A convention) in Section 3.3. A brief note clarifying the channel-number correspondence between the two sensors would prevent confusion.
12. A few language points warrant a check: the Abstract's opening sentence has a subject–verb agreement issue ("transformation ... are essential" should be "is essential"); "compared those without applying TL" is missing a preposition; and "F-4A" appears to be a typo for "FY-4A."

Review of “Physics-Constrained Transfer Learning with a Spectral-Fidelity-Preserving Model for Satellite Remote Sensing Applications”

This study presents a physics-constrained transfer learning (TL) framework for transforming satellite radiance observations across sensors with different spectral response functions (SRFs). The core component is a Spectral-Fidelity-Preserving (SFP) model built on MODTRAN-based radiative transfer simulations, which derives polynomial transfer coefficients between spectrally similar channels on different satellite imagers. The framework is evaluated through sensitivity experiments on SRF discrepancies and radiometric calibration uncertainties, and demonstrated through cloud amount profile and quantitative precipitation estimation using FY-4A and FY-4B AGRI observations.

The concept of grounding cross-sensor transfer learning in radiative transfer physics rather than purely data-driven approaches is sound and practically relevant, particularly for operational satellite transitions (e.g., FY-4A to FY-4B). The sensitivity analysis quantifying the impact of SRF width scaling and central wavelength shifts on transfer performance is a useful contribution, and the empirical guidelines (scaling factor 0.5–1.5, JSD ≤ 0.3) provide actionable criteria for operational users.

Overall, the manuscript is well-structured, although several minor concerns and corrections are still needed. I therefore recommend minor revisions prior to acceptance, listed as follows.

1. The FY-4B/AGRI against CALIPSO validation period for the cloud amount profile experiment is limited to approximately 10 months (June 2022–March 2023). While the TL model reduces the RMSE by 0.08 km and improves the correlation coefficient from 0.841 to 0.851 (lines 645–646), these improvements are relatively modest. Given the limited validation sample, the authors should discuss whether these differences are statistically significant. Reporting confidence intervals, a paired t-test, or a bootstrap significance test on the RMSE/R differences would substantially strengthen the claim that the TL framework yields a "measurable improvement" (line 648). Without such analysis, it is difficult to determine whether the observed gains exceed the natural sampling variability.
2. The authors separate the theoretical framework into VIS/NIR (Eq. 1) and TIR (Eq. 2) regimes, and the error propagation analysis (Section 3.3) is similarly divided between reflectance-based (Eqs. 20–21) and BT-based (Eqs. A20–A21) formulations. However, the 3.75 µm channel included in the CLANN retrieval model (line 607) contains both reflected solar and thermal emissive contributions during daytime. This dual-source nature means that neither the pure VIS/NIR nor the pure TIR transfer model fully captures the channel's radiative behavior under all illumination conditions. It is suggested to briefly discuss how the SFP model and the transfer coefficients handle this mixed channel, particularly whether the MODTRAN simulations adequately capture the solar reflection component at 3.75 µm across the range of solar geometries in Table 1, and whether the transfer performance degrades for daytime observations at this wavelength.
3. The manuscript provides several empirically derived guidelines for applying the TL framework, scattered across Sections 3.2, 3.3, and 5. These include: SRF scaling factor within 0.5–1.5, central wavenumber shift below 200 cm⁻¹ for VIS/NIR channels, below 20 cm⁻¹ for IR atmospheric window channels (e.g., 10.80 µm), and JSD ≤ 0.3 as a transferability threshold. I suggest providing a concise summary table consolidating these recommended thresholds, for example, organized by channel type (VIS/NIR, water vapor IR, atmospheric window IR) with the corresponding metric, threshold value, and consequence of violation. This would improve the manuscript's practical utility and would serve as a quick-reference guide for potential users.
4. The manuscript states that "the classical MODTRAN (Version 4.2) model (Berk and Hawes, 2017)" is employed (line 296–297). However, the cited reference (Berk and Hawes, 2017) describes the validation of MODTRAN 6, not Version 4.2. Please make sure that the version of MODTRAN used is correctly cited.
5. Lines 625–629 contain a near-identical repeated sentence: "Consequently, previous work did not attempt to develop an FY-4B/AGRI-based CLANN retrieval model" appears twice within five lines. Please clarify.

More technical issues:

- Line 92: "adopt algorithms of F-4A to FY-4B": "F-4A" should read "FY-4A."

- Line 93: The keyword list ("Radiative transfer, transfer learning, Physics-Constrained") uses inconsistent capitalization. Consider standardizing to either title case or sentence case throughout.

- Table 1: The surface type "Forest" uses "P" (Ρ = 0.08–0.83) instead of "ρ".

- The manuscript would benefit from a brief notation table or consistent first-use definitions for the numerous abbreviations (AGRI, SFP, SRF, BT, TL, FTL, MTL, FL, UDA, SSL, etc.), as the density of acronyms in the Introduction may challenge readers from adjacent fields.