Dear Dr. Wolf and co-authors,

Thank you for developing this framework to address the cloud influence on ground reflectance measurements. Indeed, in the remote sensing community, our field measurements are highly dependent on the illumination conditions, frequently altered by clouds. I have several minor remarks for your consideration.

In eq. 1, 4, 6 what do you mean by the ‘sr’ argument? Pi is already assumed to be in steradian (sr) units, cancelling steradian in the upwelling radiance I.

A bit on the same line, multiplication by pi suggests that the surface reflects homogeneously in all directions, Lambertian reflectance. How big would you expect the influence of the directionality of the actual surface to be on the reflectance value?

On the SCOPE model (section 2.2.2 and Table 2).

• First of all, it is unclear why SCOPE was chosen instead of SAIL or INFORM. The latter is more suitable for pine forest (L163) simulations, as it explicitly has the concept of trunks and branches in it. SCOPE has energy balance, thermal domain, photosynthesis and chlorophyll fluorescence that other models do not have. Using it as a tool for a single reflectance simulation is overkill. Nonetheless, your choice.
• Why was only half of the important SCOPE input parameters chosen? The BSM model, for example, has brightness, two shape parameters and moisture content, but only B is shown in Table 2. In any case, those parameters are set to their default values so it is also not clear why highlighting them at all.
• Finally, could you please be more explicit about which SCOPE output was integrated with the libRadtran output and how? I am a bit confused because L152 says “As an initial guess of the surface albedo in libRadtran, the "mixed-forest" albedo was taken from the IGBP data base.” According to my understanding, it was sufficient to take some spectral forest reflectance for the exercise instead of running an RTM.

Figure 2b. Please, add a legend.

L187 – “Wolf et al. (2024) have shown the influence of clouds on direct and diffuse F↓(λ), the associated effects on F↑(λ),” please, write exactly what the influence was. I guess more clouds – more diffuse radiation.

Figure 3. What do grey areas show? Sentinel-2 bands?

Figures 3 and 4 captions. Please, note, you are working with synthetic (modelled) data. Remove the term “measured” reflectance; do not mislead the readers.

Figure 5. Please, check the location of symbols inside the heatmaps Whereas for NDVI (circle) lambda1 and lambda2 are matching the expected NIR and RED, NDWI1240 is definitely far from lambda1=1240 nm. Furthermore, the symbol in Figures 5c and 5d around lambda1=900nm, lambda2=1600nm is unclear (or absent from the legend).

This manuscript reports on the effect of clouds on the derivation of vegetation indices from remote sensing platforms in a radiative transfer modelling approach. Basically it is a sensitivity study on how clouds contribute to the incoming radiation and what effect it might have on the calibration conditions for deriving the reflectance values and consequently on the computation of the various VI with Sentinel 2 bands as example for different solar zenith angles.

Overall, it is an interesting and relevant study since it brings our attention to the basics of using reflectance values and its impact on derived vegetation indices. It is important that we do not forget the basics of RS data. The preprint is very well written and well-documented, with nice figures.

To my opinion, the authors should elaborate a bit more on the consequences of the effect on biases in values of VI’s. They briefly touch the impact on LAI derived from EVI, but the authors should add some paragraph how it could impact remotely sensed derived biophysical properties such as GPP, green water fluxes etc with proper referencing. This would make the paper even more relevant. Perhaps, indicating some of the effects as percentages can increase the visibility.

In lines 117, 306 and others, the authors directly link NDVI to vegetation health. I would refrain from that, since NDVI essentially says something about the “greenness” of the vegetation, but not necessarily on its health. It can be used for vegetation health, but it is not synonymous for health.

Furthered, I only have few minor/textual comments/

- Normally, numbers less than 10 are written as text; So less than one; equals one, etc

- If one uses for instance 0.29, also use 0.20 and not 0.2 (see L338, but also elsewhere): always use the same amount of decimals for the same property;

- I do not know the policy of the journal, but normally the cited references in the text are first ordered chronologically and then alphabetically;

- Abstract: add more on possible consequences;

- Captions Fig 1. Replace “‘relate” with “connect”;

- L30: specify what tau is under the text of Eq 1; see lines 45-47; this should come earlier in the text;

- L61: “attempts”? This is not a proper use of the word here;

- L67: In general, it is a “transfer function” rather than a factor, although used as a factor;

- L76: Should be “requires frequent calibrations of the transfer function”;

- L217: remove “the“ before Appendix A;

- L224: “valueS”;

- L270-271: “ARE close to zero”;

Figure 5: Why is the symbol of NDII missing in the upper right panel?

- L281, 324 and other lines: I would refrain of using the term “exemplary”; it echoes a bit as “exemplary behaviour or punishment”; Perhaps use “illustrates”?

- L345: Should be “The effect of changes in fdir”;

- L378-379: “… was between 0 and 40,” and “.. ranged …”; Remove “were covered”:

- L406: remove comma after NDWI

- L407: twice increase, increasing;