This work presents crop modeling using the AquaCrop model within NASA’s Land Information System Framework (LISF). First, the authors describe how crop growth is simulated in the AquaCrop model. Then, they present three experiments that inform new LISF features in parameter perturbation and data assimilation. They find that updated crop parameters based on satellite estimates of land surface phenology improve canopy cover simulations. Using data from crop states in winter wheat fields in the Piedmont region of Italy, biomass and canopy cover improved, though crop yield did not. The manuscript is well written. However, some comments need to be addressed before it can be considered for publication.  

Comments:

Line 5. Please specify the coarse-scale, for example: coarse-scale (>10km?)

For the showcase 2, I don’t understand how exactly to perform the perturbations on shortwave radiation, precipitation, soil moisture. In my understanding, perturbations are used to understand the model sensitivity to the changes of forcing data or other targets. Typically, large ensemble simulations are performed with an increase or decrease of a target (using precipitation as an example) to understand how crop growth responds to variations in precipitation. Some studies perform such perturbations one at a time to understand the impact of a single perturbation on the simulation. In Table 2, I see that SW and P are multiplied by a standard deviation ratio. Is that correct? If so, then SW and P are both decreased and soil moisture is increased. This type of perturbation seems odd to me because it only shows how the crops respond to decreased SW and P. Why do these perturbation experiments matter for the study?

Line 290. Why is a perturbation bias correction is needed here? Again, in my understanding, perturbation experiment is just to vary the forcing data to an acceptable range and see how crop growth repones to these changes.

Line 292. What are these 24 members? With which perturbation combinations?

Line 294. Why use only three years results?  

For Showcase 3, I think the goal is to compare the DA results and the original results, why was the ensemble model performed? What is the relationship between the ensemble runs in showcase 2 and 3? I suggest deleting the OL results because they distract from the main points of Showcase 3.

Line 354. “The years 2017 through 2023”  why the simulations are carried out for these years? Is it because of observation availability?

Table 3. Please explain why DA does not generate a better yield simulation.

Figure 10. Please show a plot for obs vs Det.

Line 482. Could you perform process-based deeper analysis to confirm there are parameter constraints.

The work by De Lannoy et al. applies the AquaCrop model within NASA’s Land Information System Framework (LISF) to support high-performance crop modeling and scalable satellite data assimilation (DA). The capabilities of this integrated system are demonstrated through three exploratory showcases: (1) coarse-scale simulations across Europe, where incorporating spatially varying Growing Degree Day (GDD) crop parameters, derived from satellite-based phenology estimates, improved canopy cover (CC) simulations compared to uniform calendar-day parameters; (2) coarse-scale ensemble simulations revealing that uncertainties in root-zone soil moisture and biomass (B) are greater in water-limited regions; and (3) fine-scale satellite DA using an Ensemble Kalman Filter (EnKF) over winter wheat fields in Italy, where CC state updating benefited biomass estimates but provided limited improvements in yield estimates. Overall, the authors concluded that the resulting computational efficiency is encouraging for advancing regional-scale crop modeling and DA.

The manuscript is well written and addresses an important application. In the review below, I outline several suggestions and related comments that the authors might consider to help with the interpretation and readability of the manuscript. These comments are provided in two categories; (1) page- and line-specific comments, and (2) general comments related to the conceptual clarity of broader sections.

(1) Comments specific to Page and Line:

Page 7. Lines 177-178. The statement from “The entire…” to “…simulation year.”

This statement appears to imply that this condition holds for both GDD and Calendar-day configurations. However, I don't think this is the case when AquaCrop is run in calendar-day mode. It would be helpful if the authors could clarify this distinction and explicitly state that the described behavior pertains only to the GDD configuration.

Page 8. Figure 3.

The parameters related to rooting depth evolution (Zn and Zx) did not appear to be defined in the text prior to this figure. It would be helpful to clarify their meaning in the caption (Zn: minimum rooting depth and Zx: maximum rooting depth).

In addition, I understand that the crop stage timings derived from the GLSP product correspond to points A through F, and these should align with AquaCrop’s crop stages, as they represent equivalent physiological events. However, in Figure 3, the GLSP points did not appear to align temporally with the corresponding AquaCrop’s crop stages (with the exception of point A, which matched the (I) time to emergence). Could the authors clarify whether this misalignment is due to the visualization, or whether there is a conceptual reason for the offset?

Page 9. Lines 213-215. The statement from “Furthermore, only….” to “…future versions.”

Are these parameters (i.e., spatially variable vs. uniform parameters) listed anywhere in the manuscript? It would be helpful for readers if these parameters were explicitly stated.

Page 10. Lines 231-233. The statement from “The most…” to “…and HIo.”

First, this statement would be stronger if supported by one or more references. Ideally, the authors might cite studies that have conducted sensitivity analyses for AquaCrop and identified these parameters as the most important crop parameters. As it stands, making this claim without supporting evidence or analysis is not fully convincing.

Second, there appears to be a typo, GCG should be CGC (Crop Growth Coefficient). The acronym is used as CGC earlier in the manuscript, so this should be corrected here.

Page 10. Lines 250-251. The statement from “The mapping…” to “…in Appendix A.”

This comment is related to the earlier comment raised for Page 8, Figure 3, but with an additional point that requires clarification.

Table 1 clearly explained the relationships between AquaCrop and GLSP crop stages; however, in Figure 3, these parameters did not align on the same points on the x-axis. AquaCrop’s crop stages consistently appeared earlier than the corresponding GLSP, except for one stage. It would be helpful if the authors could clarify whether this misalignment is intentional (e.g., due to methodological reasons) or a visualization issue.

Additionally, the definition of stage “II. Time to maximum rooting depth” being set at 0.7*D needs further explanation, why was 0.7 chosen specifically? What is the basis for this proportion?

Page 12. Line 285. The statement “….an ensemble of 24 members.”

It would be helpful to provide a brief explanation (either here or in an appendix) of how the perturbations to the meteorological forcing and soil moisture state variables generated the ensemble of 24 members. A short description of the perturbation scheme or the combination logic would greatly improve clarity for readers.

Page 12. Lines 290-291. The statement from “A perturbation…” to “…simulation.”

To clarify the workflow: was the perturbation bias correction applied before generating and running the ensemble members?

Page 12. Lines 294-295. The statement from “…multi-year average” to “…croplands in Europe.”

Given the description of Section 3.2.2, I was anticipating that the authors might report the multi-year average ensemble standard deviation for CC, alongside biomass and root-zone soil moisture.

While I recognized that the primary objective of this showcase is biomass (as stated on Page 9, Line 221), the inclusion of root-zone soil moisture, despite it not being explicitly part of the stated objective, suggests that the analysis is not limited strictly to B. Was there a reason that CC was excluded from this uncertainty assessment?

Page 12. Lines 297-299. The statement from “The spread…” to “…water content (RSW).”

The explanation of the spread in B as directly resulting from perturbations in soil moisture and radiation (through ETo) is conceptually sound - but doesn't it simplify AquaCrop’s internal behavior? As a process-based model, AquaCrop includes multiple threshold-driven stress functions and nonlinear feedbacks among canopy cover, transpiration, and biomass. Consequently, the ensemble variability in B likely reflects not only the imposed input perturbations but also the model’s inherent stress-response interactions.

The statement that “The ensemble spreads of soil moisture and B are related to each other, and to environmental conditions, more specifically to RSW” would benefit from additional mechanistic clarification. Although RSW is a physically meaningful indicator of soil wetness, AquaCrop’s stress functions translate this variable into nonlinear physiological responses. Hence, the relationship between B and soil moisture spreads cannot be attributed to RSW alone without considering these embedded physiological thresholds and nonlinear responses. Clarifying this connection would likely make the interpretation more consistent with AquaCrop’s process-based structure.

Page 13. Table 2.

I could not find where the choice of probability distributions for the ensemble perturbations (normal for additive variables & lognormal for multiplicative variables) was explicitly justified in the manuscript. It would be useful to explain the rationale behind these selections, as the assumed distribution directly influences the shape and magnitude of the propagated uncertainty.

It was also a little unclear whether the perturbed parameter values remain within physically plausible ranges. Depending on the perturbation amplitude and distribution, it is possible that the sampled values may fall outside the realistic domain for the study region. Such unrealistic values could introduce artificial spread in the outputs and potentially complicate interpretation of the resulting uncertainty patterns. Clarifying whether constraints, bounds, or validity checks were applied to the perturbed parameters would help readers assess the robustness of the ensemble design.

Page 13. Lines 321-323. The statement from “The cumulative…” to “…assimilation frequency.”

It is unclear whether this was implemented in your experiment or just mentioned conceptually.

Page 14. Lines 329-330. The statement from “Except for…” to “…updating in AquaCrop.”

I believe there are at least two published studies that have reported on the assimilation of satellite-based fractional vegetation cover into AquaCrop for CC state updating. For example:

https://www.mdpi.com/2073-4395/11/11/2265

https://www.mdpi.com/2073-4395/9/7/404

The authors might want to review these works and perhaps revise the statement on Page 14, Lines 329-330. 

Page 14. Lines 346-347. The statement from “moisture state variables…” to “…in Table 2.”

Was there a reason why soil moisture state variables were excluded from the perturbation setup in Showcase 3? Although Section 4.4 provides justification, specifically the lag between soil and crop responses, it would strengthen the clarity of this section if that rationale were also mentioned here.

Page 15. Equation 11.

Equation 11 lists both CC and B as state variables updated by the EnKF. However, as far as I understood, FCOVER was assimilated primarily to update CC. It would be helpful if the authors clarified how B is handled during the update step: is B explicitly updated by the EnKF, or does it adjust only indirectly through model propagation after CC is updated?

This point is related to the earlier comment on Page 13, Lines 321-323, and addressing both would improve clarity regarding the DA update mechanism.

Page 15. Lines 383-387. The statement from “At this…” to “…the year 2018.”

There is no issue having this qualitative evaluation of AquaCrop estimates - but to make the evaluation more meaningful, you might also include some quantitative error metrics, such as RMSE, bias, or other standard performance measures, to assess the discrepancy between AquaCrop time-series estimates and the independent satellite data. If possible, incorporating these metrics would likely strengthen the evaluation and provide a clearer picture of how well the model performs at coarse scale.

Page 16. Line 391. The statement “…responds to the temperature pattern.”

The authors might consider including a map (either into Figure 5 or placed in an appendix) illustrating the temperature pattern referenced here.

Page 16. Lines 397-398. The statement from “For ΔB…” to “….part of the domain.”

For CC, the manuscript provided possible explanations for the strong performance reduction in Norway, but no similar interpretation was offered for ΔB. The authors might like to discuss some of the potential reasons why simulations using GDD crop parameterization did not yield improvements in other regions of Europe (particularly western and northern areas).

(2) General Comments:

Appendix C. AquaCrop Crop Parameters.

What is the difference between the parameter (Total length of crop cycle in growing degree-days) and the parameter (GDDays: from transplanting to maturity)?

Based on their physical meaning, they appear to represent the same concept, which suggests they should be assigned the same value. However, in the second column of the table (Generic GDD), the authors gave Total length of crop cycle in growing degree-days a value of 3123 GDD, while GDDays: from transplanting to maturity was assigned F, meaning each had a different value. This differentiation did not appear in the third column (Winter wheat GDD), where both inputs had the same value (2694 GDD). Might be helpful if this were clarified. 

Showcase 1. Crop Parameterization.

The authors described how inputs related to climate, crop, soil, and field management were parameterized (for field management; only one input - soil fertility level). However, there was no explanation of how other inputs, such as those related to irrigation management, groundwater table, or additional field management, were handled? How were these inputs/parameters parameterized, especially given that the simulations were conducted at a coarse scale where substantial spatial variation exists across the study area?

Showcase 3. Satellite-based Data Assimilation.

A simple exploration of the rationale for choosing fractional vegetation cover (FCOVER) as the assimilated variable in AquaCrop i.e. why was FCOVER selected specifically, rather than other variables - might prove useful here.