The manuscript titled “The development of an operational system for estimating irrigation water use reveals socio-political dynamics in Ukraine” introduces a system that leverages remote sensing data to estimate irrigation water use. This system is notably valuable for providing crucial data in water resource management, such as for modeling and monitoring. The application of this system is demonstrated using a timely case study of the Russian-Ukraine conflict. However, while the system shows great promise, the manuscript falls short in certain areas:

• The background research that led to the development of the system is inadequately detailed, particularly regarding the estimation of irrigation water use.
• The methodology section is too concise, lacking a comprehensive introduction or synthesis (if some methods are previously published) of the key methods or algorithms employed in this system.

I recognize the potential of the system proposed, but believe the manuscript requires substantial improvement to effectively showcase this important work. Given the extent of revisions needed, I recommend rejecting its current version. However, I strongly encourage resubmission of a revised version. I provided detailed thoughts and suggestions below which might guide these improvements.

First, the introduction section of the manuscript currently offers a limited synthesis of the research context. While there is an abundance of references to studies in remote sensing algorithms, the manuscript fails to adequately address the background in estimating irrigation water use. Given that the primary objective of your system is to estimate and monitor irrigation water use, the absence of a thorough discussion on the research gaps in existing systems for estimating irrigation water use significantly weakens the presentation of your proposed system. To enhance this aspect, I recommend the following revisions for the introduction: (1) incorporate a comprehensive literature review that emphasizes the significance of estimating irrigation water use in water resource management and identifies existing research gaps; (2) establish a clear connection between these identified research gaps and your proposed system, highlighting how your system aims to bridge these gaps.

Second, Section 3 on Materials and Methods currently provides insufficient information for readers. The section appears to predominantly describe the necessary data and data sources, yet it neglects to adequately detail the critical methods or algorithms that underpin the system. Consequently, while the data requirements for the system are clear, its structure (such as the system components, modules, and data flows) and the core algorithms that facilitate its operation remain obscure. Additionally, the results and discussion sections indicate the application of the system to a real-world scenario for demonstration purposes, yet the method section lacks a comprehensive overview of the chosen case area. I recommend that the authors thoroughly reevaluate and substantially revise the methodology section to provide a clearer and more detailed presentation of the proposed system. This revision should aim to explain in detail for both the system architecture and its foundational algorithms, as well as incorporate a more detailed description of the case study area within the methodological framework.

I would like to reiterate that the topic and the proposed system are both valuable and of great interest to me. However, the current presentation of the manuscript does not do justice to the value of the work. It is essential that the content and delivery are refined to effectively convey the significance of your research. I look forward to having the opportunity to review a revised and resubmitted version of this manuscript in the future, hoping it will fully reveal the potential and importance of the work.

REVIEW: egusphere-2023-2479

This study used data from satellite soil moisture, reanalysis precipitation and potential evaporation to estimate irrigation water use. The topic of this study is in general interesting. However, there are some issues that need to be addressed with the connection to introduction, materials and methods, discussions, and potential uncertainties or caveats of the results from this study. I feel that these points need substantialimprovements, and, thus, I would recommend rejecting its current form.

General comments:

Introduction: The background and necessity of conducting this research are not adequately introduced. It is very different for readers to understand the how this topic has been developed in the community, and what is the innovation of the current study. I do not think that „no operational services for monitoring large-scale irrigation are available“ can be the research gap for the scientific journal of HESS.

Study area: There are lack of enough information about the study area, for example, climate, agriculture, or societal and political situation.

Materials and Methods: A detailed description of the SM-based inversion approach is need in this section for readability.

Page 3, Line 70: Could the authors explain the meaning of “scaling value” and why 30% is used.

Results and discussion:

Page 3, Line 75-80: I think that this paragraph would fit in the methods section.

Page 3, Line 82: How many years are selected for deriving Figure 1?

Page 6, Line 121: I would like to encourage the authors to validate your irrigation results derived from satellites against ground observations.

In general, I have not seen thoughtful discussion in this section.

Conclusions:

I agree with the authors that advancements in high-resolution satellite technology and new high-resolution productions, particularly, irrigation water use, are needed. However, the analysis and discussion in this study are not adequately support for these conclusions.

In this work, the authors discuss the importance of building an operational system for monitoring water-use quantity for large-scale irrigation purposes (for example, during high-impact dam-destructions, big-scale pandemics or wars), through satellite images, with spatial resolution of 1 km, of soil-moisture, precipitation and potential evapotranspiration (through ERA5 reanalysis data and GLEA models), and with application south of Kakhova in Ukraine and during the period 2015-2023. Although the idea seems interesting, further analysis and validation are required to support it. Please see some major issues that I hope they can be of help to the authors:

1) I would recommend the Abstract and Introduction focus on the idea of "developing an operational system for monitoring water-use quantity for large-scale irrigation purposes", and mention to the end something like that "to support this idea, we select for application the largest scale events of the last decade in Europe, which is the COVID and the conflict in Ukraine that led to the destruction of high-impact irrigation dam", since there seems to be much emotion and focus on the application area (that, although justified, it is not the purpose of this paper).

2) I do not comprehend the sentence "Concurrently, a few studies deepened the role of the evapotranspiration term within the algorithm structure"; please consider further explaining it or maybe rephrasing it, and present what exactly these other studies' advantages and disadvantages.

3) Please add more information for the study area (for example, why the dam collapsed, dimensions of the dam and its reservoir, temperature, PET, precipitation, soil-moisture, streamflow, etc.), so that the readers are familiarized with the area's social, climatic and hydrological conditions.

4) It is mentioned that "To assess irrigation water use from satellite observations of soil moisture (or evaporation), the observations must detect the increase in soil water associated with irrigation application."; however, it is not clear what exactly the methodology is, and how evaporation can be used instead of the soil-moisture. Please consider presenting the methodology in further detail and how exactly one can estimate soil-moisture or evaporation from the satellite image processing.

5) Some of the main conclusions of this study (i.e., "Consequently, we can confidently stipulate that Sentinel-1 soil moisture data is capable of detecting the irrigation signal in space with good precision." or "The possible impact of COVID-19 pandemic is also highlighted.") are not fully supported, in my opinion, from the analysis, due to (a) the small range of data from 2015-2023 (at least 30 years of data is required to include the intrinsic uncertainty of the key hydrological-cycle processes, traced in their short-term dependence and long-term persistence), and (b) more data and different climatic and seasonal conditions need to be examined to support the above conclusions and exclude other factors that may result in the same impacts and similar images (for example, how do you take into account the type of irrigation and land-use in the area; is there a change during the 2015-2023 period?).