General assessment

The manuscript tackles an important and timely topic: evaluating state-of-the-art gridded soil moisture (SM) products in natural ecosystems of central and southern Chile. The study is original, methodologically robust, and provides valuable insights into the performance of SM datasets in under-monitored areas of the Southern Hemisphere. The combination of standard statistical performance metrics with event-based soil moisture signatures (rising time and amplitude) is a notable strength and introduces novelty. Overall, the manuscript is well-prepared and deserves publication after significant revisions. Below, I offer detailed comments that could help enhance the clarity, impact, and wider relevance of the research.

Strengths

Methodological robustness: Employing both statistical performance metrics (ubRMSE, KGE′, Spearman, PBIAS) and event-based analyses offers a thorough evaluation of SM dynamics.

Unique dataset: The Kimün-Ko network provides a valuable contribution, delivering new ground-based data from natural ecosystems in the Southern Hemisphere.

Balanced interpretation: Conclusions appropriately emphasise the strengths and weaknesses of each product (ERA5 and ERA5-Land excel in humid regions, SPL4SMAU performs better in arid areas, GLDAS-Noah is the weakest).

Scientific relevance: The findings are highly pertinent for hydrological modelling, ecohydrology, and climate research in regions with limited data.: The use of both statistical performance metrics (ubRMSE, KGE′, Spearman, PBIAS) and event-based analyses provides a comprehensive assessment of SM dynamics.

Major Suggestions

Conclusions (clarity and structure). The conclusions are dense and could be reorganized into a concise list of take-home messages. This would enhance readability and emphasise the main findings for a broader audience.

Implications and applications. Expand the discussion on the practical relevance of the results. For example: How can the findings support water management or drought monitoring? What are the implications for regional climate modeling in semi-arid Chile?

Limitations and future work: The relatively short observational period (2022–2023) limits the assessment of interannual variability. This should be explicitly acknowledged, with suggestions on how longer records (or complementary datasets) could improve robustness. The definition of RZSM (0–100 cm) is harmonized across products but may obscure differences in vertical soil processes. A brief sensitivity discussion would strengthen the analysis. Consider proposing specific future research avenues (e.g., integration with Sentinel-1 or other high-resolution sensors, extension of the monitoring network, cross-comparisons with ISMN data).

International connection: The study would gain broader relevance if results were briefly compared with findings from other arid/humid regions (e.g., Africa, Asia). This would highlight the global implications of the Chilean case study.

Verification of assumptions: You averaged all data to 3-hour resolution. Could this temporal aggregation mask short-term dynamics, particularly in ERA5/ERA5-Land, which have hourly outputs? Please justify. Moreover, precipitation drives soil moisture. A more explicit evaluation of precipitation inputs in ERA5/ERA5-Land (and their consistency with local rain gauges) would strengthen confidence in the results.

Minor Suggestions

Writing style: Sometimes the text is dense and filled with acronyms. Making the prose simpler and cutting down on jargon where possible would make it easier to understand, especially for readers who are less familiar with SM modelling.

Figures: Some figures are very detailed and hard to interpret. Think about adding schematic diagrams or visual summaries that compare key differences (e.g., “north arid vs south humid”) to make the main points clearer.

Terminology: Make sure the terms are used consistently (e.g., SSM vs “surface SM”) and check that all acronyms are explained when first introduced.

Formatting: While tables that summarise site details and datasets are useful, they could be made clearer by streamlining their layout. At times, the text is dense and acronym-heavy. Simplifying the prose and reducing jargon where possible would improve accessibility, especially for readers less familiar with SM modelling.

The manuscript addresses an interesting and relevant topic, namely the comparison and reliability of different datasets, including Earth Observation and reanalysis products, in reproducing observed patterns of soil moisture. The paper is highly technical and, in some sections, the succession of data and results makes the reading somewhat heavy, at the expense of a deeper discussion on the reasons behind the observed trends. To avoid the impression that the manuscript reads as a technical report, some of the results could be moved to the Appendix.

One major point that is not addressed is that the capacitive probes used as reference sensors often require calibration themselves. What is referred to as ground truth therefore also deserves further investigation. At the same time, although a large amount of information is reported, one of the most relevant aspects of the comparison remains underexplored, namely the type of soil and its hydraulic properties as represented in the models used for the analysis. This factor could be important in explaining differences across datasets. For this reason, some of the results might also be interpreted in terms of saturation degree rather than volumetric water content. Another important aspect concerns the precipitation input to the models, which strongly influences soil response. It would be useful to show how precipitation varies across models compared to the observational values used.

In the abstract, the number of acronyms should be reduced, and the correct name of the dataset is TERENO rather than TERRENO. For each dataset considered, it would also be useful to report the temporal and spatial coverage. The choice of the models needs to be better justified, in particular why ERA5 and ERA5-Land are used when they are largely overlapping, and why the two specific EO products were selected over other available alternatives. Some background on the TEROS sensor technology and the associated measurement uncertainties should be included. Clarification is also needed on how the classes E, G, S, and B are defined. The mismatch between SSM and RZSM may also be related to the different timescales of the processes involved.

For these reasons, the manuscript requires substantial revisions before it can be considered for publication in a scientific journal.