The manuscript titled "Assessing evapotranspiration dynamics across central Europe in the context of land-atmosphere drivers" evaluates evapotranspiration (ET) products derived from satellite remote sensing, modeling, and reanalysis data in conjunction with in-situ observations from Integrated Carbon Observation System (ICOS) stations in central Europe from 2017 to 2020. The study investigates the effects of varying land cover types, soil moisture (SM), and vapor pressure deficit (VPD) on ET dynamics, including the severe drought of 2018. It uses extended triple collocation methods to assess the accuracy of ET products, revealing notable differences among products under heterogeneous land cover conditions and during drought years. The findings highlight that ET variability is strongly influenced by VPD under non-limiting soil moisture conditions and demonstrate that SEVIRI, ERA5-land, and GLEAM products show superior performance. The research provides insights into the suitability of various ET products for capturing land-atmosphere interactions and drought impacts across diverse land cover types. This manuscript presents valuable insights into evapotranspiration dynamics across central Europe, but significant revisions are needed before it can be considered for publication.

Major comments:

1. Limitations in the Performance Analysis of ET Products

The manuscript reveals significant performance differences among ET products, but it does not fully address how these discrepancies might affect the conclusions drawn, particularly under specific land cover types or climatic conditions. A deeper analysis of how these differences impact the study's practical implications is crucial.

1. In-depth Analysis of Drought Year Effects

The analysis of the 2018 drought is valuable, but the study does not sufficiently explore how different ET products capture the effects of drought. A more detailed comparison of how products perform under drought conditions is needed, including which models and parameterizations are more sensitive to such extremes.

1. Improvement of Remote Sensing and Ground Observation Matching

There is an acknowledged mismatch between point-scale ICOS data and the coarser-resolution remote sensing products. The manuscript could benefit from a discussion on methods to address this issue, such as spatial downscaling or data fusion techniques, to improve the alignment of ground and remote sensing observations.

1. Considering extending the study period

The study period appears to be too short, and the comparison between satellite-based ET and local measurements seems insufficient. The authors might consider extending the study period and incorporating a comparison across different timeframes. This would enable the use of the water balance principle for a more comprehensive evaluation.

Minor comments

1. At lines 34-35: please add explanation (why).
2. At line 34: please add the specific number to describe “the highest potential dependencies (error cross-correlation)”.
3. At lines 36-38, please explain why.
4. Please add the longitude and latitude in Figure 1.
5. Please add a table that describes all the sites, including their longitude, latitude, land cover type, altitude, and other relevant details.
6. At line 150: Please explain why you used the standardized precipitation-evapotranspiration index (SPEI).
7. If the time step is from hourly to 8-day, consider generated them at a shorter time step rather than monthly which loose too much information.
8. At lines 206-225: The spatial resolution differences among these ET products are quite significant. The authors should consider using methods to standardize all the ET products to a common resolution or use products with longer time periods. Otherwise, direct comparisons may not be valid or meaningful.
9. Table 1: please change as three lines table format and added other dataset’s information such as soil moisture and SPET etc.
10. At lines: 228-233 The removal of the seasonal signal may not be necessary, as the study period is too short for such adjustments.
11. At lines 295-296: Please provide the specific values for the highest R² and lowest RMSE and lowest percentage bias, PBIAS here.
12. Figure 4. Please added some statices numbers in every panel.
13. Same as Figure 4