Dear Editor,

I carefully read the Manuscript entitled "High-resolution near-surface electromagnetic mapping for the hydrological modeling of an orange orchard'', from Peruzzo et al.

The paper is well written, and show a very serious study on EM processing, which is too often not done that deep. I had more difficulties to follow the Discussion section which seems to me more collection of conclusion items related to the what has been done in the field, but I may have missed something. I recommend publication after minor corrections. The paper will be cited for the excellent EM processing.

Please find a couple of comments a way to improve the paper, the first ``major'' comment need to be addressed with more care probably :

Major :

Line 448-449 : ``These contrasting resisty values and their spatial distribution agree with ERT inverted in Figure 6.'' If you want to state that you probably need a bivariate plot, Em versus ERT and/or a deeper statistical analysis.

I have a couple of minor corrections :

- L43-44 [...] AI drove'' I'd add ``and drive''

- L55-66. I did not understand why you chose to mention so much the cosmic-ray method ? It is a very goo paragraph, in term of references and sciences, but I tend to think that you insisted too much in regards with your paper, very much EM focused?

- L73-74 I would mention that ERT and FDEM measure other physical parameter than the resisitvity.

- L171 ``non falsified''. I enjoyed reading this. Thanks.

- Materials and methods. L174-184. Please can you add that there is not slope (or very minor) in your orchad ?

- Line 182-184, for your +/- this is not uncertainties nor sigma, this is the range of your data if I understood correctly, please state it.

- Line 197, state that METER is a brand, maybe METER, GMBH/LTD or something similar, to avoir confusion.

- Line 214-217. Congrats on doing the measurements in such hot conditions.

- Line 224. How did you manage to keep the instrument height at 0.1 m, and what is the influence on having a higher or lower value on your data ?

- Line 238  would remove QGis from this line and write it further, because we get the impression that Qgis helped for the inversion.

- Line 241-243 The GEM-2 ``[....] factory conversion''. Can you add just one sentence to explain it a bit more especially concerning the factory calibration ?

- Line 245 ``processed in QGIS.'' I would add ``for positionning.'' just

- Line 249  What type of distribution are your or were you expecting ?

- Line 265 GPR should be GPS I think.

- Line 270-271, ``atan2 function'', you may need to cite numpy

- Line 321, ``perpendicular to the tree line'' maybe add : ``(represented by the black rectangle)''.

- Line 329, ``Feddes parameters''. Could you describe them very very rapidly in the intro, when you introduce RWU ?

- Line 339, compare the water volume input in the model with real values.

- Line 343, Mualem-van Genuchtenm since you are using the van genuchten model, it implies the mualem model for hydraulic conductivity.

- Line 441-443. I did not get what you did with the extract values above 40, since your limit since to be 45 (by the way can you redo the colorbar with 45 written plainly).

- Line 451. Restate FC and WP and maybe restate that FC is -0.3 kPa and WP is -15 kPa, so you can write the standard deviation with units.

- Line 455-460 I am not certain the gamma-ray survey brings more info than a granulo+Xray driffrac, but if you think it brings something to your study, why not. I still believe that a granulo curve would give more info for retention curve and other hydro parameters. I probably have missed something.

- Line 467-468. You mentionned soil heat flux with the letter H, I think you meant G ?

Line 505 : Mulaem-van Genuchten

Line 525 : What is the distance to your flux tower ?

Figure correction :

In general some of your labels omit A and B while you have figure A and B. Please, can you check them all ?

Figure 1-B : remove outliers on the figure ? Very high spots in the south and one in the middle of the black rectangle.

Figure 1-B, add something like `` background is an reconstructed RGB orthomosaic'' other wise background is Google map or other

Figure 3 , add ``the black rectangle is ...''

Figure 3 : add A and B in the legend then split the description

Figure 5, How about making a figure of EM from CMD and EM from GEM2 as bi-variate plot for the same depth ?

Figure 6, needs a bit of polishing, especially the axis name, the RMS or RRMS of the data/model and I would add tree picture symbolizing tree position, it will help the reader. I would also suggest to not cut the negative percentage difference, it will help assessing the quality of the data and inversion.

Figure 8-A : change the ylabel, with unity. Add vertical arrows for the other infiltration 

Figure 8-B : change ylabel with squared exponent. Why Rn is negative ?

Figure 9 : add a colorbar, even if it is conceptual.

Figure 10 - A : replace the position of the sensors VWC3-VZC4

I found this work on the use of the frequency domain electromagnetic (FDEM) method and the electrical resistivity tomography (ERT) method in precision agriculture very interesting and engaging. I know that the FDEM method has been used in agricultural management for a long time, and is one of the most important geophysical techniques in the field of agrogeophysics. However, despite the existence of many similar studies, this work is interesting and relevant because it examines the usefulness of two FDEM devices in an orange tree study plot in conjunction with the ERT technique to derive a 3D resistivity model. This model helps to implement a numerical hydrological model that reproduces key aspects associated with volumetric water content (VWC), the position of the trees and the effects of the dripping system and root water uptake (RWU), among others. Apart from the fact that the authors implemented open-source codes to accomplish the geophysical data inversion, which I consider appropriate for potential practitioners involved in this field of study. The work is clearly described and structured, and is well supported by figures that guarantee the reproducibility of the methodology in similar environments. In my opinion, scientists concerned with precision agriculture or water management in agriculture will be pleased to read this interesting work, which deserves to be published.

I only suggest to replace Frequency-Domain ElectroMagnetic induction (FDEM) by Frequency-Domain Electromagnetic (FDEM), the term commonly accepted.