General comments:

This paper proposes a method for estimating aboveground biomass density (AGBD) in olive orchards by combining GEDI L2A height data with orthophoto-derived canopy cover through a volumetric approach. Specifically, crown volume is defined as the overlap between the GEDI L2A footprint and canopy cover, and this volume is then converted to AGBD based on a series of assumptions regarding tree density, stem-to-canopy height ratio, and wood density. Finally, the GEDI L2A-derived AGBD is further predicted using satellite imagery and compared against the GEDI L4A AGBD product as a form of "validation". I think, the use of a volumetric method for AGBD estimation is relevant, particularly in plantation settings such as olive orchards, where tree density, stem-to-canopy height ratio, and wood density can be reliably estimated or sourced from the literature. However, there are three major concerns with how the method is applied in this study:

• First, the authors did not validate their product against field measurements or a more robust method for AGBD estimation (e.g., airborne LiDAR combined with allometric equations). Comparing results only against the GEDI L4A AGBD product is insufficient, as both products could share similar biases or errors.
• Second, the use of a volumetric approach at the footprint level based on GEDI L2A data is questionable. The L2A product has a ~25 m footprint, a positional accuracy of ~10 m, and a vertical accuracy of ~5 m. Combining this with orthophoto-derived canopy cover to estimate biomass is ambitious but fundamentally flawed, as it risks substantial spatial mismatches. Given the number of assumptions involved and the lack of field validation, it is difficult to assess the reliability of the resulting AGBD estimates.
• Third, it is unclear why the GEDI L2A-derived AGBD is subsequently predicted using satellite imagery at the footprint level. If the goal is to generate wall-to-wall maps, the issue of spatial mismatch remains. Moreover, the manuscript currently reads as if the L2A product itself was used as a predictor for estimating the GEDI L2A-derived AGBD, which, if true, would introduce severe data leakage and invalidate the approach.

Unfortunately, the paper also reads as an incoherent combination of machine learning approaches and derived results that are neither sufficiently explained nor properly validated. For example:

• The tree density prediction using Gaussian Process Regression lacks a clear explanation of the training data, validation process, or accuracy assessment.
• The canopy cover prediction from aerial imagery using linear regression appears to be based on 250 points derived from orthophotos, but there is insufficient methodological detail or accuracy evaluation.
• The AGBD estimation via the volumetric method seems to depend on unvalidated tree density and canopy cover products.
• The prediction of AGBD estimates from the volumetric method using Random Forest applied to satellite imagery lacks a clear justification, especially since a direct comparison between the GEDI L2A-derived and L4A AGBD products would have been more straightforward.

Finally, the paper also lacks a clear flow, and many concepts are referenced as common knowledge without proper introduction or explanation. There’s a lot of guesswork involved, as key details seem to be assumed rather than explained. It appears that there is a misunderstanding or lack of clarity regarding how the L4A product was derived. Additionally, the resolution at which you are working is unclear. I didn't proceed with the discussion section, as my concerns haven't been addressed earlier in the paper, making it difficult to engage with that part meaningfully.

In short, while the study introduces interesting ideas, the lack of rigorous validation and the reliance on stacked, unverified models undermine confidence in the results.

Specific comments:

P1, L15: The statement "provided more accurate predictions than the GEDI L4A product" is misleading without field data for validation.

P1, L19: Is olive tree biomass correlated with olive production? It would be useful to clarify this relationship.

P2, L1: The background on remote sensing methods for biomass estimation seems insufficient. Could you expand on the approaches typically used?

P2, L40-41: To my knowledge, SAR, especially L-band, can measure standing dead trees. Do you have a reference to support this?

P2, L46-47: Most natural forests are more structurally complex than a plantation. This statement contradicts your assumptions about tree density and structure. Could you clarify this?

P2, L57-58 and P3, L63-64: There are existing allometric databases for trees, including olive trees (e.g. Tallo). Have you considered these?

P3, L64-66: Isn't it the opposite? If you know the volume of each tree, there should be less uncertainty, assuming wood density remains constant. Could you clarify?

P3, L83: I'm still unclear on the main challenges of biomass estimation. Is it data scarcity, scale, or something else?

P3, L88: How exactly do you ensure spatial consistency? Given the misalignment between GEDI, satellite imagery, and orthophotos, this is a critical issue to address.

P3, L89: The jump to carbon sequestration seems sudden. Could you link this to biomass first? Are olive orchards typically used for carbon sequestration? This feels somewhat out of context.

P4, L93: The first sentence should be deleted, as it doesn’t seem relevant or necessary.

P4, L105: The proper notation should be Mg/ha or t/ha, not Ton/ha. Please apply this notation consistently throughout the text.

P5, L08: I'm confused about the contents of your training and validation dataset. Are you using GEDI, SAR, and optical data? This hasn’t been clearly mentioned. Or do you mean that your coverage provides a robust and variable area for training and validating your model?

P5, L113-118: This seems more suited for the introduction rather than the methods section. Could you move it accordingly?

P5, L120: The motivation for not using the L4A product seems to be missing in the introduction. Could you elaborate on why it was not used instead of developing a new product?

P5, L122: The second approach seems more like a comparison rather than an actual methodology. What is your ground truth here?

P5, L123-124: Why integrate with remote sensing data? If it's for creating wall-to-wall maps, this isn’t clear. Could you explain the reasoning?

P5, L125: At this stage, it's still unclear what optical and SAR data you are using. Could you clarify?

P7, L147: You still haven’t explained why you are linking this to remote sensing data. Could you provide more context?

P7, L149: This is confusing. Are you using Random Forest to scale up biomass estimates, or for variable importance? Please clarify.

P7, L151-153: Delete the last sentence as it doesn’t seem to add value.

P7, L163-165: This is not correct. I suggest reviewing how the L4A product is actually derived, as this part is misleading.

P7, L167: You can compare against L4A, but you cannot use it for validation. Validation implies true biomass measurements, which are not available here.

P7, L167-169: This is incorrect. The GEDI L4A product does not directly relate GEDI L2A metrics to field-measured biomass. Instead, it uses a model inversion approach based on airborne LiDAR-derived AGBD estimates, which were previously calibrated with field plot data.

P8, L181-182: Why use SAVI and BSI? Are they known to correlate with olive tree biomass? Could you provide more justification for this choice?

P8, L183: Could you clarify the resolution at which you're processing the HLS imagery?

P8, L184: Ground truth for what exactly? Is this intended for tree cover estimation? This needs to be clarified.

P8, L184-185: This is confusing. Is your canopy cover product based on aerial imagery or Sentinel-2 data? This needs to be clearer.

P8, L189: Why PALSAR and not Sentinel-1? Can you explain your choice of SAR data?

P8, L204: Where are the details of canopy cover prediction? How do you distinguish olive tree canopy cover from other types of cover? This needs more explanation.

P8, L206: Given the positional accuracy of the GEDI 25m footprint (~10m), does it make sense to use it at this scale? Could you clarify?

P9, L215: Please clarify that crown diameter (Cdiam) refers to the average crown diameter per tree within the footprint. This needs to be more explicit.

P9, L218: Olive tree heights range from 3m to 8m, and the RMSE of the L2A height product is about 5m. This creates a significant discrepancy. How do you address this uncertainty in tree height?

P9, L227-229: This needs to be introduced earlier in the paper. How exactly have you derived tree numbers? What ground truth and predictors did you use for your supervised learning?

P9, L235-236: L4A is not your approach; you are simply using it for comparison. Could you rephrase this section?

P10, L248-249: What do you mean by “improve”? Are you using olive trees identified in high-resolution imagery to train a model for olive canopy cover prediction in HLS data?

P10, L258: I don’t think the positional accuracy of GEDI L2A is suited for this type of work. Could you address this limitation?

P10, L272: I’m confused. Are you using both L2A and L4A as predictors? What’s your response variable? Or are you using your volumetric approach (based on L2A and canopy cover) as the response? If so, this creates issues with data leakage, as you cannot use L2A as both the predictor and the response.

P11, Table 1: What are the predictor and response variables here? This table needs clarification.

P13, L321: What’s RVI? Please define this abbreviation clearly.

P14, Figure 3: The frequency of GEDI L2A estimates is 10 times less than that of L4A. Why is this the case? Could you clarify this discrepancy?

P15: L355: In this section, it’s unclear what exactly you’re comparing and what your ground truth is. I assume you are comparing L2A-derived AGBD predicted using remote sensing data against the L4A AGBD product. However, this comparison is problematic without true biomass measurements for validation.

P16, Figure 4: You’re comparing two highly inaccurate models. Without field measurements, this comparison is not meaningful. I suggest reconsidering this analysis

Technical corrections:

P4, L107: Consider using "key producer" instead of "key reference"

P4, L107: Consider replacing "geometric normalization" with "co-registration", which is the more appropriate term in this context.

GENERAL COMMENTS:

The authors of this paper propose an integrative methodology to determine above-ground biomass density (AGBD) in olive orchards. The combined use of height data from GEDI L2A, SAR images, DEM, optical and infrared images can provide a lot of information on biomass distribution, especially in the case of olive orchards, a species of great agricultural and cultural importance in the countries of the Mediterranean basin. The validation and comparison of the results obtained with those modelled from other LiDAR data, in particular GEDI L4A, can be used to analyse the reliability of the proposed methodology. The analysis of the different regression models obtained with the different groups of variables used allows to identify the most important explanatory variables, although the grouping of all of them proves to be the best solution.

However, this ambitious goal has some weaknesses that need to be improved in order to make both the methodology and the analysis of the results clearer and more comprehensible:

- The explanation of the theoretical framework for biomass modelling is confusing and somewhat disorganised. Not enough details are provided for some parameters, e.g. canopy cover metrics or stem volume. How many field samples are used to train the Gaussian Process Regression that determines the total number of trees within each GEDI footprint? How is the number of trees per GEDI footprint determined from the size of the trees per hectare? I think more explanation is needed.

- This paper evaluates AGBD obtained by integrating data from different sources (with their limitations and accuracies) with the results of a model not specifically calibrated for olive trees (AGBD L4A), which makes the results less reliable. If validation were carried out against a ground truth, the estimates obtained for AGBD would be more reliable.

- Another important aspect of the work is the resolution of the GEDI footprints (25x25 m2) and their positional and vertical accuracy. In the 625 m2 of the GEDI footprint, there are very different types of olive trees, with regular and irregular planting frames and different stages of development, making it impossible to capture the fine-scale variations inherent to the crop. Other products have clearly better resolutions and accuracies, which should be detailed and justified why some are used and not others, such as the use of SRTM DEM (10m RMSE vertical accuracy) and not Copernicus DEM GLO-30 (2-4m RMSE vertical accuracy).

- In some parts of the methodology it is not entirely clear whether the authors have done some pre-processing of the data or whether they have accessed this information already pre-processed. There are no details of such pre-processing or the parameters that may have been used, so this should be clarified.

- It is not necessary to explain each acronym that appears at the caption of each figure or table, because once a term appears in the text for the first time, it is not advisable to repeat it each time.

- The temporal factor is not clearly detailed and analysed, i.e. what range of data is used to implement the methodology in Figure 2. Although the olive tree is an evergreen plant, the spectral variables obtained from HLS are highly conditioned by the dates analysed and the existing environmental conditions.

- There is some confusion as to whether L2A and L4A are used as predictor variables, or whether one is used as predictor (L2A) and the other for validation (L4A).

- There are some important errors in the citations to previous work:  The data used or the results obtained and analysed are not as stated in this paper (see details in the specific comments).

In conclusion, I think it is an interesting paper with a lot of potential, but it needs significant improvements to be understandable, reliable and reproducible.

SPECIFIC COMMENTS:

Figure 1, understood as a graphical description of the working area, should not contain the results of the article. In any case, it could contain existing information in land use databases such as Corine Land Cover or SIGPAC (for Spain), but never results. The AGBD map obtained in this work for the whole of Europe should be included as a result in the corresponding section, as is the case for Spain.

In the table in Figure 1, the value of millions of hectares of olive groves in Spain is very different from the value given in the text (line 98), which was obtained from SIGPAC. This discrepancy should be explained.

Table 1 is referred to in the text on line 181, but is located in line 277, it is quite separate from the text, which makes its interpretation somewhat more complex. In this table, the detail of all the variables used at the bottom of the table is somewhat excessive when it could be placed in the text, for example on line 185.

The citation Velàzquez-Martì et al. has a spelling error in the accents, which are repeated each time it appears, and should be Velázquez-Martí et al.

Line 33: The natural process of carbon storage by olive trees contributes to reducing the amount of greenhouse gases in the air, but does not reduce greenhouse gas emissions per se.

Line 55: This part of the introduction talks about biomass in olive crops, but line 55 cites work related to forest biomass. I think the introduction could be reorganised to emphasise the importance of work focused on forest environments and to highlight the differences with the analysis of olive tree biomass.

Line 125: GEE is an environment for accessing, not acquiring, satellite imagery, among other products or data. Acquisition of satellite imagery has other connotations.

Line 144: The various datasets are said to be pre-processed, including atmospheric corrections and geometric normalisation. The article gives no details of this pre-processing, nor of the algorithms or parameters used. Line 177 indicates that the HLS product is pre-processed, so there is some contradiction with line 144. I think it is very important to clarify how far the authors have gone in their work on pre-processing and, if it has been done, how it has been done.

Line 146: I'm not sure I understand the sentence between lines 146 and 148.

Lines 148 and 149: This sentence does not say the same as it appears in Figure 1.

Line 156: It is necessary to specify the characteristics of the GEDI LiDAR sensor, its resolutions, accuracy, etc., as well as its products L2A and L4A (line 165).

Line 166: It is stated “AGBD values were derived from L4A using ...” but in Figure 2 it is stated that these values are part of the data accessible in GEE. Regarding this variable, lines 259 and 272 refer to it as a 'predictor variable' in the models, but in Figure 1 it appears to be used only to compare and validate the model that estimates AGBD. I find this confusing and do not clearly understand the role of AGBD L4A in the workflow.

Line 188: Details of how the global annual mosaic was generated and the pre-processing applied to the PALSAR images are missing. Why were these images used rather than Sentinel-1, which has better spatial resolution? PALSAR operates in L-band, with greater penetration of vegetation canopies than Sentinel-1 (C-band), but for small canopies (olive trees) it is not clear that this is an advantage over the greater spatial detail that can be obtained with Sentinel-1.

Line 196: Why SRTM (10 m RMSE vertical accuracy) is used rather than other global models with higher accuracy such as Copernicus DEM GLO-30 (2-4 m RMSE vertical accuracy), which is also much more recent (2011-2015) than SRTM (2000)?

Lines 207 to 209: the same citation is used to indicate the value used in a variable (WD) and to justify or validate its use. For the latter, the citation from Velázquez-Martí 2014 cannot be included.

Line 241: Has a minimum threshold of olive trees been used to apply this selection or filter? The GEDI footprints (25x25 m2) have to be within the olive tree plots, so it is important to use an excess threshold for this to happen and to be able to apply erosion filters (line 253).

From line 295 to 305 the idea of using different combinations of variables to estimate biomass is repeated three times. These paragraphs should be rewritten to avoid repetition of ideas.

Line 329: The last paragraph of section 2.6 looks like a conclusion, but it is in the data and methodology section.

Line 339, Figure 3. The size of the dot symbol looks different in (a) and (b), causing confusion. The provincial boundaries (with different colours) are not relevant and do not add anything as there is no spatial analysis by province. In any case, since they are mentioned in the text (line 336), the Autonomous Communities could be added. And the symbol used in the legend should be included.

Line 344: I don't understand that after filtering the GEDI prints by olive tree parcels, you are now talking about cover crops such as grasslands, shrubs and woods, and they appear in the results.

Line 357: Mg/ha is given as the unit of measurement, whereas in other cases Ton/ha is used. It should be homogenised.

Line 369: 0.30 should be corrected to 0.29.

Line 372 can be combined with line 373 to give continuity of meaning.

Lines 384 to 387: this statement is repeated several times and seems to be a conclusion rather than a result in itself.

Line 390 to 392: Combine these two sentences to make it clear that Andalusia is in the south of Spain.

Line 407: “AGBD mean” should be deleted.

Section 3.4. The way it is written, it could be part of the discussion because it does not give numerical results, but an analysis and interpretation of results that are not there. In this sense, something is said about the most influential predictor variables within each group of variables, but there are no models, values, weights, explanatory percentages or importance of each variable individually to confirm that NDVI and NDWI are the most influential variables, for instance.

Line 445: the paper by Estornell et al. 2015 does not use spectral reflectance but LiDAR variables.

Line 492: The work by Fernández-Sarría et al. 2019 does not compare olive tree biomass with forest biomass, but only studies the residual biomass from olive tree pruning, logically with its structural planting framework.

Line 501: UAVs were not used in either of the cited studies: aerial LiDAR was used in the 2015 study and TLS in the 2019 study.

Row 529: The cited papers do not analyse irrigation or soil types or how they may affect biomass estimates. And only one dataset (TLS) is used. The statement "highlights the need for more comprehensive datasets in future research" is very general and cannot be limited to this work.

Line 567: This conclusion is too optimistic. With an R2 of 0.62 and an RMSE of 5.95 Ton/ha, it cannot be said that the model successfully captures the spatial heterogeneity of biomass, and even less so for the whole Mediterranean. This R2 is not high and the reported RMSE is higher than desirable.