Dear authors,

thank you very much for your submission. I think that your research field is of high relevance and that your data assimilation approach is of high interest to the community. However, I have severe doubts, if the current quality of the presentation will enable the community to really benefit from your study. Consequently, I recommend rather substantial changes, which more correspond to "reject and resubmit" (an option that is not available for GMD) than to "major revisions". 



Please, see my detailed comments in the attached PDF. 



Yours sincerely

General comments

This preprint describes a newly developed method of estimating the soil carbon budget of major crops at high spatial resolution. It addresses a “hot” and very relevant topic in climate research, specifically the field of estimating soil carbon fluxes. The method embodies a novel combination of both high spatial resolution (the intra-field scale) with large spatial coverage (up to 100x100 km) of carbon flux-related variables and their variability. It assimilates high-resolution remote sensing data into an agronomic model and accounts for uncertainties across the processing chain. The paper deals with the evaluating the model accuracy at multiple scales, assessing the impact of spatial and temporal scale in the remote sensing-based input data, and integration of multiple models/dataset for a specific crop type and study site (winter wheat in SW France).

Although the topic of quantifying soil carbon fluxes holds high significance, the way the proposed methodology is presented in the manuscript offers only little knowledge gain and shows only limited potential to be used for further studies of the soil carbon budget.

First, although the paper starts with the need for better quantifications of soil organic carbon, it is too hung up on general carbon flux, data/model integration and comparison to make interesting statements about the variability and accuracy of soil carbon estimation. To me, rather than addressing the estimation of soil carbon fluxes, it seems that the proposed approach is a crop biomass model based on correlations from remote sensing-derived GLAI.

Second, it remains unclear why exactly the study area was chosen and to what extent it has greater relevance for other potential applications in different regions of the world with different crop types, different climatic and soil characteristics.

Furthermore, it is not clear which possible users the presented processing chain has and which questions - apart from comparing/evaluating data and model parameters - it can answer.

Lastly, the paper touches on too many aspects – the discussion of scale (spatial and temporal), inter-comparison of different data/models, different crop-related parameters –so that it is rather difficult to read. The focus on relevant questions within the carbon modelling community and clear answers to those got lost.

Overall, the paper holds certain potential for wide scientific interest, but needs to be substantially revised for better informative value on soil carbon fluxes and scalability of the method for other regions. I recommend performing major revisions before considering for publication in GMD.

I suggest to address the following major points:

1. the many spelling and grammatical errors in the manuscript (I can’t list all of these)
2. missed topic: the discrepancy between the introduction of the topic of soil carbon fluxes and the presentation of the approach to a crop model that rather maps biomass and NPP instead of soil carbon.
3. Clearer designation and focussed answering of relevant research questions/objectives
4. Justification for the selection of the study area in terms of what we can learn from it for other regions

Specific comments

Lines73ff (objectives)

Please be more specific about the objectives here. What are specifically “the outputs” you aim to check for accuracy and coherence? Instead of throwing in buzz words like “multi-scale validation”, name the research questions precisely, e.g. what is the impact of (a) spatial resolution and (b) temporal resolution of the input data? “verifying the coherency of the outputs through intra-field as well as regional analysis” sounds spongy too me. What exactly is meant by “coherency”, what is “intra-field and regional analysis”?

Line 112/land cover map

So, only the border extents of the shape file are used here to download the remote sensing and weather forcing data? Please describe how the land cover information is used, if at all, and how the land cover categories should look like.

Figure 4

What do the different colours mean? Why not use different colours for each dataset groups: input, validation and regional datasets?

Line 571f: “So high resolution allows more accurate estimates of the mean DAM values at field scale which enables more accurate field scale estimates of SOC changes by soil models in the perspective of monitoring SOC stock changes”

SOC was not really analysed and assessed here, so how would you know the effect of high-spatial resolution input data on soil organic carbon modelling? I think that this conclusion is simply too far-fetched here.

Line 665: “an intra-field scale quantification of the DAM and of the biomass that returns to the soil is needed for accurate monitoring of soil organic carbon stock changes”

Again, this conclusion seems far-fetched here. It is not clear to me how you assessed soil organic carbon with the proposed modelling chain, as it was never computed or derived from the output variables.

Technical corrections (selection, please perform comprehensive spelling correction!)

• Line 8: misspelling – “radiative transfert”
• Line 10/11: misspelling - “a land cover maps”
• Line 14: misspelling - “agaisnt"
• Line 103: misspelling – “corpping”
• Line 111: misspelling - “Landcover”
• Line 165: misspelling – “In Contrast”
• Line 268 (and every other occurrence): misspelling “lykelyhoods”
• Line 304: misspelling- “dry Biomass”
• Line 322: “in the field 27” What is 27?
• Line 325: misspelling – “is applied over a for winter wheat”
• Table 3: “Quantify spatial and variability” What variability?
• Line 353: “Dry Aboveground biomass, DAM measurements” Please present abbreviations in a uniform manner.
• Line 453: misspelling - “respiration”
•  etc.