the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Sep 2026
From in situ to remote sensing: a framework for quantifying boundary-condition sensitivity in soil organic carbon models across diverse European sites
Abstract. Process-based soil organic carbon (SOC) models are widely used for carbon accounting and assessments. For applications beyond experimental sites, remote sensing–derived inputs are often used to complement or replace in situ boundary conditions. This substitution introduces additional sensitivity that has rarely been explicitly quantified. Here, we propose a general framework to quantify boundary-condition sensitivity in SOC modelling, distinguishing between dynamic sensitivity, which reflects deviations in the temporal trajectory of SOC stocks, and endpoint sensitivity, which captures differences in estimated SOC changes over defined reporting periods. Sensitivity is quantified by comparing substitution simulations, in which boundary conditions are replaced one by one with remote sensing–derived spatial datasets, against a baseline simulation driven by in situ observations. The framework is applied to 16 long-term experimental sites across Europe, covering diverse land-use types and environmental conditions.
Boundary-condition substitution affected both SOC trajectories and endpoint estimates, although the magnitude and form of these effects varied among inputs. Initial SOC, vegetation-derived carbon inputs, and climate data were the dominant drivers of dynamic sensitivity. Fixed endpoint sensitivity was generally small relative to measured SOC changes, supporting the use of remote sensing–derived spatial datasets as scalable boundary-condition proxies for SOC modelling. Rolling endpoint sensitivity further revealed that SOC change assessments depend not only on SOC dynamics but also on the selected reporting window. Short reporting periods were more affected by short-term variability, whereas longer periods were more susceptible to accumulated deviations. A reporting window of approximately 15–30 years provided a practical balance between these effects.
Overall, the framework enables a systematic quantification of sensitivity arising from boundary-condition substitution, enabling evaluation of both trajectory and reporting robustness. These findings highlight the importance of jointly considering critical boundary conditions and reporting-window design when applying remote sensing–driven SOC modelling for regional carbon accounting.
Competing interests: At least one of the (co-)authors is a member of the editorial board of SOIL.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 25 Nov 2026)
- RC1: 'Comment on egusphere-2026-4735', Anonymous Referee #1, 09 Nov 2026 reply
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- 1
Manuscript: From in situ to remote sensing: a framework for quantifying boundary-condition sensitivity in soil organic carbon models across diverse European sites. This is a well-organized and relevant paper in the field. However, I have some suggestions to improve clarity before publication.
My major concern is that differences in sampling depth and modelling procedures among sites are not sufficiently addressed (lines 178–186). Related with the previous, Table 1 reports depths of 20 cm for Spanish sites and 30 cm elsewhere. The authors should explain how these differences affect cross-site comparisons, particularly because depth and country are confounded. An appendix should document SOC analytical methods and stock calculations, including bulk density and coarse-fragment corrections, and confirm that measurements, spatial products, and simulations represent the same depth within each site.
Regarding the in situ reference (lines 165–171; Table 2), the authors describe the baseline as entirely based on local observations, although Table 2 includes gridded climate data and NDVI-derived inputs. Please clarify which inputs were measured or estimated and which actually changed between scenarios. Where the same inputs were used, negligible differences cannot be interpreted as evidence of robustness to substitution.
Some other modelling procedures also need more detail to ensure reproducibility (lines 136–140; Table 3). Please document each site’s model version , parameter settings, temporal resolution, initialization, spin-up duration, and initial pool allocation in an appendix. Specify which settings remained fixed across scenarios and whether substitutions involved recalibration.
The statement that initialization ensures consistency with measured SOC conditions requires clarification (lines 180–184). What criteria were used to establish equilibrium and an acceptable match to observed SOC? Please describe how carbon inputs were adjusted. Matching total SOC does not guarantee that the pool allocation or equilibrium assumptions truly represent the conditions at the site.
For sites with a single SOC observation (lines 184–186), briefly explain how that observation constrained initialization and how the resulting pools were used in the subsequent simulation. The assumptions and limitations of the spin-up should be acknowledged, without presenting it as a reconstruction of historical SOC dynamics.
Regarding the rolling-window analysis (lines 270–290, particularly 278–282 and 285–290), averaging signed differences may conceal substantial deviations through cancellation. Please include absolute deviations and dispersion and report the proportion of windows showing directional agreement rather than only the most frequent sign combination. Window counts, overlap, and the treatment of changes close to zero also need clarification.
Finally, the recommendation of a 15–30-year reporting window needs an explicit scientific basis (lines 525–545, particularly 540–545). I can only think that this interval may reflect policy or reporting horizons (?), but the rationale is not clear. Please explain the quantitative criterion used to select it and assess whether the result depends on which sites contribute to longer windows.
Minor format observations
L 127. Standardize the abbreviation for Spain: “ES” in the text versus “SP” in site identifiers.
L 317. Identify the relevant “Spanish sites” explicitly: SP-Grass-02, SP-Forest-02, and SP-Shrub-01?.
Figures 4 and 7. Increase precision where rounding displays small non-zero values as 0.00. Use additional decimals, scientific notation, or “<0.001” where justified by the underlying values.