the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Sep 2026
Dynamic vegetation–soil–water feedbacks as an under-recognised source of structural uncertainty in digital soil mapping
Abstract. Digital soil mapping (DSM) produces spatially continuous soil information for land-use planning, carbon accounting, and Earth system analysis, resting on the SCORPAN framework and its use of remote-sensing and terrain covariates as indicators of soil-forming processes. Similar approaches estimate ecological and hydrological conditions from vegetation, moisture, and climate data. Two assumptions receive little scrutiny: that soil-covariate relationships stay constant in space and time, and that covariates act as independent predictors. In reality vegetation, soil, and water form a coupled system in which each influences the others, and the strength and even the direction of these interactions vary across landscapes and shift over time. We argue that these dynamic relationships are an important but under-recognised source of structural uncertainty, distinct from the sampling, measurement, and model-fitting error usually partitioned in DSM. Drawing on wetlands, drylands, sloping farmland, burned watersheds, drought-affected forests, and thawing permafrost, we show how the feedbacks break the assumptions behind DSM, why the same problem affects land-surface, ecological, and hydrological models, and how mapping might be made feedback-aware.
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Status: open (until 21 Oct 2026)
- RC1: 'Comment on egusphere-2026-4516', Anonymous Referee #1, 30 Sep 2026 reply
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- 1
This is an interesting perspective, and I agree with the central premise raised by the authors. Soil properties emerge from interacting processes whose influence depends on environmental setting and history, and these interactions form a feedback loop perpetuated across space and time in terrestrial landscapes. Therefore, this perspective raises a relevant concern for predicting soil properties: whether relationships learned from environmental snapshots or long-term averages remain reliable across locations, periods, and disturbances. That said, I think that the conceptual contribution and practical implications of the proposed feedback-aware framework need further clarification for practical application in spatiotemporal digital soil mapping (DSM).
My understanding is that the authors argue that commonly used covariates for predicting soil properties, under the SCORPAN framework, provide an incomplete description of a soil-plant-water system with feedbacks, legacies, and possible transitions. Consequently, similar observed covariates may correspond to different soil conditions, and a model that performs well within its training domain may become unreliable under another inference space. These concerns overlap with established challenges in model transferability and spatiotemporal DSM, including those discussed by Heuvelink et al. (2021). If I understand correctly, the authors argue that these known effects constitute a distinct class of structural uncertainty. That said, I think the proposal of structural uncertainty requires more explanation and stronger justification.
I find the idea of structural uncertainty interesting. That said, I am confused, as the manuscript does not clearly establish how structural uncertainty differs from uncertainty associated with omitted variables, inadequate model architecture, or differences in soil–covariate relationships between the conditions used to train the model (i.e., model parameterization) and those where it is applied. Limited or unrepresentative training data may further restrict model applicability and inference, particularly when relevant environmental regimes or histories are poorly sampled. I think it is important to clarify how these sampling limitations interact with the proposed feedback-related uncertainty, and under what conditions additional observations would reduce it or not.
Furthermore, I find the idea of feedbacks interesting, but the manuscript would benefit from explaining the conditions under which feedbacks create a specific practical limitation that existing DSM approaches do not adequately address. The proposed distinction between feedback-related uncertainty and existing uncertainty categories needs further clarification. I encourage the authors to explain how this contribution differs from omitted-variable effects, model misspecification, and changing training-to-application relationships.
Section 6 identifies several reasonable directions, but I feel that their connection to the proposed uncertainty mechanism needs more work. This section is important and may require some additional effort for clarification. Here are some ideas from reading this section that may help the authors improve their proposal. A) Time series and phenology metrics can represent seasonal changes and potentially some environmental history, but they do not automatically identify reciprocal feedbacks. B) Local models and stratification by soil–landscape units can accommodate differences among settings, but require a defensible approach to defining regimes and handling transitions. I like the idea of soil-landscape units, but the authors should connect it more strongly to their proposal. C) Process-model outputs may provide information about unobserved patterns, but they could also introduce uncertainty from the process model itself due to a wrong mathematical interpretation of the physical world. D) Repeated monitoring can reveal temporal change, but I think that attributing that change specifically to feedbacks requires additional explanation. Therefore, I think it is important to help readers connect these ideas and sources of uncertainty to proposed solutions.
Finally, it could be useful to provide a clearer conceptual or empirical example. This effort could connect the proposed mechanism to a practical DSM problem. For example, it could identify the target soil property, relevant covariates and history, the circumstances under which a conventional model becomes unreliable, the additional information or model structure needed, and an appropriate validation design. It could also explain how to distinguish the contribution of feedbacks from alternative explanations. I think this effort would help readers connect the ideas and see a clear explanation/application of the proposed actions. This will help clarify how an explicit focus on feedback adds to established DSM methods and would make the manuscript’s contribution more actionable.