| 15 Jun 2026
A hybrid physics and machine learning approach highlights and alleviates soil moisture downscaling challenges
Abstract. Soil moisture information is crucial for predicting natural hazards like droughts and landslides, improving climate and weather forecasts, understanding soil-atmosphere exchanges and feedback, and better managing water resources. Soil moisture networks are sparse, and remote sensing products lack necessary resolution. Downscaling techniques aim to enhance resolution, but face challenges because the ancillary data they rely on are often sparse and lack representativeness.
Here, a ”downscaling playground” is created over the Contiguous USA using physics-based hydrological simulations that allow downscaling techniques to be trained and tested without practical limitations. We show that existing biases in soil moisture station locations lead to spatial overfitting, resulting in poorer performance than, for example, if those stations had been randomly located. The biases lead also to unwarranted confidence in the improvement achieved with downscaling, as testing is carried out on stations in similar locations. Finally, we propose a new paradigm that fuses physics-based modelling (ParFlow-CLM) with machine learning (XGBoost) and downscales remote sensing observations (e.g., SMAP) to produce a 1km2 soil moisture product over CONUS. We test the approach thoroughly against in situ observations.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Hydrology and Earth System Sciences.
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The authors introduce a "downscaling playground" based on ParFlow-CLM simulations and use it to investigate fundamental limitations of traditional downscaling approaches that rely on sparse in-situ observations. They show that the spatial distribution of monitoring stations can introduce strong biases and overoptimistic estimates of model performance. Building on this framework, they propose a hybrid approach that combines physics-based simulations with machine learning (XGBoost) to downscale SMAP soil moisture to 1 km over CONUS.
The manuscript addresses an important topic in hydrology and remote sensing and raises a valuable point regarding representativeness and validation. The idea of exploiting physically based simulations as a surrogate training dataset is very innovative and has the potential to influence future downscaling studies.
However, in its current form, the manuscript contains a few weaknesses that limit the strength of its conclusions, including aspects of methodological clarity and the interpretation of results; these are detailed in the major and minor comments below.
Major comments:
The central assumption of the study is that ParFlow simulations provide realistic high-resolution reference data, and this assumption underpins almost all experiments. Although the authors acknowledge its limitations (Sections 4.2 and 4.3), several conclusions appear stronger than warranted by the evidence presented. While the manuscript compares the downscaled soil moisture product with independent in-situ observations using the metrics proposed by Crow et al. (2022), the validation would benefit from a more direct assessment of how well the downscaled product reproduces the observations themselves using conventional statistics (e.g., RMSD, ubRMSE, ME).
The most important practical outcome of the study is the downscaling application of the SMAP observations. However, validation against observational data does not convincingly demonstrate that the proposed product consistently outperforms the original SMAP product. Criterion 1 remains essentially unchanged, whilst Criterion 2 is close to zero or even negative (e.g. −0.211 for XGBoost), with improvements occurring only during certain time periods and, above all, at stations at low altitudes. The results therefore provide evidence of the feasibility of the proposed approach rather than clear proof of superior performance. In this context, some statements in the summary and conclusions appear exaggerated, particularly those suggesting that the approach ‘mitigates the challenges of downscaling soil moisture’ or ‘enables the production of high-resolution soil moisture estimates’. I recommend toning down these claims and instead emphasising the methodological potential of the hybrid framework, whilst acknowledging that its advantages over the native SMAP have only been partially demonstrated by validation against observational data.
The manuscript is generally well written but would benefit from tightening, particularly in the Introduction, improved consistency in terminology, and a more focused narrative that reduces redundancy and enhances readability. In addition, the experiments could be presented more clearly, for example by introducing a dedicated subsection in the Methods and by improving the clarity and informativeness of Fig. 1. This subsection should also explain the assumptions made, for example, why only 20 days were used for the testing and how these were selected.
The Discussion would benefit from a clearer comparison with existing downscaling approaches, as it currently lacks sufficient context within the broader literature. In particular, Montzka et al. (2018) proposed a physically based method using high-resolution soil properties to downscale SMAP to 1 km without requiring hydrological modelling. Given the relative simplicity of that approach, the manuscript should better clarify the added value of the more complex framework presented here and whether it leads to improved performance or broader applicability.
In my opinion, the Conclusions would benefit from the inclusion of a brief outlook outlining future research directions, potential pathways toward operational application, and remaining challenges for transferring the proposed framework beyond the current setup.
Specific comments:
L5: The term "downscaling playground" sounds somewhat informal. Consider replacing it with a term such as "simulation-based testbed", "controlled experimental framework", or "benchmark framework", which would better convey the methodological rigor and purpose of the approach.
L6: At this stage it is unclear what “existing biases in soil moisture station locations” means.
L11: CONUS needs to be introduced.
L11: The final sentence of the abstract is not effective. Abstracts should conclude with the principal findings and/or their broader significance rather than with a description of a methodological step.
L13: “soil moisture estimates”
L17: Rather old citation.
L22: I assume that "soil moisture" is meant here. Please use a consistent name for the variable throughout the manuscript and avoid switching between different terms unless a distinct quantity is intended.
L50: Which data sources are you referring to.
L80: Unclear which components are meant here.
L103-104: This should be reformulated into clearer way.
L104: Why did you choose 10 cm? SMAP’s effective penetration depth is generally considered about the top 0–5 cm of soil (e.g. Brown et al., 2013)
L123-125: This statement understates the representativeness problem. Even at 1 km² resolution, the support volume is still many orders of magnitude larger than that of typical in-situ sensors. This issue remains central to the interpretation of validation results and should be more explicitly emphasized.
L220-225 and L244-248: These sections should be moved to a separate section within the Methods section, where the experiments are described.
L242: The meaning of “classic downscaling” should be explained.
L377: Consider closing with an outlook.
Figure 1: The clarity of the schematic could be improved. The caption is also rather brief and could be made more informative by explicitly describing the main steps of the workflow.
Literature
Brown, M. E., Escobar, V., Moran, S., Entekhabi, D., O'Neill, P. E., Njoku, E. G., ... & Entin, J. K. (2013). NASA's soil moisture active passive (SMAP) mission and opportunities for applications users. Bulletin of the American Meteorological Society, 94(8), 1125-1128.
Crow, W. T., Chen, F., & Colliander, A. (2022). Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations. Remote Sensing of Environment, 283, 113300.
Montzka, C., K. Rötzer, H.R. Bogena and H. Vereecken (2018): A new soil moisture downscaling approach for SMAP, SMOS and ASCAT by predicting sub-grid variability. Remote Sensing 10(3): 427. DOI: 10.3390/rs10030427