the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jan 2025
High-resolution soil moisture mapping in northern boreal forests using SMAP data and downscaling techniques
Abstract. Soil moisture plays an important part in predicting different forest-related phenomena, such as tree growth or forest fire risk. As these phenomena influence the carbon storage capacity of boreal forest ecosystems, it is crucial to provide soil moisture information at high temporal and spatial scales. Current satellite-based soil moisture products often have high temporal resolution at the expense of spatial resolution. Therefore, we developed a machine-learning-based model to estimate soil moisture at high temporal and high spatial resolution over boreal forested areas for the annual time period from May to October. The basis data of the model is the enhanced 9 km spatial resolution soil moisture data from the Soil Moisture Active Passive (SMAP) mission. Additionally, soil and vegetation properties, reanalysis-based parameters, and measured in situ soil moisture data are used to guide the model construction process. The analysis of the developed model shows that the model retains the temporal and large-scale spatial variability of SMAP soil moisture. Furthermore, comparisons with the independent in situ soil moisture data show that the soil moisture values predicted by the developed model have a better agreement with in situ values than SMAP soil moisture, as RMSE decreases from 0.097 m3/m3 to 0.065 m3/m3, and correlation increases from 0.30 to 0.52 over forest sites. Therefore, this machine-learning-based model can be used to predict high-resolution soil moisture over boreal forested areas.
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RC1: 'Comment on hess-2024-390', Anonymous Referee #1, 28 Jan 2025
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The manuscript presents a machine-learning-based approach to downscale SMAP soil moisture data from 9 km to finer resolutions of 1 km and 250 m for boreal forests. The model integrates SMAP data with soil, vegetation, and weather inputs to provide higher spatial resolution soil moisture estimates, addressing the limitations of SMAP's coarse coverage in northern latitudes. Validation against in situ measurements shows improved accuracy, with reduced RMSE and increased correlation compared to raw SMAP data. However, the methodology is limited to forested areas, excluding peatlands and other land types. While the approach demonstrates the potential for high-resolution soil moisture mapping, several areas require substantial improvement before publication.
Major Comments:
- SMAP Mission provides SMAP-Sentinel 3 km and 1 km soil moisture (https://doi.org/10.1016/j.rse.2019.111380), and it is very strange to see that these are not discussed in the literature section.
- One of the key advantages of this study is its complement to the SMAP Sentinel Soil Moisture product, particularly by addressing NASA’s limitation in providing soil moisture data over northern latitudes. However, while this contribution is acknowledged, the paper could have been strengthened significantly by demonstrating a more direct comparison with SMAP Sentinel dense time series in areas where such data are available. I would suggest the author replicate the same method over the mainland where SMAP Sentinel retrieval is available and compare for multiple locations. This can help a wider audience understand how the discussed method is reliable when compared to the operational product. This would provide a robust validation framework and establish the superiority or limitations of the proposed methodology.
- The reliance on static inputs such as bulk density and silt content raises concerns about the adaptability of the model to regions beyond the boreal forests of Northern Finland. The training set’s limited geographic and environmental variability suggests that the model may not perform well in regions with differing soil or vegetation characteristics. This could potentially undermine the generalizability of the approach, and expanding the training dataset to include diverse boreal forest sites would address this shortcoming.
- The exclusion of peatlands from the study is a significant limitation, especially given their critical role in carbon storage in boreal ecosystems. Although the authors briefly discuss this gap, they fail to propose a concrete pathway for integrating peatlands into future models. More effort should have been made to outline how the methodology could be adapted to incorporate such essential land cover types.
- The discussion around uncertainty analysis highlights the model’s heavy dependence on soil properties, which dominate the prediction outcomes. While these are undoubtedly critical inputs, the relative insensitivity of the model to weather-related inputs like precipitation suggests a potential flaw in the approach. The coarse resolution of ERA5-Land data used for precipitation might be a contributing factor, and exploring higher-resolution meteorological datasets could refine the model’s sensitivity to short-term climatic variations.
- The use of a machine-learning-based gradient boosting model (LightGBM) is appropriate for capturing complex relationships, but the small training dataset limits the robustness of the approach. Therefore, it’s important to discuss the limitations of the method used and how to overcome them.
- The SMAP L3_SM_P_E spatial resolution is 33 km, which is gridded to 9 km, but this is not mentioned anywhere in the manuscript. Typically, downscaling should consider the original 33 km resolution rather than the 9 km gridded resolution. If the model directly uses the 9 km gridded data as the spatial resolution, I recommend reprocessing the model by considering the original 33 km resolution. Additionally, the revised version should clearly explain how this resolution is incorporated into the methodology.
- While the authors discuss future L-band missions like NISAR and ROSE-L, more focus should be given how this method will be useful for this mission.
- Validation against in situ measurements shows promising accuracy improvements, but the exclusion of outliers like DIS0004 suggests sensitivity to anomalies that the model should handle better.
- The conclusion section needs attention as it does not read well. Please consider rewriting the section more scientifically.
Minor Comments:
- L2: “Phenomena” is not appropriate here.
- L4: “High spatio-temporal scale” would be more appropriate.
- L37: “Short distance” is not appropriate. Rephrase as “spatially heterogeneous.”
- L41: Citation of the operational 1 km SMAP soil moisture product is missing (https://doi.org/10.1016/j.rse.2019.111380).
- There are multiple sites in Alaska where in-situ soil moisture is available, and those should be included, such as the site from Delta Junction (NEON site).
- A description of the study site is required in the main text.
- It is unclear why CORINE land cover is used. The new ESA 10 m land cover provides more sufficient information for this study and spatially has more detail than CORINE.
- L336: The NISAR mission will provide a 200 m soil moisture product as an operational soil moisture product. It is suggested to include proper citations in the manuscript (https://doi.org/10.1016/j.rse.2023.113667 and https://doi.org/10.1016/j.rse.2024.114288).
- L338: NISAR will be launched in April 2025.
Citation: https://doi.org/10.5194/hess-2024-390-RC1
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