the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Nov 2025
Unveiling China's Forest Soil properties: High-Resolution, Multi-Depth Mapping of Soil Bulk Density and pH Using Machine Learning Methods
Abstract. Precise monitoring of key forest soil properties is crucial for addressing global challenges like carbon sequestration and soil acidification. However, existing national soil maps, primarily derived from comprehensive ecosystem samples, inadequately represent the distinct characteristics and high spatial heterogeneity of China's vast and diverse forest ecosystems. To bridge this gap, we present the first high-resolution (90-m), forest-specific maps of soil bulk density (BD) and pH across China. Leveraging 4,356 forest soil profiles collected through extensive field surveys and 41 environmental covariates within an optimized Quantile Regression Forests (QRF) framework incorporating forward recursive feature selection (FRFS), we generated wall-to-wall predictions for five standardized depth intervals (0–5, 5–15, 15–30, 30–60, 60–100 cm). Model performance, assessed through 10-fold cross-validation (CV) and independent validation (IV), achieved model efficiency coefficients (MEC) ranging from 0.78 to 0.89 (CV) and 0.60 to 0.66 (IV) for bulk density (BD), and from 0.83 to 0.87 (CV) and 0.71 to 0.81 (IV) for pH, indicating the product's strong capability to capture the spatial variability of forest soil properties across China. The 90-m resolution BD and pH maps contribute to the GlobalSoilMap initiative and provide forest-specific inputs for regional Earth system and land surface models. These products advance the quantification of soil acidification processes and provide critical baseline data for estimating forest soil carbon stocks across China. The dataset is available at https://doi.org/10.57760/sciencedb.25375.
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Status: open (until 27 Dec 2025)
- RC1: 'Comment on essd-2025-496', Anonymous Referee #1, 17 Nov 2025 reply
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- 1
The manuscript proposes a high-resolution forest-specific mapping approach for predicting soil bulk density and pH across China. It presents a substantial body of work and addresses a topic of interest, which has the potential to contribute to the field. However, in my opinion, the current manuscript requires major revision before it can be considered for publication. Below are my major concerns:
First of all, after reading the Introduction, I wasn’t fully convinced of the necessity and urgency of this study. The Introduction section begins with very basic background information on forest soil, which is too general to establish a compelling rationale. The excessive introduction about methodology doesn’t effectively build a case for the study’s significance, either. For instance, the entire second paragraph is basically saying “a lot of people have done this”, which may justify methodological reliability but not why this work is needed. The fourth paragraph focuses on the historical development of methodologies, which isn’t the main goal of an Introduction. While building a nationwide forest soil profile database is potentially valuable, the current Introduction does not sufficiently highlight how this study advances beyond simply extracting forest-covered data from existing maps.
Similarly, in the Result section, the authors keep emphasizing that their “patterns align with former maps”, which further raises questions about the novelty and importance of this work. Some findings are presented without statistical validation and therefore unconvincing. For example, L255 “BD prediction accuracy...peaking at intermediate depths (15–30 cm: MEC = 0.657) with lower accuracy in surface layers (0–5cm: MEC = 0.598) and deep layers (60–100 cm: MEC = 0.656)”. Without testing for statistical significance, how can 0.656 represent “lower accuracy” compared to 0.657? Similarly, statements such as “all predictions maintained negligible bias (|ME| ≤ 0.019) across depth intervals” lack a defined threshold for “negligible”. Descriptions like “Conversely, pH predictions demonstrated superior accuracy: CV maintained strong performance across depths” appear subjective, without definition for “superior” or “strong”. Likewise, many descriptions are excessive and repetitive (eg., L268-270, L274-279), which obscure the main message.
Additionally, abbreviations (including BD, SD and the abbreviations of models) in Tables and Figures should be clearly defined in their captions to make them self-explanatory. Why is FRFS introduced in the Introduction section but QRF in the Method? Table 1 may be presented more clearly as a figure, and currently has a confusing caption. The statement in L271 “BD values increase from the coast inland” is unclear. Figure 6 might benefit from an overall analysis across depths, and consider adding relationships between BD and MAP (or other key covariates) in supplementary materials. L85 & 91, QRF should be explained upon its first mention. L111 is redundant with L108. L251, rephrase “conversely”.
Overall, the manuscript is informative and holds value but requires further refinement. The authors are encouraged to more clearly emphasize the importance and novelty of their work, revise redundant descriptions in Results while focusing on demonstrating statistical significance. With careful revision, this manuscript has considerable potential to make a meaningful contribution to the field.