Soil thickness is a crucial parameter for local, regional and globalscale assessments, including those related to carbon cycles, water retention capacity, and landslide risk in the context of biogeochemical, soil physical, and slope stability models. This study had two primary objectives: 1) to predict maps depicting the probability of the A-horizon thickness exceeding 15 cm (AT15) and the A and B-horizon thickness exceeding 75 cm (soil thickness, ST75), across the expansive mountainous regions of Japan. 2) To evaluate the enhancement of model performance through the preselection of the maximum value and implementation of enhanced modeling using gridded modern soil survey data to utilize geographically clustered data. Initially, the study modeled the SOC, bulk density, and coarse fraction proportion using data from a modern soil survey of 0–30 cm. Subsequently, we employed a random forest model for AT15 and ST75 using a dataset of geographically clustered legacy soil profiles as training data, and topographic, vegetation, climatic, and volcanic attributes, and the predicted soil organic carbon and related properties as explanatory variables. We reduced the number of legacy soil profiles in the densely sampled areas by selecting the maximum soil thickness before modeling. The study assessed the final model performance at the national scale, with the accuracy and F1 scores reaching 0.691 and 0.754 for AT15 and 0.600 and 0.511 for ST75, respectively. Maximum value selection proved effective for modeling ST75 but not AT15. Although an enhanced model incorporating data from legacy and modern surveys did not improve accuracy at the national level, it did improve recall in sparsely sampled areas, for ST75. The resulting spatial pattern of the predicted ST75 effectively captured variations in soil thickness associated with topography, geology, and tephra deposition. However, ST75, representing pedological soil thickness, tended to be strongly underestimated compared to geotechnical soil thickness, including the C-horizon, especially in areas affected by tephra deposition. This study represents a significant contribution as it delivers Japan's first national-scale soil thickness map and an approach for utilizing clustered legacy data. It is expected to find practical applications in estimating water storage capacity for forest management and other relevant purposes.

中文翻译：

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利用传统和现代土壤调查绘制日本丘陵和山区的全国尺度土壤厚度概率图


土壤厚度是地方、区域和全球范围评估的关键参数，包括与生物地球化学、土壤物理和边坡稳定性模型背景下的碳循环、保水能力和滑坡风险相关的参数。这项研究有两个主要目标：1) 预测描绘广阔山区 A 地平线厚度超过 15 厘米 (AT15) 以及 A 和 B 地平线厚度超过 75 厘米（土壤厚度，ST75）的概率的地图日本的。 2）通过预选最大值和利用网格化现代土壤调查数据利用地理聚类数据实施增强建模来评估模型性能的增强。最初，该研究使用 0-30 厘米现代土壤调查的数据对 SOC、容重和粗粒比例进行了建模。随后，我们采用了 AT15 和 ST75 的随机森林模型，使用地理上聚集的遗留土壤剖面数据集作为训练数据，并以地形、植被、气候和火山属性以及预测的土壤有机碳和相关属性作为解释变量。我们通过在建模前选择最大土壤厚度来减少密集采样区域中遗留土壤剖面的数量。该研究评估了全国范围内的最终模型性能，AT15 的准确率和 F1 分数分别达到 0.691 和 0.754，ST75 的准确率和 F1 分数分别达到 0.600 和 0.511。事实证明，最大值选择对于 ST75 建模有效，但对于 AT15 建模无效。尽管整合了传统调查和现代调查数据的增强模型并没有提高国家层面的准确性，但它确实提高了 ST75 稀疏采样区域的召回率。 由此产生的预测 ST75 的空间模式有效地捕获了与地形、地质和火山灰沉积相关的土壤厚度的变化。然而，与岩土土壤厚度（包括 C 层）相比，代表土壤厚度的 ST75 往往被严重低估，特别是在受火山灰沉积影响的地区。这项研究做出了重大贡献，因为它提供了日本第一个全国范围的土壤厚度图和利用集群遗留数据的方法。预计它将在估计森林管理和其他相关目的的储水能力方面找到实际应用。