Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional or national scale. Disaggregating these regional and/or national maps is applicable for farm-scale tasks, particularly in data-poor or limited situations. Although disaggregation is a frequently discussed topic in recent digital soil mapping literature, the uncertainty of the disaggregation process is not often discussed. Underestimation of inferential or predictive uncertainty in statistical modelling leads to inaccurate statistical summaries and overconfident decisions. The use of Bayesian inference allows for quantifying the uncertainty associated with the disaggregation process. In this study, a framework of Bayesian area-to-point regression kriging (ATPRK) is proposed for downscaling soil attributes, in particular, maps of soil organic carbon. An estimation of point support variograms from block-supported data was carried out using the Monte Carlo integration via the Metropolis–Hastings algorithm. A regional soil carbon map with a resolution of 100 m (block support) was disaggregated to 10 m (point support) information for a farm in northern New South Wales (NSW), Australia. The derived point support variogram has a higher partial sill and nugget, while the range and parameters do not deviate much from the block support data. The disaggregated fine-scale map (point support with a grid spacing of 10 m) using Bayesian ATPRK had an 87 % concordance correlation with the original coarse-scale map. The uncertainty estimates of the disaggregation process were given by a 95 % confidence interval (CI) limit. Narrow CI limits indicate that the disaggregation process gives a fair approximation of the mean soil organic carbon (SOC) content of the study site. The Bayesian ATPRK approach was compared with dissever, which is a regression-based disaggregation algorithm. The disaggregated maps generated by dissever had 96 % concordance correlation with the coarse-scale map. Dissever achieves this higher concordance correlation through an iteration process, while Bayesian ATPRK is a one-step process. The two disaggregated products were validated with 127 independent topsoil carbon observations. The validation concordance correlation coefficient for Bayesian ATPRK disaggregation was 23 %, while downscaled maps generated from dissever had 18 % concordance correlation coefficient (CCC). The advantages and limitations of both disaggregation algorithms are discussed.

中文翻译：

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使用贝叶斯面积到点回归克里金法分解区域范围的数字土壤图，用于农场规模的土壤碳评估

大多数土壤管理活动都是在农场规模上实施的，但是数字土壤图通常可以在区域或国家范围内获得。分解这些区域和/或国家地图适用于农场规模的任务，尤其是在数据贫乏或有限的情况下。尽管在最近的数字土壤制图文献中，分解是一个经常讨论的话题，但是分解过程的不确定性却不经常讨论。统计模型中推断性或预测性不确定性的低估会导致统计摘要不准确和决策过于自信。贝叶斯推断的使用允许量化与分解过程相关的不确定性。在这项研究中，提出了一种贝叶斯面积对点回归克里金（ATPRK）框架来缩小土壤属性，特别是，土壤有机碳分布图。使用蒙特卡洛积分通过Metropolis？Hastings算法从块支持的数据估计点支持变异函数。分辨率为100â€的区域土壤碳图米（块支持）被分解为10â??? 澳大利亚新南威尔士州北部（NSW）某农场的m（点支持）信息。派生的点支持变异函数图具有较高的局部门槛和块金，而范围和参数与块支持数据的偏差不大。分解后的精细比例尺地图（点支持，网格间距为10？m）使用贝叶斯ATPRK与原始粗比例图具有87％的一致性。分解过程的不确定性估计值由95％置信区间（CI）限制给出。狭窄的CI限值表明，分解过程给出了研究地点平均土壤有机碳（SOC）含量的近似值。贝叶斯ATPRK方法与Dissever进行了比较，后者是基于回归的分解算法。Dissever生成的分类地图与粗比例图具有96％的一致性。Dissever通过迭代过程实现了这种更高的一致性相关性，而贝叶斯ATPRK是一步式过程。通过127个独立的表土碳观测值对这两个分类的产品进行了验证。贝叶斯ATPRK分解的验证一致性相关系数为23％，而根据异议产生的缩小地图具有18％一致性相关系数（CCC）。讨论了两种分解算法的优缺点。