The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels.

中文翻译：

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多尺度土壤有机碳储量变化的时空建模——以匈牙利为例


土壤有机碳 （SOC） 的作用不仅对众多土壤功能和过程至关重要，而且对于解决我们面临的各种环境危机和挑战也至关重要。因此，对 SOC 时空变化信息的需求正在增加，这带来了新的方法挑战，例如需要有关 SOC 和 SOC 变化的信息，并在各种空间尺度和时间周期中具有量化的不确定性。我们的目标是提出一种基于机器学习和时空地理统计学相结合的方法，以预测 SOC 储量的时空变化，并在各种空间支持（即点支持、1 × 1 公里、5 × 5 公里、10 × 10 公里、25 × 25 公里、县和整个国家）下为匈牙利预测 1992 年至 2016 年间的不确定性。地统计的作用至关重要，因为它考虑了插值误差的时空相关性，这对于可靠地量化与空间聚合 SOC 存量和 SOC 存量变化预测相关的不确定性至关重要。使用 5 次重复 10 倍遗漏-定位-退出交叉验证来评估点支撑预测和不确定性量化，为 SOC 存量（ME = −0.897，RMSE = 19.358，MEC = 0.321 和 G = 0.912）和 SOC 存量变化（ME = 0.414，RMSE = 16.626，MEC = 0.160，G = 0.952）产生可接受的结果。我们编制了一系列 1992 年至 2016 年间每个支撑的 SOC 股票预测图，以及量化的不确定性，这在匈牙利是前所未有的。结果表明，支持越大，预测不确定性越小，这有助于识别和划定具有统计学意义的 SOC 存量变化的较大区域。 此外，该方法可以克服 SOC 时空建模方法的局限性，允许预测任何年份、时间段和空间尺度的 SOC 和 SOC 变化，并具有量化的不确定性。该方法能够满足国家和国际层面对 SOC 动态信息的当前和预期需求。