Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m⁻² over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m⁻² (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m⁻² and a root mean squared error of 2.04 kg C m⁻². In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.

中文翻译：

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用于模拟土壤有机碳变化的时空机器学习

土壤有机碳 (SOC) 储量变化的空间解析估计对于支持旨在实现土地退化零增长和减缓气候变化的国家和国际政策是必要的。在这项工作中，我们报告了一种数据驱动的统计方法的开发、实施和应用，用于绘制时空 SOC 储量，以阿根廷为试点。我们使用分位数回归森林机器学习来预测 1982 年至 2017 年阿根廷 0-30 cm 深度、250 m 分辨率的年度 SOC 储量。该模型使用来自 36 年时间段的 5,000 多个 SOC 储量值和 35 个环境协变量进行校准. 我们使用时间低通滤波器预处理归一化差异植被指数 (NDVI) 动态协变量，以允许给定年份的 SOC 存量取决于当前和前几年的 NDVI。预测有适度的时间变化，整个国家的平均减少从 2.55 到 2.48 kg C m^-2在 36 年期间（相当于 211 Gg C 下降，占阿根廷 0-30 cm SOC 总储量的 3.0%）。同一时期，潘帕地区的 SOC 储量估计值从 4.62 千克降到 4.34 千克 C m ^-2 (5.9%)。在 2001-2015 年期间，预测的时间变化比使用政府间气候变化专门委员会和联合国防治荒漠化公约的方法 1 获得的时间变化大七倍。结果证明，预测的不确定性很大，主要是由于校准数据的数量有限且时空分布不佳，以及协变量的解释力有限。交叉验证证实 SOC 储量预测精度有限，平均误差为 0.03 kg C m ^-2以及 2.04 kg C m ^{-2 的}均方根误差。尽管存在很大的不确定性，但这项工作表明，只要 SOC 时空观测密度足够大，机器学习方法可用于时空 SOC 制图，并可能为土地管理者和政策制定者提供有价值的信息。