国家尺度土壤有机碳储量空间分布预测建模方法的评价
Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale
摘要：
土壤有机碳在全球碳收支中起着重要作用。它可以作为大气碳的源或汇，从而可能影响气候变化的进程。改进在国家尺度上模拟SOC储量空间分布的工具是一个优先事项，无论是监测SOC的变化，还是作为全球碳循环研究的投入。在本文中，我们比较和评估两个最近的和有前途的建模方法。首先，我们考虑了几个越来越复杂的提升回归树（BRT），这是统计学习领域的一种方便有效的多元回归模型。此外，我们考虑了一个强大的地质统计方法耦合到BRT模型。对法国土壤监测网的数据集进行了不同方法的测试，并采用了一致的交叉验证程序。我们发现，当BRT模型中包含有限数量的预测因子时，通过对残差进行稳健的地质统计建模，BRT的独立预测得到了显着改善。然而，当包括几个SOC驱动程序的数据时，独立的BRT模型预测并没有通过地质统计建模得到显着改善。因此，在后一种情况下，BRT预测可能被认为是足够的，而不需要地质统计建模，前提是：i）在模型拟合和验证时要小心，ii）数据集不允许对局部空间自相关进行建模，就像许多国家系统抽样方案一样。
英文摘要：
Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes.
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