Soil temperature and soil moisture are key drivers of various soil ecological processes, which implies a significant importance of datasets including their variations in space, depth and time (4D). Current gridded products typically have a low resolution, either spatially or temporally. Here, we aim at modelling and explaining high-resolution soil temperature and soil moisture patterns in 4D for a 400 km2 study area in a heterogeneous landscape. Our target resolution of 10 m in space, 10 cm in depth, and 1 h in time allows capturing small-scale variations as well as short-term dynamics. We used multi-depth soil temperature and soil moisture measurements from 212 locations and linked them to 45 potential predictors, representing meteorological conditions, soil parameters, vegetation coverage, and landscape relief. We trained random forest models that were able to predict soil temperature with a mean absolute error of 0.93 °C and soil moisture with a mean absolute error of 4.64 % volumetric water content. Continuous model predictions enabled a comprehensive analysis of 4D patterns and confirmed that the selected resolution is essential to capture soil temperature and soil moisture variations at the landscape scale. In addition to a strongly pronounced annual cycle and recognisable influences on the diurnal cycle, there were significant differences between the land uses and patterns due to the relief, which also varied along the depth gradient. By applying interpretable machine learning techniques, we investigated the detailed influence of all drivers and discussed overlapping effects that led to the prediction patterns.

中文翻译：

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高分辨率土壤温度和土壤湿度在空间、深度和时间上的模式：一种可解释的机器学习建模方法


土壤温度和土壤湿度是各种土壤生态过程的关键驱动因素，这意味着数据集的重要性，包括它们在空间、深度和时间 （4D） 上的变化。当前的格网化产品通常在空间或时间上具有较低的分辨率。在这里，我们的目标是在异质景观中为 400 km2 的研究区域在 4D 中建模和解释高分辨率土壤温度和土壤湿度模式。我们的目标分辨率为 10 m 空间、10 cm 深度和 1 h 时间，可以捕获小尺度变化和短期动态。我们使用了来自 212 个位置的多深度土壤温度和土壤水分测量，并将它们与 45 个潜在预测因子联系起来，代表气象条件、土壤参数、植被覆盖和景观地势。我们训练了随机森林模型，这些模型能够预测土壤温度（平均绝对误差为 0.93 °C）和土壤水分（平均绝对误差为 4.64 % 体积含水量）。连续的模型预测能够对 4D 模式进行全面分析，并证实所选分辨率对于在景观尺度上捕获土壤温度和土壤湿度变化至关重要。除了非常明显的年周期和对昼夜周期的明显影响外，由于地势，土地利用和模式之间存在显着差异，这也沿深度梯度而变化。通过应用可解释的机器学习技术，我们调查了所有驱动因素的详细影响，并讨论了导致预测模式的重叠效应。