Soil temperature and moisture are important variables controlling ecological processes, but continuous high-resolution data are rarely available. Therefore, we used the correlation with widely accessible meteorological variables, including air temperature and precipitation, to develop models that predict time series of soil temperature and moisture. To model high-resolution time series, predictor and target variables had a temporal resolution of 1 h. We tested the applicability of Gated Recurrent Units with time series from one exemplary site. The models showed a high predictability on the four years test set with a mean absolute error of 0.87°C for soil temperature and 3.20% volumetric water content for soil moisture. We further investigated the plausibility of the models by passing simplified synthetic data to the trained models and thereby proved their ability to reflect known processes. Finally, we showed the potential to apply the models to other sites and soil depths using transfer learning.

中文翻译：

---

用于模拟土壤温度和湿度时间序列的门控循环单元：性能和过程反射率评估


土壤温度和湿度是控制生态过程的重要变量，但很少有连续的高分辨率数据可用。因此，我们利用与广泛可用的气象变量（包括气温和降水）的相关性来开发预测土壤温度和湿度时间序列的模型。为了对高分辨率时间序列进行建模，预测变量和目标变量的时间分辨率为 1 h。我们用来自一个示例站点的时间序列测试了门控循环单元的适用性。这些模型在四年测试集上显示出很高的可预测性，土壤温度的平均绝对误差为 0.87°C，土壤水分的体积含水量为 3.20%。我们通过将简化的合成数据传递给经过训练的模型来进一步研究模型的合理性，从而证明它们能够反映已知过程。最后，我们展示了使用迁移学习将这些模型应用于其他地点和土壤深度的潜力。