The increasing availability of remotely sensed soil moisture data offers new opportunities for data-driven modelling approaches as alternatives for process-based modelling. This study presents the applicability of transfer function-noise (TFN) modelling for predicting unsaturated zone conditions. The TFN models are calibrated using SMAP L3 Enhanced surface soil moisture data. We found that soil moisture conditions are accurately represented by TFN models when exponential functions are used to define impulse-response functions. A sensitivity analysis showed the importance of using a calibrated period which is representative of the hydrological conditions for which the TFN model will be applied. The IR function parameters provide valuable information on water system characteristics, such as the total response and the response times of soil moisture to precipitation and evapotranspiration. Finally, we encourage exploring the possibilities of TFN soil moisture modelling, as predicting soil moisture conditions is promising for operational settings.

遥感土壤水分数据的可用性不断提高，为基于数据的建模方法提供了新的机会，这些方法可用于数据驱动的建模方法。这项研究提出了传递函数噪声（TFN）模型在预测非饱和带条件方面的适用性。使用SMAP L3增强的表层土壤湿度数据校准TFN模型。我们发现，当使用指数函数定义脉冲响应函数时，TFN模型可以准确地表示土壤湿度条件。敏感性分析显示了使用校准周期的重要性，该周期代表了将应用TFN模型的水文条件。红外功能参数提供有关水系统特性的有价值的信息，例如土壤水分对降水和蒸散的总响应和响应时间。最后，我们鼓励探索TFN土壤湿度建模的可能性，因为预测土壤湿度条件对于操作环境很有希望。