Transfer Function‐Noise Modeling Using Remote Sensing Data to Characterize Soil Moisture Dynamics: a Data-driven Approach

Michiel Pezij, Denie C.M. Augustijn, Dimmie M.D. Hendriks, Suzanne J.M.H. Hulscher

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Although many consider soil moisture as an important hydrological variable, the application of soil moisture modelling for operational water management is limited. The availability of high-resolution remotely sensed soil moisture data leads to new opportunities for exploring data-driven modelling methods. In this study, we assessed the applicability of transfer function‐noise modeling (TFN) for describing soil moisture dynamics. TFN has been applied in groundwater modelling for describing long-term dynamics. We show that TFN is useful for soil moisture modelling. TFN applies an autoregressive-moving-average method (ARMA) to relate observed time series to input stresses using impulse-response functions. The input stress is convoluted with the impulse-response function to determine the soil moisture response due to that stress. The advantage of TFN is that it does not require prior assumptions on system processes.

In this study, the Python 3 package Pastas is utilized for setting up the TFN model. Furthermore, precipitation and reference crop evapotranspiration stresses are used to explain soil moisture dynamics. The SMAP L3 Enhanced surface soil moisture product is used as a training dataset. We found that the TFN can accurately describe soil moisture dynamics. In particular, the time series model can be used to characterize the response of soil moisture to precipitation and evapotranspiration stresses. The individual impulse-response functions for precipitation and evapotranspiration describe the sensitivity of soil moisture in terms of absolute change and reaction time to these stresses. The spatial distribution of these attributes describes water system characteristics. The results of this study enable water management to take more robust decisions, as water managers get insight in unsaturated zone dynamics. Further research may focus on the applicability of TFN for predicting short-term soil moisture dynamics.
Original languageEnglish
Number of pages1
Publication statusPublished - 2019
EventAGU Fall Meeting 2019 - San Francisco, United States
Duration: 9 Dec 201913 Dec 2019


ConferenceAGU Fall Meeting 2019
Country/TerritoryUnited States
CitySan Francisco
Internet address


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