The dynamics of field soil water retention curves predicted by autoencoder neural network

The matric potential plays a pivotal role in understanding of water movement, plant water availability, and mechanical stability. In lack of direct measurements, the matric potential dynamics must be deduced from soil water content values, using the soil water retention curve. This approach is of particular importance at larger scales where only the water content (but not the potential) can be deduced from satellite data. However, because the relationship between water content and matric potential in natural field soils is highly ambiguous, not unique and dynamic, the prediction of matric potential from water content data is a big challenge. This ambiguity is related to different structures controlling drainage and wetting, dynamic effects, and seasonal changes of structures controlling the water distribution. In this study we present an autoencoder neural network as a new approach to analyze the soil moisture dynamics and to predict matric potential from water content data. The autoencoder compresses the water content time series into a site-specific feature (denoted as autoencoder value, AUV) that is representative of the underlying soil moisture dynamics. The AUV can then be used as predictor of the matric potential and the highly hysteretic soil water retention curve. The approach was tested successfully for nine soil profiles in the region of Solothurn (Switzerland). Three sites were chosen to establish the connection between AUV and the ambiguous soil water retention curve using a deep neural network, that was then applied to predict the matric potential dynamics of the other six sites. This method offers the potential to (i) deduce matric potential dynamics by relying solely on soil water content measurements (including satellite data), even when strong seasonal effects challenge standard methods, and (ii) serves as a warning system for changes in soil properties and in the intricate relationship between soil water content and matric potential dynamics.