On the relevance of using artificial neural networks for estimating soil moisture content

Soil moisture is a key variable that defines the land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy balance and water balance. This paper investigates the utility of the widely adopted data-driven model, namely artificial neural networks (ANNs), for modeling the complex soil moisture dynamics. Datasets from three experimental soil covers (D1, D2, and D3), with thickness of 0.50 m, 0.35 m, and 1.0 m, comprising a thin layer of peat mineral mix over varying thickness of till, are considered in this study. Volumetric soil moisture contents at both the peat and the till layers were modeled as a function of precipitation, air temperature, net radiation, and ground temperature at different layers. Initial simulations illustrated that, in the absence of time-lagged meteorological variables, the ground temperature is the most influential state variable for characterizing the soil moisture, highlighting the strong link between the soil thermal properties and the corresponding moisture status. With the objective of extracting the maximum information from the most influential state variables (ground temperature), a higher-order neural networks (HONNs) model was developed to characterize the soil moisture dynamics. The HONNs resulted in relatively higher correlation coefficient, than traditional ANNs, for some of the soil moisture simulations. Time-lagged inputs were used to improve the model performance and obtain optimum results. The ANN models performed better than a previously developed conceptual model for estimating the depth-averaged soil moisture content. Results from the study indicate that modeling of soil moisture using ANNs is challenging but achievable, and its performance is largely influenced by the structure and formation of the soil covers, which in turn governs the dynamics of soil moisture variability.