Identifying soil signatures from soil moisture time series via a changepoint-based approach

Healthy soil plays a critical role in sustaining biodiversity, maintaining food production, and mitigating climate change through carbon capture. Soil moisture is an important measure of soil health that scientists model via soil drydown curves. The typical modelling process requires manually separating the soil moisture time series into segments representing the drying process and fitting exponential decay models to these segments to obtain an estimation of the key parameters. With the advancement of sensor technology, scientists can now obtain higher frequency measurements over longer periods in a larger number of locations. To enable automatic data processing and to obtain a dynamic view of the soil moisture drydown, a changepoint-based approach is developed to automatically identify structural changes in soil moisture time series.

Specifically, timings of the sudden rises in soil moisture over a long time series are captured and the parameters characterising the drying processes following the sudden rises are estimated simultaneously. An algorithm based on the penalised exact linear time (PELT) method was developed to identify the changepoints and estimate the model parameters. This method can be considered as a complement to the conventional soil moisture modelling. It requires little data pre-processing and can be applied to a soil moisture time series directly. Since each drying segment has its unique parameters, the method also has the potential of capturing any temporal variations in the drying process, thus providing a more comprehensive summary of the data.

The method was applied to the hourly soil moisture time series of nine field sites from the NEON data portal (https://data.neonscience.org/). Distributions and summary statistics of key model parameters, such as the exponential decay rate and the asymptotic soil moisture level, are produced for each field site. Investigating and comparing these quantities from different field sites enables the identification of soil signatures which can reflect the hydrological properties of the soil. Visualising the model parameters as a time series reveals the subtle temporal pattern of the drying process in some field sites.