  Soil moisture dynamics provide an indicator of soil health that scientists
model via drydown curves. The typical modelling process requires the soil
moisture time series to be manually separated into drydown segments and then
exponential decay models are fitted to them independently. Sensor development
over recent years means that experiments that were previously conducted over a
few field campaigns can now be scaled to months or years at a higher sampling
rate. To better meet the challenge of increasing data size, this paper proposes
a novel changepoint-based approach to automatically identify structural changes
in the soil drying process and simultaneously estimate the drydown parameters
that are of interest to soil scientists. A simulation study is carried out to
demonstrate the performance of the method in detecting changes and retrieving
model parameters. Practical aspects of the method such as adding covariates and
penalty learning are discussed. The method is applied to hourly soil moisture
time series from the NEON data portal to investigate the temporal dynamics of
soil moisture drydown. We recover known relationships previously identified
manually, alongside delivering new insights into the temporal variability
across soil types and locations.
