Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning

Soil moisture information is important for a wide range of applications including hydrologic modelling, climatic modelling and agriculture. L-band passivemicrowave satellite remote sensing is themost feasible option to estimate near-surface soil moisture (similar to 0-5 cmsoil depth) over large extents, but its coarse resolution (similar to 10s of km) means that it is unable to capture the variability of soil moisture in detail. Therefore, different downscaling methods have been tested as a solution tomeet the demand for high spatial resolution soil moisture. Downscaling algorithms based on the soil thermal inertia relationship between diurnal soil temperature difference (Delta T) and dailymean soil moisture content (mu(SM)) have shown promising results over arid and semi-arid landscapes. However, the linearity of these algorithms is affected by factors such as vegetation, soil texture and meteorology in a complex manner. This study tested a (i) Regression Tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, given the ability of machine learning algorithms to capture complex, non-linear relationships between predictors and responses. Downscaled soil moisture from the RT, ANN and GPR models showed root mean square errors (RMSEs) of 0.03, 0.09 and 0.07 cm(3)/cm(3) compared to airborne retrievals and unbiased RMSEs (ubRMSEs) of 0.07, 0.08 and 0.05 cm(3)/cm(3) compared to in-situ observations, respectively. The study showed encouraging results to integrate machine learning techniques in estimating near-surface soil moisture at a high spatial resolution. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study tested downscaling algorithms based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia, showing promising results in integrating machine learning techniques for estimating near-surface soil moisture at a high spatial resolution.