The regional assessment of near-surface soil moisture is essential for agricultural and environmental monitoring. Although it frequently facing difficulties due to insufficient in-situ measurements. This study investigates a machine learning approach to predict soil moisture fracture utilizing publicly accessible remote sensing data, such as vegetation indices, land surface temperature and topography details. An example dataset from an agricultural region in northwestern Spain (Zamora region) including satellite observations with in-situ measurements, functioned as a testbed for creating an Extreme Gradient Boosting model.
The model showed a promising ability to predict soil moisture using transferable data. The initial findings suggest that the methodology can provide insights for regional soil moisture evaluation, helping applications such as drought monitoring and water resource management, especially in places where direct measurements are not available. Further validation in different settings is required to determine wider applicability.
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