Global daily 9km remotely sensed soil moisture (2015-2025) with microwave radiative transfer-guided learning

This research presents a global dataset of daily soil moisture at a 9km resolution, covering the period from 2015 to 2025. The dataset was generated using a novel approach that combines microwave radiative transfer modeling with machine learning techniques. This method aims to improve the accuracy of soil moisture estimation, which is crucial for understanding terrestrial ecosystems and their responses to environmental changes. The generated data provides a valuable resource for researchers studying climate, hydrology, agriculture, and environmental management. By offering a consistent and high-resolution view of soil moisture across the globe, this dataset can support a wide range of applications, from drought monitoring to crop yield prediction and carbon cycle studies.