EGU24-12837, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Mapping Regional Sub-Surface Soil Moisture Dynamics and Extremes on the Large Scale Through Data Fusion

Toni Schmidt1,2, Martin Schrön3, Steffen Zacharias3, Till Francke4, and Jian Peng1,2
Toni Schmidt et al.
  • 1Helmholtz Centre for Environmental Research – UFZ, Department of Remote Sensing, Leipzig, Germany
  • 2Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, Germany
  • 3Helmholtz Centre for Environmental Research – UFZ, Department of Monitoring and Exploration Technologies, Leipzig, Germany
  • 4Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany

Soil moisture products play a pivotal role in monitoring and predicting droughts that affect crop yield, water supply, and land–atmosphere interactions. The availability of various satellite-based soil moisture products allows for a comprehensive investigation of droughts on a large scale. However, limitations in their spatial sampling impact their suitability for regional applications. Accurately inferring sub-pixel heterogeneity is crucial for a representative understanding of regional dynamics and their implications for drought assessment across diverse landscapes. Furthermore, the shallow vertical support of satellite-based soil moisture products hinder the detection and quantification of droughts within the sub-surface. This study leverages multi-scale data fusion, aiming to replicate soil moisture extremes both regionally and in the sub-surface. We integrate ground-based Cosmic-Ray Neutron Sensing (CRNS) data, representing soil moisture within an extensive soil volume, with high-resolution Sentinel-1 data. Employing machine learning models that account for spatiotemporal autocorrelations, our objective is to generate gridded soil moisture data representing regional sub-surface dynamics. Tested in a German catchment, our approach tackles challenges associated with the scarcity of CRNS stations and the complexities of integrating multi-scale data. Our findings establish a foundation for monitoring regional droughts in the sub-surface across extensive areas.

How to cite: Schmidt, T., Schrön, M., Zacharias, S., Francke, T., and Peng, J.: Mapping Regional Sub-Surface Soil Moisture Dynamics and Extremes on the Large Scale Through Data Fusion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12837,, 2024.