Erika Lück, Julien Guillemoteau, Jens Tronicke, Udo Rummel, Wilfried Hierold

• Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering. Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes. Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture. Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany. Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points. In addition, soil analysis and classification results are available for 132 regularlyInformation regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering. Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes. Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture. Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany. Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points. In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m. We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data. Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil. For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture. While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function. We observe a mean prediction error for soil moisture of +/-0.034 m3 & BULL; m? 3 using all observation points while we notice that point-specific models further reduce the error. Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.…