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Geowissenschaftliches KolloquiumMittwoch, 02. Februar 2022 - 12:15 Uhr | ||
 | Near surface characterization using geophysical and optical methods supported by machine learning Sustainable management of water and nutrients in agricultural landscapes requires reliable data to support informed decisions. As geophysical characterization offers insights in processes driving the soil–plant–atmosphere continuum there is a great potential to better characterize and quantify the related processes noninvasively from the plot to landscape scale. For sustainable food production, knowledge of the horizontal as well as vertical variability of soil organic C (SOC) and soil moisture at field scale is crucial. I will illustrate how spatial geophysical data help to characterize a large field site in terms of SOC and soil moisture. In particular machine learning models using depth-related data from multiple electromagnetic induction sensors and a gamma-ray spectrometer can provide insights into the field scale variability. | |
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