The soil beneath our feet is home to a wealth of biodiversity: up to 25% of all species on earth are estimated to live in the soil. Besides the intrinsic value of the soil biota, they are also of large economical importance due to the important ecosystem services they provide as a community, such as water retention, nutrient cycling, and carbon storage. Soil biodiversity is being threatened, however; in the European Union, it has been estimated that more than 60% of soils are in bad health due to human induced stressors, however, such as intensive agriculture, urbanization, and industry. Routine monitoring of the soil community is therefore of importance to guide policy and management practices that can prevent further loss of diversity. Unfortunately, the soil biota is notoriously difficult to monitor due to the required labour, expertise and costs of conventional monitoring methods, which makes routine biomonitoring of the entire community infeasible. Consequently, knowledge of the complex relations between the soil biota, stressors and function is lacking. In my PhD project, I research whether conventional morphology-based methods can be replaced by environmental DNA (eDNA) metabarcoding methods to enable large scale monitoring. Furthermore, by linking eDNA community profiles to stressors and ecosystem services through gradient analyses, network analyses and machine learning, I try to generate soil health indices that are relevant for policy and management decisions.