Soil phenotyping: Integrating phenosoils and proximal soil sensing for spatiotemporal characterisation of managed soil variability

The characterisation of soil variability has challenged soil science since the early recognition that soils exhibit complex, structured spatial patterns across landscapes. As agriculture intensifies and soils are expected to deliver more—storing carbon, supporting biodiversity, and sustaining other key functions—there is an urgent need for scalable, quantitative methods to monitor and manage soil variability across space and time. Here we synthesise and advance a framework for soil phenotyping, defined as the quantitative assessment of soil structural, chemical, physical, and biological properties and their spatiotemporal patterns using proximal sensing—explicitly grounded in the distinction between genosoils (baseline soils under minimal human influence) and phenosoils (persistent management-modified soil expressions). Drawing parallels with field plant phenotyping, we argue that soils exhibit phenotypic responses arising from interactions among inherent soil capacity, environmental, and management drivers, and that these responses can be measured efficiently using high-throughput sensing. We review proximal soil sensing technologies, including electromagnetic induction and vis–NIR spectroscopy, and discuss their integration within multi-sensor platforms, digital soil mapping, and machine-learning workflows for data fusion, prediction, and uncertainty quantification. We position soil phenotyping as a practical pipeline from in situ sensing to spatially explicit indicators of soil condition and function, supporting precision agriculture, soil health assessment, and soil security planning. Finally, we identify key challenges for operational deployment, including calibration transferability, sensor harmonisation, spatiotemporal sampling design, and open, interoperable data frameworks, and outline research priorities to accelerate adoption in managed landscapes