One of the main problems facing society today is soil degradation and contamination. Soil quality affects environmental and human health: directly, by regulating the retention, immobilisation, or mobilisation of pollutants and thus controlling exposure pathways; and indirectly, by shaping water quality and food security through its effects on infiltration, runoff generation, and nutrient cycling. However, soil quality decline goes beyond contamination and stems from interacting processes such as erosion, organic carbon loss, salinization/sodification, compaction and reduced infiltration, surface sealing/crusting, structural degradation, biodiversity loss, and landscape alteration. These factors collectively weaken vegetation performance and reduce the resilience of ecosystems.
Although traditional field-based methods for evaluating soil degradation and contamination, and assessing restoration outcomes are essential, they are labour-intensive, time-consuming, and spatially constrained. Integrating remote sensing with in-situ observations and modelling enhances the ability to map degradation, identify hotspots and drivers, and quantify recovery trajectories. This session combines the in-situ evaluation of the relationship between soil quality and environmental and human health, via exposure to contaminants, with the use of remote and proximal sensing to monitor and assess soil degradation and its impacts on environmental and human health risk. We invite colleagues to present their research and to establish new cross-cutting, multidisciplinary collaborations aimed at proposing solutions and identifying soil health–related risks, as well as risks to environmental and human health. We welcome contributions using UAV, airborne, and satellite data together with in-situ and proximal sensors (DRS, XRF, EM, GPR), including: (i) indicator retrieval (e.g., SOC, texture, moisture, vegetation stress, contaminant proxies); (ii) bare-soil compositing and time-series workflows; (iii) physics- (or process-) based versus machine-learning approaches; (iv) sensor and data fusion; (v) spectral libraries and transferability; and (vi) case studies tracking degradation and recovery trajectories under diverse management actions, amendments, mitigation, or remediation strategies. The aim is to develop scalable, reproducible workflows that produce decision-ready outputs for assessing land degradation, planning restoration, and reducing environmental and human health risks.