Soil contamination is complex, multifactorial process that threatens ecosystem services, agricultural productivity, and sustainability. Traditional field-based methods for evaluating soil contamination and its restoration are often labor-intensive, time-consuming, and spatially constrained. The integration of remote sensing data with in-situ observations and modelling frameworks enhances our capacity to map degradation patterns and assess recovery trajectories.This session targets soil contamination monitoring via remote and proximal sensing as well as spanning other soil degradation processes resulting from contamination such as erosion, SOC decline, salinization, landscape alteration, compaction.. We welcome contributions using UAV, airborne and satellite (multispectral/hyperspectral, SAR, LiDAR) together with in-situ/proximal sensors (Vis-NIR/DRS, XRF, EM/GPR), including: (i) indicator retrieval (e.g., SOC, texture/clay, moisture, roughness, crusting, vegetation stress); (ii) bare-soil compositing/time-series workflows; (iii) physics-based (or process-based) vs. ML approaches; (iv) sensor/data fusion ; (v) spectral libraries & transferability; (vi) case studies tracking degradation and recovery trajectories under diverse management actions or amendments (without restricting to any specific remediation strategy). The aim is to advance scalable, reproducible workflows that deliver decision-ready products for land degradation assessment and restoration.