Forest soils underpin ecosystem resilience and productivity but are increasingly threatened by natural and anthropogenic disturbances. Monitoring post‑disturbance soil degradation at operational scales remains challenging in forests, where ground‑signal obstruction and reliance on proxy indicators constrain remote sensing (RS) applications. To identify where RS can benefit soil monitoring and support emerging reporting needs, we developed a structured evidence map of studies assessing post‑disturbance forest soil degradation using RS methods. From 4,338 records, 72 primary studies were synthesized across disturbance types, biomes, platforms, scales, and indicators. The evidence base is dominated by wildfire and harvesting, reflecting disturbance pathways that produce observable surface impacts. Multispectral satellite data remain the primary tool for mapping post‑fire severity and erosion‑related indicators, while LiDAR and stereo‑photogrammetry are most often used to quantify surface deformation after harvest operations. Indicators tied to subsurface physical, chemical, or biological change remain sparsely represented due to observability limits. Overall, RS is most effective for mapping disturbance footprints, detecting surface‑expressed indicators, and stratifying landscapes for targeted field assessment, rather than directly measuring soil properties. This evidence map clarifies the benefits and limits of RS, identifies persistent gaps, and highlights priorities for developing disturbance‑aware soil‑monitoring frameworks. It also specifies which soil indicators are defensibly observable with RS and which require complementary approaches. By linking disturbance processes to observable indicators, this synthesis helps define realistic RS‑supported objectives for reporting frameworks within national forest monitoring and assessment programs.