Structural Recovery of Overlooked Shrublands Drives Asymmetric Restoration in Dryland Ecosystems

Current remote sensing of dryland ecosystems is fundamentally limited by a reliance on vegetation indices (“greenness”), which struggle to disentangle mixed pixel signals and fail to capture the non‐photosynthetic structural components critical for resilience. This “greenness bias” obscures the dynamics of sparse vegetation and leads to significant uncertainties in distinguishing true restoration from ephemeral greening. Here, we developed a novel spatio‐temporal framework integrating continuous change detection and classification with spectral mixture analysis to decouple sub‐pixel photosynthetic vegetation, non‐photosynthetic vegetation (NPV), and soil fractions across the temperate drylands of China. By reconstructing 21‐year continuous time series of endmember abundance, we mapped previously undetected sparse shrublands, revealing they constitute a dominant “landscape skeleton” covering nearly half of the vegetated area. Our results uncover a profound asymmetry in ecosystem transitions: while degradation typically manifests as an abrupt collapse of vegetation–soil coupling, recovery follows a distinct “structure‐first” hysteresis. This recovery pathway is driven by the gradual accumulation of NPV, which stabilizes substrates and facilitates subsequent canopy expansion. Furthermore, trajectory analysis demonstrates that these resilience mechanisms are strictly substrate‐dependent, with restoration pathways diverging significantly across sandy, gravelly, and saline substrates. By moving beyond pixel‐level greenness to sub‐pixel structural characterization, this study provides new insights into the asymmetric processes shaping drylands and offers a robust, spatially explicit tool for monitoring adaptive restoration under climatic and anthropogenic pressures.