Mapping Shrub Fractional Abundance: A Multi-Scale Remote Sensing and Machine Learning Framework for Arid Ecosystem Monitoring

Shrubs, characterized by their multiple dwarf stems, are a dominant plant functional type in arid and semi-arid regions, which cover 40% of Earth's land surface. These ecosystems are fragile and highly susceptible to climate change and human disturbances. The abundance of shrubs serves as an important indicator of ecosystem health, and their projected increase due to CO₂ fertilization and warming climates could significantly alter ecosystem functioning, exacerbate desertification, and impact essential ecosystem services. Monitoring shrub fractional abundance—the proportion of vegetative cover occupied by shrubs—is crucial for understanding these dynamics and guiding sustainable management practices. However, mapping shrub fractional abundance over large areas presents challenges due to their small crowns, sparse distribution, and high density, rendering traditional field surveys and conventional satellite remote sensing techniques inadequate. In this study, we propose an innovative two-step approach that integrates sub-meter resolution Google Earth (GE) imagery with decametric-resolution Sentinel-2 time-series data for accurate and scalable shrub fractional mapping. Our methodology consists of two main steps: (1) a semi-automatic process that uses GE imagery to delineate 1.31 million shrub crowns and generate high-quality training data, and (2) a machine learning model that combines spectral and phenological features from Sentinel-2 data to upscale GE-derived shrub fractional abundance across diverse arid and semi-arid landscapes in Inner Mongolia, China. The model achieved strong predictive accuracy (R² = 0.70), with phenological features—particularly during early May, mid-June, and late September—proving critical for distinguishing shrubs from seasonal vegetation. These periods correspond to key phenophases, including germination, peak growth, and senescence of grasses, which contrast with the perennial phenology of shrubs, highlighting the significance of phenology in differentiating shrubs from dynamic seasonal vegetation. Our results demonstrate the effectiveness of integrating multi-scale remote sensing data with machine learning to address existing limitations in shrub monitoring. This approach provides a scalable and transferable framework for global mapping of shrub fractional abundance, offering valuable insights into shrub encroachment and its implications for ecosystem health in the context of changing climatic and anthropogenic conditions.