Underestimated shrubland distribution in the Eurasian drylands

• An integrated phenology-pixel and multi-strategy sampling framework improved shrubland mapping. • Achieved a mean F1 score of 67.05%, surpassing existing global land cover products. • Identified a 60% shrub cover threshold as key for reliable Sentinel-2 classification. The arid and semi-arid regions of the Eurasian continent (the Eurasian arid region) are home to extensive shrublands and these shrublands are critical components of the regional biomes. However, identifying shrublands and distinguishing them from grasslands via remote sensing remains challenging due to their spectral and phenological similarity, as evidenced by the varying distribution areas and patterns of shrublands across each common land cover product. To address this, we combined a multi-strategy sampling framework with a phenology-pixel-based method to map shrubland distribution in the arid Eurasian region. We achieved a mean F1 score of 67.05%, notably higher than that of existing global land cover datasets. The total mapped shrubland area is approximately 427,643 km 2 , exhibiting a clear latitudinal peak (30°–50°N) and multimodal longitudinal distribution pattern. In different biomes, climatic conditions—moisture or temperature—regulate the optimal phenological windows by driving functional trait divergence between shrubland and grassland. Background shrubland cover strongly determines identification accuracy. When shrubland coverage exceeds 60%, mapping accuracy shifts from “detectable” to “reliable”. Below this threshold, classification accuracy is limited by phenological noise and topography interference, whereas above it, canopy continuity enhances spectral separability and reduces terrain-related noise. This study demonstrates that shrub coverage, phenological timing, and terrain collectively regulate mapping accuracy. The phenology-optimized classification framework significantly outperforms existing common shrubland distribution datasets, especially in complex microtopography and transition zones. Improved shrubland mapping is critical for evaluating ecosystem health and investigating carbon cycling in arid regions.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2026-02-01
DOI
https://doi.org/10.1016/j.jag.2026.105144
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Underestimated shrubland distribution in the Eurasian drylands

Guang Zhao, Huihui Liu, Nan Cong, Yangjian Zhang et al.
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing in Agriculture
article

Underestimated shrubland distribution in the Eurasian drylands

Guang Zhao, Huihui Liu, Nan Cong, Yangjian Zhang, Li Liu, Zhoutao Zheng
article en

Abstract

• An integrated phenology-pixel and multi-strategy sampling framework improved shrubland mapping. • Achieved a mean F1 score of 67.05%, surpassing existing global land cover products. • Identified a 60% shrub cover threshold as key for reliable Sentinel-2 classification. The arid and semi-arid regions of the Eurasian continent (the Eurasian arid region) are home to extensive shrublands and these shrublands are critical components of the regional biomes. However, identifying shrublands and distinguishing them from grasslands via remote sensing remains challenging due to their spectral and phenological similarity, as evidenced by the varying distribution areas and patterns of shrublands across each common land cover product. To address this, we combined a multi-strategy sampling framework with a phenology-pixel-based method to map shrubland distribution in the arid Eurasian region. We achieved a mean F1 score of 67.05%, notably higher than that of existing global land cover datasets. The total mapped shrubland area is approximately 427,643 km 2 , exhibiting a clear latitudinal peak (30°–50°N) and multimodal longitudinal distribution pattern. In different biomes, climatic conditions—moisture or temperature—regulate the optimal phenological windows by driving functional trait divergence between shrubland and grassland. Background shrubland cover strongly determines identification accuracy. When shrubland coverage exceeds 60%, mapping accuracy shifts from “detectable” to “reliable”. Below this threshold, classification accuracy is limited by phenological noise and topography interference, whereas above it, canopy continuity enhances spectral separability and reduces terrain-related noise. This study demonstrates that shrub coverage, phenological timing, and terrain collectively regulate mapping accuracy. The phenology-optimized classification framework significantly outperforms existing common shrubland distribution datasets, especially in complex microtopography and transition zones. Improved shrubland mapping is critical for evaluating ecosystem health and investigating carbon cycling in arid regions.

International Journal of Applied Earth Observation and GeoinformationVol. 146
Chinese Academy of Sciences (CN), Beijing Normal University (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN), Hebei University (CN)
Life in Land
Openalex Percentile: Top 66%
Remote Sensing in Agriculture
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