Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Liu, Zhonghua; Feng, Yuhao; Cao, Xin; Peñuelas, Josep; Descals, Adrià; Yang, Daryl; Cheung, Ka Kin; Liu, Shuwen; Lin, Ziyu; Liu, Xiaoqiang; Wu, Xiaoyong; Ma, Han; Li, Wenyuan; Xia, Haifeng; Jin, Shichao; Liang, Shunlin; Liu, Liangyun; Liu, Lingli; Su, Yanjun; Chen, Jin; Wu, Jin

doi:10.1016/j.rse.2026.115300

Title: Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Journal Article · Thu Feb 12 00:00:00 UTC 2026 · Remote Sensing of Environment

DOI: https://doi.org/10.1016/j.rse.2026.115300 · OSTI ID:3020931

Liu, Zhonghua ^[1]; Feng, Yuhao ^[1]; Cao, Xin ^[2]; Peñuelas, Josep ^[3]; Descals, Adrià ^[3];

^[4]; Cheung, Ka Kin ^[1]; Liu, Shuwen ^[1]; Lin, Ziyu ^[1]; Liu, Xiaoqiang ^[5]; Wu, Xiaoyong ^[5]; Ma, Han ^[1]; Li, Wenyuan ^[1]; Xia, Haifeng ^[6]; Jin, Shichao ^[7]; Liang, Shunlin ^[1]; Liu, Liangyun ^[8]; Liu, Lingli ^[5]; Su, Yanjun ^[5]; Chen, Jin ^[2] more »

University of Hong Kong, Pokfulam (Hong Kong)
Beijing Normal University, Beijing (China); Moganshan Geospatial Information Laboratory, Deqing (China)
Global Ecology Unit (CREAF-CSIC-UAB), Catalonia (Spain)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Chinese Academy of Sciences (CAS), Beijing (China); University of Chinese Academy of Sciences, Beijing (China)
Southeast University, Nanjing (China)
Nanjing Agricultural University (China)
Chinese Academy of Sciences (CAS), Beijing (China)
University of Hong Kong, Pokfulam (Hong Kong); State Key Laboratory of Agrobiotechnology (CUHK), Shatin (Hong Kong)

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km² each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R² = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R² = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 3020931

Journal Information:: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 335; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Arid and semi-arid landscapes
Machine learning
Mongolian steppe
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