Huo et al. (2026) Assessing the Contributions and Thresholds of Multi-Source Remote Sensing Informatiuon in Vegetation Aboveground Biomass Estimation Under Optical Saturation

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Identification

• Journal: Remote Sensing
• Year: 2026
• Date: 2026-09-10
• Authors: Shuhan Huo, Jiangbo Liu, Ruiqing Niu
• DOI: 10.3390/rs18183113

Research Groups

• Institute of Remote Sensing Science, Henan University
• Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences
• Dongzhai National Nature Reserve Management Office

Short Summary

This study investigates the contribution mechanisms and effective ranges of different information sources for aboveground biomass (AGB) estimation under optical saturation using multi-source remote sensing fusion. The results highlight the importance of structural information in improving AGB estimation accuracy.

Objective

• Investigate the contribution mechanisms and response thresholds of multi-source remote sensing information for AGB estimation under optical saturation

Study Configuration

• Spatial Scale: Local (Dongzhai National Nature Reserve, Henan Province, China)
• Temporal Scale: Long-term (field-measured data)

Methodology and Data

• Models used: Random Forest model
• Data sources:
  • GEDI L2A/L2B
  • Landsat 9
  • Sentinel-2
  • GF-2 hyperspectral
  • GF-5B hyperspectral
  • Sentinel-1
  • Topographic data
  • Climatic data

Main Results

• Structural information (St) is the core information source for improving AGB estimation accuracy under optical saturation.
• The predictive response of optical information (Sp) weakens with increasing AGB and eventually enters a stable saturation state.
• Multi-source remote sensing fusion can provide sustained and stable compensation for RMSE and MAE when structural information is present.

Contributions

This study provides new insights into the contribution mechanisms and effective ranges of different information sources for AGB estimation under optical saturation, highlighting the importance of structural information in improving model accuracy.

Funding

• National Natural Science Foundation of China (Grant No. 42171226)
• Key Research and Development Program of Henan Province (Grant No. 212102310124)

Citation

@article{Huo2026Assessing,
  author = {Huo, Shuhan and Liu, Jiangbo and Niu, Ruiqing},
  title = {Assessing the Contributions and Thresholds of Multi-Source Remote Sensing Informatiuon in Vegetation Aboveground Biomass Estimation Under Optical Saturation},
  journal = {Remote Sensing},
  year = {2026},
  doi = {10.3390/rs18183113},
  url = {https://doi.org/10.3390/rs18183113}
}