Bu et al. (2026) Physics-Informed Enhanced Machine Learning for Global Vegetation Optical Depth Retrieval Using Spaceborne GNSS-R

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Jinwei Bu, Huan Li, C. Ji, Xinyu Liu, Weimin Huang, Kegen Yu, Xiaoqing Zuo
• DOI: 10.1109/jstars.2026.3673386

Research Groups

N/A

Short Summary

This paper focuses on retrieving global Vegetation Optical Depth (VOD) by employing physics-informed enhanced machine learning techniques applied to data acquired from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R).

Objective

• To develop and apply a physics-informed enhanced machine learning framework for the global retrieval of Vegetation Optical Depth (VOD) using spaceborne GNSS-R observations.

Study Configuration

• Spatial Scale: Global
• Temporal Scale: N/A

Methodology and Data

• Models used: Physics-Informed Enhanced Machine Learning (specific algorithms not detailed in the title).
• Data sources: Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data.

Main Results

N/A

Contributions

N/A

Funding

N/A

Citation

@article{Bu2026PhysicsInformed,
  author = {Bu, Jinwei and Li, Huan and Ji, C. and Liu, Xinyu and Huang, Weimin and Yu, Kegen and Zuo, Xiaoqing},
  title = {Physics-Informed Enhanced Machine Learning for Global Vegetation Optical Depth Retrieval Using Spaceborne GNSS-R},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3673386},
  url = {https://doi.org/10.1109/jstars.2026.3673386}
}