Lu et al. (2026) Toward fine-scale microwave monitoring of dryland vegetation: an alternative high spatial resolution vegetation optical depth retrieval algorithm from Sentinel-1 over the Sahel region

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-09-01
• Authors: Zhixuan Lu, Lei Fan, Rasmus Fensholt, Jiangyuan Zeng, Nicolas N. Baghdadi, Frédéric Frappart, Martin Brandt, Philippe Ciais, Gabrielle Jacinthe Maria de Lannoy, Jian Peng, Abdoul Aziz Diouf, É. Mougin, Xiaojing Bai, Xiangzhuo Liu, Jean‐Pierre Wigneron
• DOI: 10.1016/j.rse.2026.115675

Research Groups

• Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, China
• Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark
• State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, China
• National Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, France
• ISPA, UMR 1391, INRAE Nouvelle-Aquitaine, Universit´e de Bordeaux, France
• Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Universit´e Paris-Saclay, France
• Department of Earth and Environmental Sciences, KU Leuven, Belgium
• Remote Sensing Centre for Earth System Research, Leipzig University, Germany
• Centre de Suivi Ecologique, Senegal
• GET, Universit´e de Toulouse, CNRS, IRD, CNES, France
• School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, China

Short Summary

This study develops a 1 km vegetation optical depth (VOD) product over the Sahel region from Sentinel-1 observations for biomass estimation and monitoring of vegetation dynamics. The VOD retrievals show high spatial and temporal correlation with vegetation indices.

Objective

• Investigate the feasibility of using Sentinel-1 data to retrieve high-resolution VOD products in semi-arid regions like the Sahel.
• Evaluate the performance of a 2-parameter retrieval algorithm for VOD estimation from active microwave observations.

Study Configuration

• Spatial Scale: Regional scale (Sahel region) with a spatial resolution of 1 km.
• Temporal Scale: Long-term monitoring (2019-2023) with frequent temporal sampling.

Methodology and Data

• Models used: Vegetation backscatter model based on the semi-empirical Water Cloud Model coupled with the Oh model for soil backscatter.
• Data sources: Sentinel-1 active microwave observations, in-situ above-ground biomass data, and vegetation indices (NDVI, EVI, NDWI, LAI).

Main Results

• The 1 km VOD product showed high spatial and temporal correlation with vegetation indices (R = 0.74 for NDVI in space and R = 0.85 for EVI in time).
• Compared to typical vegetation indices, the 1 km VOD dataset demonstrated improved robustness for site-scale biomass monitoring.
• The 1 km VOD product had a higher correlation with in-situ biomass in space (R = 0.70).

Contributions

• This study provides an alternative high spatial resolution VOD retrieval algorithm from Sentinel-1 over the Sahel region, which can be used for long-term vegetation dynamics monitoring and biomass estimation.
• The proposed method demonstrates improved robustness compared to traditional vegetation indices.

Funding

• This research was funded by the National Natural Science Foundation of China (Grant No. 41971353).
• The University of Copenhagen provided additional funding through a research grant (Grant No. 101-2020).

Citation

@article{Lu2026Toward,
  author = {Lu, Zhixuan and Fan, Lei and Fensholt, Rasmus and Zeng, Jiangyuan and Baghdadi, Nicolas and Frappart, Frédéric and Brandt, Martin and Ciais, Philippe and Lannoy, Gabrielle Jacinthe Maria de and Peng, Jian and Diouf, Abdoul Aziz and Mougin, É. and Bai, Xiaojing and Liu, Xiangzhuo and Wigneron, Jean‐Pierre},
  title = {Toward fine-scale microwave monitoring of dryland vegetation: an alternative high spatial resolution vegetation optical depth retrieval algorithm from Sentinel-1 over the Sahel region},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115675},
  url = {https://doi.org/10.1016/j.rse.2026.115675}
}