He et al. (2025) A novel index for directly indicating fractional vegetation cover based on spectral differences between vegetation and soil

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-10-03
• Authors: Bangke He, Wenquan Zhu, Cenliang Zhao, Zhiying Xie, Huimin Zhuang
• DOI: 10.1016/j.rse.2025.115056

Research Groups

• State Key Laboratory of Remote Sensing and Digital Earth, Beijing Normal University, Beijing, China
• Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• Experimental Teaching Platform, Beijing Normal University at Zhuhai, Zhuhai, China
• Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, China
• School of National Safety and Emergency Management, Beijing Normal University, Beijing, China

Short Summary

This study introduces the Vegetation Coverage Index (VCI), a novel remote sensing index designed for directly estimating fractional vegetation cover (FVC) by leveraging spectral differences between vegetation and soil. VCI demonstrates comparable or superior accuracy to existing methods, particularly in the presence of non-photosynthetic vegetation, and exhibits broad applicability across various satellite sensors.

Objective

• To develop a novel remote sensing index (Vegetation Coverage Index, VCI) for directly indicating fractional vegetation cover (FVC) that achieves high accuracy, broad applicability, and ease of use, addressing limitations of existing FVC estimation approaches.

Study Configuration

• Spatial Scale: Regional and global scales; 15 in-situ test sites and 40 in-situ comparative sites in China; spatial resolutions of 30 meters, 300 meters, and 1000 meters.
• Temporal Scale: Four phenological phases (green-up, peak, dormancy); potential for FVC monitoring across various temporal scales.

Methodology and Data

• Models used: Vegetation Coverage Index (VCI), Linear spectral mixing model, LESS (radiative transfer model), Dimidiate pixel model (DPM).
• Data sources: Simulated datasets (generated by LESS model), UAV-derived reference FVC, Sentinel-2 surface reflectance data, Landsat-8/9, Sentinel-3, MODIS (MODOCGA), existing FVC products (MultiVI FVC, GEOV3 FVC, GLASS FVC).

Main Results

• VCI utilizes spectral reflectance from blue, green, red, and near-infrared bands (400–1000 nm) to quantify vegetation and soil signals.
• In simulations, VCI performed comparably or slightly better than the dimidiate pixel model (DPM), reducing the root mean square error (RMSE) by 0.21 % to 14.42 %.
• At 15 in-situ test sites:
  • During the green-up to peak phase, VCI (RMSE = 0.13) and DPM (RMSE = 0.12) showed similar average accuracy.
  • During the peak to dormancy phase (with non-photosynthetic vegetation), VCI (RMSE = 0.11) significantly outperformed DPM (RMSE = 0.21), achieving a 46.8 % reduction in RMSE.
• At 40 in-situ comparative sites, VCI yielded RMSE comparable to the MultiVI FVC product and outperformed the GEOV3 FVC and GLASS FVC products, with RMSE reductions of 20.00 % and 30.77 %, respectively.

Contributions

• Introduction of a novel Vegetation Coverage Index (VCI) that directly indicates FVC based on unique spectral shapes of green vegetation and soil.
• Provides a simple and efficient approach for FVC estimation through basic spectral band calculations.
• Demonstrates broad applicability across widely used remote sensing sensors, including Sentinel-2, Sentinel-3, Landsat-8/9, and MODIS.
• Achieves improved accuracy, particularly in phenological phases with non-photosynthetic vegetation, addressing a limitation of existing methods.
• Shows strong potential for FVC monitoring across various spatial and temporal scales.

Funding

No specific funding projects or reference codes were mentioned in the provided text.

Citation

@article{He2025novel,
  author = {He, Bangke and Zhu, Wenquan and Zhao, Cenliang and Xie, Zhiying and Zhuang, Huimin},
  title = {A novel index for directly indicating fractional vegetation cover based on spectral differences between vegetation and soil},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115056},
  url = {https://doi.org/10.1016/j.rse.2025.115056}
}