Liu et al. (2026) Enhanced land surface phenology detections in the western U.S. drylands by fusing harmonized Landsat and Sentinel-2 observations with near-surface PhenoCam time series

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-07-23
• Authors: Yuxia Liu, Xi Zhang, Yu Shen, Yongchang Ye, Shuai An
• DOI: 10.1016/j.rse.2026.115588

Research Groups

• Geospatial Sciences Center of Excellence, Department of Geography and Geospatial Sciences, South Dakota State University
• Nicholas School of the Environment, Duke University

Short Summary

This study improves land surface phenology detections in western U.S. drylands by fusing harmonized Landsat and Sentinel-2 observations with near-surface PhenoCam time series, generating gap-free temporal observations for 30-m phenology detections.

Objective

• Investigate the feasibility of using a fusion approach to enhance land surface phenology detections in dryland regions characterized by high heterogeneity and sparse canopy cover.

Study Configuration

• Spatial Scale: Western U.S. drylands, encompassing diverse ecosystems spanning grasslands, shrublands, and deserts.
• Temporal Scale: 2019-2023

Methodology and Data

• Models used:
  • Enhanced Hybrid Piecewise Logistic Model (E-HPLM)
  • Spatiotemporal Shape-Matching Model (SSMM)
• Data sources:
  • Harmonized Landsat 8 and Sentinel-2 (HLS) observations
  • Near-surface PhenoCam time series

Main Results

• A 30-m land surface phenology product was generated for the western U.S. drylands using the fusion approach, with an uncertainty of <12 days and R2 = 0.9 in greenup onset and maturity onset.
• The evaluation showed applicability to investigate landscape-scale phenological dynamics across the region.

Contributions

• This study paves a way to link near-surface camera networks and satellite observations for enhancing phenology detections, laying the groundwork for generating advanced continental and global phenology products.

Funding

• No specific funding projects or programs are mentioned in the paper.

Citation

@article{Liu2026Enhanced,
  author = {Liu, Yuxia and Zhang, Xi and Shen, Yu and Ye, Yongchang and An, Shuai},
  title = {Enhanced land surface phenology detections in the western U.S. drylands by fusing harmonized Landsat and Sentinel-2 observations with near-surface PhenoCam time series},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115588},
  url = {https://doi.org/10.1016/j.rse.2026.115588}
}