García-Espriu et al. (2026) Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Aina García-Espriu, Cristina González‐Haro, Verónica González Gambau, Arnau Ruiz-Sebastian, Estrella Olmedo, Antonio Turiel
• DOI: 10.1109/jstars.2026.3652583

Research Groups

[Not provided in the given text.]

Short Summary

The paper focuses on developing improved projection algorithms to preserve the native spatial resolution of long-term satellite datasets.

Objective

• To develop and evaluate improved projection algorithms that maintain the native spatial resolution of long-term satellite datasets.

Study Configuration

• Spatial Scale: Relates to the native spatial resolution of satellite data.
• Temporal Scale: Long-term, as indicated by "Long-Term Satellite Datasets."

Methodology and Data

• Models used: Improved projection algorithms.
• Data sources: Long-term satellite datasets.

Main Results

[Not provided in the given text.]

Contributions

• Advances in projection algorithm development specifically designed to preserve the native spatial resolution of long-term satellite data.

Funding

[Not provided in the given text.]

Citation

@article{GarcíaEspriu2026Preserving,
  author = {García-Espriu, Aina and González‐Haro, Cristina and Gambau, Verónica González and Ruiz-Sebastian, Arnau and Olmedo, Estrella and Turiel, Antonio},
  title = {Preserving Native Spatial Resolution in Long-Term Satellite Datasets Through Improved Projection Algorithms},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3652583},
  url = {https://doi.org/10.1109/jstars.2026.3652583}
}