Nieto et al. (2026) dms-bias-correction: Enhancing the dynamic LST range of sharpened LST scenes, by fusing them with Landsat LST imagery

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-04-01
• Authors: Hector Nieto, Radoslaw Guzinski, Magí Pàmies-Sans
• DOI: 10.5281/zenodo.19371038

Research Groups

• Hector Nieto
• Radoslaw Guzinski
• Magí Pàmies-Sans

Short Summary

This software, dms-bias-correction, enhances the dynamic range of sharpened Land Surface Temperature (LST) scenes by fusing them with Landsat LST imagery.

Objective

• To enhance the dynamic Land Surface Temperature (LST) range of sharpened LST scenes by fusing them with Landsat LST imagery.

Study Configuration

• Spatial Scale: Regional, processing satellite Land Surface Temperature (LST) imagery, including Landsat data.
• Temporal Scale: Applicable to various temporal periods of satellite LST data, as it is a processing tool.

Methodology and Data

• Models used: The dms-bias-correction software implements an algorithm for bias correction and fusion of LST scenes.
• Data sources: Sharpened Land Surface Temperature (LST) scenes and Landsat Land Surface Temperature (LST) imagery.

Main Results

• The dms-bias-correction software provides a method to enhance the dynamic range of sharpened LST scenes, improving their representativeness by integrating information from Landsat LST imagery.

Contributions

• Development and release of the dms-bias-correction software, offering a novel approach to improve the dynamic range and quality of sharpened Land Surface Temperature (LST) products through data fusion.

Funding

• Not specified in the provided text.

Citation

@article{Nieto2026dmsbiascorrection,
  author = {Nieto, Hector and Guzinski, Radoslaw and Pàmies-Sans, Magí},
  title = {dms-bias-correction: Enhancing the dynamic LST range of sharpened LST scenes, by fusing them with Landsat LST imagery},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19371038},
  url = {https://doi.org/10.5281/zenodo.19371038}
}