Yang et al. (2025) Retrieval of land surface temperature in mountainous areas considering terrain shadows

Identification

• Journal: Advances in Space Research
• Year: 2025
• Date: 2025-10-09
• Authors: Yi Yang, Lei Zhang, Chang Lü, Hong Cai
• DOI: 10.1016/j.asr.2025.09.100

Research Groups

• College of Mining, Guizhou University, Guiyang 550025, China
• Institute of Surveying and Mapping Guizhou Geology and Mineral Exploration Bureau, Guiyang 550018, China

Short Summary

This study proposes a fast and efficient mountain shadow correction method based on the Normalized Difference Mountain Vegetation Index (NDMVI) to improve land surface temperature (LST) retrieval accuracy in mountainous areas. The method significantly enhances computational efficiency and reduces LST errors, particularly in valleys where deviations can reach 1.17 K due to neglected shadow effects.

Objective

• To develop a fast and efficient mountain shadow correction method for land surface temperature (LST) retrieval in mountainous areas, addressing the inaccuracies in land surface emissivity (LSE) estimation caused by inaccurate vegetation information in shadow regions.

Study Configuration

• Spatial Scale: Mountainous areas, suitable for large-scale applications.
• Temporal Scale: High-frequency LST retrievals.

Methodology and Data

• Models used: Normalized Difference Mountain Vegetation Index (NDMVI) for shadow correction; DART (Discrete Anisotropic Radiative Transfer) model for indirect validation.
• Data sources: Thermal infrared (TIR) remote sensing imagery.

Main Results

• Neglecting mountain shadows leads to significant LST deviations, especially in valley regions, with temperature errors reaching up to 1.17 K.
• The proposed mountain shadow correction method effectively improves LST retrieval accuracy and significantly enhances computational efficiency.
• After correction, R², Bias, and RMSE values in validation areas were improved compared to before correction, as validated by DART simulations.
• Ignoring shadow effects can result in an overestimation of surface temperatures, with substantial estimation bias in LSE caused by mountain shadows being the dominant factor in affected valleys.

Contributions

• Proposes a novel, fast, and efficient mountain shadow correction method based on NDMVI for LST retrieval in complex mountainous terrain.
• Offers a valuable complement to existing, more computationally intensive LST retrieval approaches, enabling large-scale and high-frequency applications.
• Highlights the critical importance of considering mountain shadows in LST retrieval, especially in valleys, to prevent significant LST overestimation.

Funding

No specific funding projects, programs, or reference codes were listed in the provided text.

Citation

@article{Yang2025Retrieval,
  author = {Yang, Yi and Zhang, Lei and Lü, Chang and Cai, Hong},
  title = {Retrieval of land surface temperature in mountainous areas considering terrain shadows},
  journal = {Advances in Space Research},
  year = {2025},
  doi = {10.1016/j.asr.2025.09.100},
  url = {https://doi.org/10.1016/j.asr.2025.09.100}
}