Cao et al. (2025) A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-12-18
• Authors: Liqin Cao, Hang Zhao, Du Wang, Yanfei Zhong, Fawang Ye
• DOI: 10.1016/j.rse.2025.115190

Research Groups

• School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China
• State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China
• National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing, 100029, China
• Beijing Research Institute of Uranium Geology, Beijing, 100029, China

Short Summary

This paper introduces a novel wide-band atmospheric correction Temperature and Emissivity Separation (TES) algorithm for SDGSAT-1 Thermal Infrared Spectrometer (TIS) data, which retrieves Land Surface Temperature (LST) without requiring auxiliary atmospheric or land surface parameter input. The method demonstrates high accuracy and robustness across various global ground stations, outperforming existing algorithms.

Objective

• To develop a novel multiple-band Land Surface Temperature (LST) retrieval method for SDGSAT-1 TIS data that does not require auxiliary atmospheric and land surface parameter input, thereby improving LST retrieval accuracy and robustness.

Study Configuration

• Spatial Scale: Global (validated across various ground stations worldwide, including specific regions like Wuhan, Heihe, SURFRAD, ICOS, TERN, and BSRN).
• Temporal Scale: Not explicitly defined for a specific study period; the method is developed for continuous application with SDGSAT-1 TIS data.

Methodology and Data

• Models used:
  • Proposed: Wide-band atmospheric correction TES algorithm incorporating a transmittance ratio refinement module.
  • Comparative: Split-window (SW), Temperature-and-Emissivity Separation (TES), General Split-Window (GSW) algorithm, MODTRAN-TES.
• Data sources:
  • Satellite: Thermal Infrared Spectrometer (TIS) onboard Sustainable Development Science Satellite-1 (SDGSAT-1).
  • Validation: Simulated datasets, MODIS temperature products (for Wuhan region), and ground validation data from 108 points at Heihe, SURFRAD, ICOS, TERN, and BSRN stations.

Main Results

• The proposed method achieved a Root Mean Square Error (RMSE) of 1.32 K on simulated datasets, maintaining stability at 1.39 K with estimated transmittance, indicating strong robustness to water vapor content variations.
• Cross-validation in the Wuhan region against MODIS temperature products showed a bias of -1.79 K and an RMSE of 2.28 K.
• Ground validation across 108 points at Heihe, SURFRAD, ICOS, TERN, and BSRN stations yielded an overall RMSE of 1.95 K.
• This overall RMSE represents improvements of 0.25 K over the GSW algorithm and 0.55 K over MODTRAN-TES.
• Specific RMSEs for individual ground stations were: Heihe (2.07 K), SURFRAD (1.55 K), ICOS (1.84 K), TERN (1.72 K), and BSRN (2.14 K).

Contributions

• Development of a novel multiple-band LST retrieval method for SDGSAT-1 that eliminates the need for auxiliary atmospheric and land surface parameter inputs.
• Introduction of a transmittance ratio refinement module that iteratively refines atmospheric transmittance.
• Demonstrated high robustness of the method across varying water vapor content and diverse ground stations globally.
• Achieved superior accuracy in LST retrieval compared to existing GSW and MODTRAN-TES algorithms.

Funding

• No funding information was provided in the article text.

Citation

@article{Cao2025novel,
  author = {Cao, Liqin and Zhao, Hang and Wang, Du and Zhong, Yanfei and Ye, Fawang},
  title = {A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115190},
  url = {https://doi.org/10.1016/j.rse.2025.115190}
}