Na et al. (2026) Generation of angular-normalized, cloud-filled, 0.01°-downscaled land surface temperature from 2018 to 2023 based on official FY-4A dataset

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-08-26
• Authors: Qiang Na, Biao Cao, Boxiong Qin, Tian Hu, Hua Li, Lixin Dong, Huanyu Zhang, Falu Hong, Qinhuo Liu
• DOI: 10.5194/essd-18-6139-2026

Research Groups

• National Engineering Research Center for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
• University of Chinese Academy of Sciences, Beijing 100049, China
• State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
• Department of Environment Research and Innovation, Luxembourg Institute of Science and Technology, 4362 Belvaux, Luxembourg
• Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
• State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
• University of Chinese Academy of Sciences, Beijing 100049, China
• Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, China

Short Summary

This study proposes a novel framework for generating angular-normalized, cloud-filled, and 0.01°-downscaled land surface temperature (LST) products from the official FY-4A dataset. The framework combines directional, nadir, and hemispherical LST layers to produce high-resolution all-weather LST products.

Objective

• To develop a unified processing framework for generating angular-normalized, cloud-filled, and 0.01°-downscaled land surface temperature (LST) products from the official FY-4A dataset.
• To evaluate the performance of the proposed framework using various validation methods, including cross-validation with VIIRS LST data and in situ validation with pyrgeometer measurements.

Study Configuration

• Spatial Scale: Global, with a focus on the Heihe River Basin and Australia.
• Temporal Scale: Hourly, from 2018 to 2023.

Methodology and Data

• Models used:
  • Time-evolving kernel-driven model (TEKDM)
  • CatBoost model optimized via Bayesian methods
  • Improved hybrid downscaling algorithm (IHDA) combining kernel- and fusion-based methods
• Data sources:
  • FY-4A LST dataset
  • MODIS MxD11A1 LST product
  • ERA5 reanalysis data
  • GLASS fractional vegetation cover product
  • GTOPO30 DEM data
  • ATC-simulated LST

Main Results

• The proposed framework generates angular-normalized, cloud-filled, and 0.01°-downscaled land surface temperature (LST) products with enhanced spatial continuity and finer resolution.
• The hemispherical LST product is validated using in situ pyrgeometer measurements at 8 sites in the Heihe River Basin and Australia, showing a root mean squared error (RMSE) of 2.48 K under clear-sky conditions and 3.65 K under cloudy-sky conditions.
• The nadir LST product is cross-validated using VIIRS near-nadir LST data, with an RMSE of 4.53 K.

Contributions

• This study contributes to the development of high-resolution all-weather LST products that can be used for various applications, including climate change research and environmental monitoring.
• The proposed framework provides a unified processing approach for generating angular-normalized, cloud-filled, and 0.01°-downscaled LST products.

Funding

• National Natural Science Foundation of China (Grant No. 42141005)
• National Key Research and Development Program of China (Grant No. 2021YFA1502302)

Citation

@article{Na2026Generation,
  author = {Na, Qiang and Cao, Biao and Qin, Boxiong and Hu, Tian and Li, Hua and Dong, Lixin and Zhang, Huanyu and Hong, Falu and Liu, Qinhuo},
  title = {Generation of angular-normalized, cloud-filled, 0.01°-downscaled land surface temperature from 2018 to 2023 based on official FY-4A dataset},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-6139-2026},
  url = {https://doi.org/10.5194/essd-18-6139-2026}
}