Work

Satellite-Ground Data Fusion for Hourly 5-km Gridded Human-Perceived Temperature Estimation in the Yangtze River Basin, China

Year: 2025

Type: article

Abstract: Human-perceived temperature (HPT) reflects the synergistic effects of multiple meteorological factors, and its extremes challenge human-managed and natural systems worldwide, especially in densely populated regions such as the Yangtze River Basin of China. However, detailed information on HPT at high temporal (e.g., hourly) and spatial resolution is severely lacking. In this study, we conduct a collaborative inversion for 12 HPT indices at a ~5 km spatial resolution and an hourly temporal resolution in the Yangtze River Basin from multi-source data (e.g., Himawari-8 images, meteorological stations, ERA5-Land reanalysis, and DEM data) using the LightGBM model. The model exhibited high predictive accuracy across all indices, achieving an average coefficient of determination (R2) of 0.981, root mean square error (RMSE) of 1.150 °C, and mean absolute error (MAE) of 0.860 °C. These results aligned well with observational data across spatial and temporal scales, effectively capturing the spatial heterogeneity and diurnal evolution of the region’s thermal environment. Our research provides a reliable data foundation for heat-health risk assessment and regional climate adaptation strategies.

Source: Remote Sensing

DOI: 10.3390/rs17183260

Authors: Huabing Ke, Zhongyuan Li, Zhaohua Liu, Zhaoliang Zeng

Institutions: Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, Jiangxi University of Science and Technology

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,258