四川环境2026，Vol.45Issue(4)：31-40,10.DOI:10.14034/j.cnki.schj.2026.04.005
基于机器学习的多源卫星遥感成都地表温度降尺度研究
Research on Downscaling of Land Surface Temperature in Chengdu Using Multi-Satellite Remote Sensing Based on Machine Learning
李晓敏 1潘媞 2冯晓 3王晨曦3
作者信息
- 1. 四川省气象数据中心,四川成都 610072
- 2. 四川省气象服务中心,四川成都 610072
- 3. 四川省气候中心,四川成都 610072
- 折叠
摘要
Abstract
Land surface temperature(LST)is one of the most critical environmental parameters and effectively reflects land surface processes.To better support research on the urban thermal environment of Chengdu and related studies,high spatial resolution(30 m)from Landsat-8/9 data was integrated with high temporal resolution(daily)data from MODIS,and the random forest machine learning downscaling method was adopted in this study to construct a land surface temperature(LST)downscaling model for Chengdu.An LST dataset with both high spatial and high temporal resolution was generated and compared with the MODIS LST product.The results showed the following:First,the split-window algorithm was used to retrieve LST from Landsat-8/9 imagery.This step yielded LST data with low temporal but high spatial resolution,and the correlation with the MODIS LST product reached an R2 of 0.73,laying a reliable data foundation for the subsequent downscaling study.Second,a dual-factor machine learning-based LST downscaling model was constructed,producing daily LST data at 100 m resolution over the study area.The mean absolute error(MAE)and root mean square error(RMSE)were 0.932 and 1.197,respectively,indicating satisfactory overall performance.Compared with conventional LST data from a single satellite,which typically offer either 1 000 m spatial resolution or 16-day temporal resolution,the downscaled results accurately capture the spatial distribution characteristics of daily LST,exhibit markedly improved textural detail relative to the MODIS LST product,and are able to mitigate cloud interference to a certain extent.This provides support for the dynamic,fine-scale monitoring and assessment of the urban heat effect in Chengdu.关键词
多源卫星遥感/地表温度降尺度/机器学习/随机森林Key words
Multi-satellite remote sensing/land surface temperature downscaling/machine learning/random forest分类
资源环境引用本文复制引用
李晓敏,潘媞,冯晓,王晨曦..基于机器学习的多源卫星遥感成都地表温度降尺度研究[J].四川环境,2026,45(4):31-40,10.
