自然资源遥感2024，Vol.36Issue(3)：72-80,9.DOI:10.6046/zrzyyg.2023091
全天候逐时百米尺度地表温度重建方法
A method for reconstructing hourly 100-m-resolution all-weather land surface temperature
摘要
Abstract
Land surface temperature(LST)proves to be an important parameter in surface processes on regional and global scales,and its spatiotemporal information can be obtained through thermal infrared remote sensing.However,the constraints of thermal infrared sensors(TIRSs)themselves and the inability of thermal infrared electromagnetic waves to penetrate clouds render it impossible to obtain LST with a high spatiotemporal resolution currently.This study presents a method for reconstructing hourly LST at 100-m resolution in all weathers.This method consists of three main steps:① cloudy LST at four moments is reconstructed using a moderate resolution imaging spectroradiometer(MODIS)based on the conventional annual temperature cycle(ATC)model;② the daily variation curve of LST is estimated based on the daily trend in the skin temperature(SKT);③ with spectral indices as regressors,spatial downscaling is conducted for the hourly LST using Extreme Gradient Boosting(XGBoost).The results show that the proposed reconstruction method can obtain spatiotemporally continuous LST products,improve the spatial resolution of LST,and provide more details.The validation of the hourly 100-m-resolution LST using data from the surface radiation budget network(SURFRAD)developed by the U.S.indicates that the reconstructed hourly LST exhibits roughly the same trend as the measured values of the SURFRAD.The method for reconstructing all-weather hourly LST boasts high accuracy,with R2 of 0.95,a root mean squared error(RMSE)of 3.75 K,and a bias of 0.75 K.关键词
地表温度/MODIS/云下地表温度重建/降尺度/XGBoostKey words
LST/MODIS/cloudy LST reconstruction/downscaling/XGBoost分类
信息技术与安全科学引用本文复制引用
颜佳楠,陈虹,张雨泽,吴骅..全天候逐时百米尺度地表温度重建方法[J].自然资源遥感,2024,36(3):72-80,9.基金项目
中国科学院战略性先导科技专项"基于无人机的黑土地数据监测与感知系统"(编号:XDA28050200)和广西科技计划项目"基于高分遥感的公路地质灾害高风险区域监测预警技术研究"(编号:桂科AB20159034)共同资助. （编号:XDA28050200）
