地表温度是区域和全球尺度地表过程的重要参数,通过热红外遥感可获取区域或全球尺度的地表温度的时空信息.然而,受到热红外传感器硬件特性以及热红外电磁波无法穿透云层的限制,目前无法获取兼顾高时空分辨率的地表温度.该研究提出了一种重建全天候100 m空间分辨率的逐小时地表温度的方法.方法主要包含3个步骤:①在传统温度年循环模型的基础上,重建中分辨率成像光谱仪(moderate resolution imaging spectroradiome-ter,MODIS)4时刻的云下地表温度;②借助于温度的日变化趋势估计地表温度的日变化曲线,获取逐小时的地表温度;③以光谱指数作为回归因子,利用极端梯度提升树对逐小时地表温度进行空间降尺度.研究结果表明,提出的重构方法可以获取时空连续的地表温度产品,提高了地表温度的空间分辨率,提供了更丰富的纹理信息.通过美国地表辐射观测网络(surface radiation budget network,SURFRAD)站点数据对逐时100 m尺度的地表温度进行验证,结果表明逐小时重建的地表温度与站点实测值的变化趋势大致相同,全天候逐小时地表温度重建方法精度较高,R2 为 0.95,均方根误差(root mean squared error,RMSE)为 3.75 K,偏差(bias)为 0.75 K.
A method for reconstructing hourly 100-m-resolution all-weather land surface temperature
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.
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