Global comparison of diverse scaling factors and regression models for downscaling Landsat-8 thermal data - Dong, Pan; Gao, Lun; Zhan, Wenfeng; Liu, Zihan; Li, Jiufeng; Lai, Jiameng; Li, Hua; Huang, Fan; Tamang, Sagar K.; Zhao, Limin (ISPRS journal of photogrammetry and remote sensing, 2020)
•A total of 35 SDLST algorithms were compared in 32 diverse areas worldwide.•The performance of the scaling factors varies with the employed regression model.•The RF-based algorithms have the highest accuracy for downscaling LST.•A novel globally applicable algorithm is proposed for downscaling Landsat-8 LST. Statistical downscaling of land surface temperature (SDLST) algorithms with diverse scaling factors and regression models have been used to produce high spatial resolution LSTs based on Landsat-8 LST. However, the optimal choice of scaling factors and regression models and their associated combinations over various land surfaces, especially from a global perspective, remain unclear and even controversial. To cope with this issue, we compare 35 SDLST algorithms derived from a combination of seven scaling factors and five frequently used regression models over 32 geographical regions worldwide. The seven scaling factors, at varying degrees, make use of the LST-related information embedded within the visible and near-infrared and short-wave infrared bands of Landsat-8 data. The five regression models involved are multiple linear regression, partial least squares regression, artificial neural networks, support vector regression, and random forest (RF). Our main findings are: (1) The performance of the scaling factors and regression models are highly dependent on each other. Nevertheless, for most scaling factors, especially for high-dimension scaling factors with numerous LST-related variables, the downscaling algorithms that use RF as the regression model achieve the highest accuracy. (2) RFT21, a newly proposed SDLST algorithm based on the comparison results and further optimization, has high global operability and sufficiently high accuracy. RFT21 requires only Landsat-8 data as the inputs, and achieves the highest accuracy in comparison with the thermal sharpening (TsHARP) and high-resolution urban thermal sharpener (HUTS) algorithms, with the mean root-mean-square error (RMSE) reduced by more than 15%. These findings will facilitate the generation of high spatial resolution LSTs worldwide and associated applications.