We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.

我们建议使用多重自适应回归样条（MARS）模型以及从高光谱获得的高分辨率辅助数据，对Landsat 8衍生的LST映射从100（30 m）分辨率降低到2-4 m进行空间缩减航空影像和大规模地形图。我们将该方法应用于四个Landsat 8场景，其中两个是夏季收集的，冬天是两个冬季收集的，用于三个代表各种城市形式的英国城镇。我们使用了几个光谱指数以及水和铺面的覆盖率作为LST预测指标，并应用了一种新颖的方法来校正从不同日期捕获的航空和卫星图像得出的光谱指数之间的时间不匹配，允许将缩减比例方法应用于多个日期，而无需重复进行航测。我们的结果表明，该方法在夏季日期表现良好，在校正残差之前，RMSE为1.40-1.83 K，而在残差校正后为0.76-1.21K。我们得出结论，通过解决粗略和精细空间分辨率下LST的非线性关系，MARS模型可以成功地应用于生成适用于局部尺度下城市热环境研究的高分辨率LST图。