It is confirmed that there exists a scaling effect in downscaling land surface temperature (DLST) processes. However, a literature review indicates that quantifying and fully understanding scaling effect in DLST processing remains unclear. In this study, a main goal is to quantify, assess and understand scaling effect in downscaling LST product processes at different higher spatial resolutions and spatial extents. A machine leaning model and a traditional multivariate regression model were adopted with corresponding scaling factors extracted from ASTER 15–30 m optical multispectral data and Airborne Imaging Spectrometer for Different Applications (AISA) 2 m hyperspectral visible-near infrared data. MODIS 990 m LST and ASTER 90 m LST products were downscaled to high and very high resolution LST maps. In addition, ETM+ 60 m retrieved LST and Thermal Airborne Broadband Imager (TABI) 2 m retrieved LST and its upscaled LSTs were used to verify higher resolution DLST maps. The experimental results demonstrate that scaling effect in downscaling LST processes is significant, especially downscaling LST to high and very high resolution LST maps. One innovation point derived from findings by assessing the scaling effect in DLST processing is that when DLST processes are at spatial resolutions beyond a range (20–30 m in this study) measured from semivariograms, the processes are safe and their results are reasonable and reliable, and thus their scaling effect may be ignored, but when spatial resolutions and spatial extent lag distance within the range, the DLST processes are not safe, and their results are not reliable and thus the scaling effect has to be considered. Therefore, it is recommended that before conducting a DLST processing project, a range needs to be calculated by plotting semivariograms with high or very high resolution images (better to include three visible and one NIR bands), then DLST processes may be conducted at spatial resolutions lower than and equal to the range.

可以确定的是，在降低土地表面温度（DLST）的过程中存在缩放效应。但是，文献综述表明，量化和完全理解DLST处理中的缩放效果仍然不清楚。在这项研究中，主要目标是量化，评估和了解在不同的较高空间分辨率和空间范围内按比例缩小LST产品过程的按比例缩放效果。采用机器学习模型和传统的多元回归模型，并从ASTER 15-30 m光学多光谱数据和机载成像光谱仪用于不同应用（AISA）2 m高光谱可见-近红外数据中提取相应的比例因子。将MODIS 990 m LST和ASTER 90 m LST产品缩小为高分辨率和超高分辨率LST地图。此外，ETM + 60 m检索的LST和热机载宽带成像仪（TABI）2 m检索的LST及其放大的LST用于验证更高分辨率的DLST地图。实验结果表明，在缩小LST过程中，缩放效果非常显着，尤其是将LST缩小为高分辨率和非常高分辨率的LST图。通过评估DLST处理中的缩放效应，从发现中得出的一个创新点是，当DLST过程的空间分辨率超出半变异函数测量的范围（本研究中为20–30 m）时，这些过程是安全的，其结果是合理可靠的。 ，因此它们的缩放效果可能会被忽略，但是当空间分辨率和空间范围滞后距离在该范围内时，DLST过程是不安全的，其结果不可靠，因此必须考虑缩放效果。因此，建议在进行DLST处理项目之前，需要通过绘制具有高分辨率或超高分辨率图像（最好包括三个可见和一个NIR波段）的半变异函数来计算范围，然后以空间分辨率进行DLST处理小于并等于范围。